Introduction: Hypokalemia is associated with an increased risk of chronic kidney disease (CKD) and is a risk factor for mortality. Albuminuria is an early manifestation of CKD. We investigated the association between hypokalemia and the prevalence of albuminuria in a Japanese general population. Methods: We analyzed the data of 18,289 subjects who underwent annual health checkups in 2018. We categorized them into four groups according to their concentration of serum potassium (sK) and performed a multivariate logistic regression analysis to determine the association between hypokalemia and the prevalence of albuminuria in this population. Hypokalemia was defined as having an sK = 3.1–3.5 mEq/L. After dividing the subjects into those with/without renal dysfunction, those with/without hypertension, and those with/without hyperglycemia, we examined the association between hypokalemia and albuminuria in each group. Results: Compared to the subjects with sK = 4.1–4.5 mEq/L, the subjects with hypokalemia had a significantly high prevalence of albuminuria: multivariable-adjusted odds ratio (OR) = 2.70 (95% confidence interval [CI] 1.84–3.96). The subgroup analyses showed the following multivariable-adjusted ORs (95% CIs) of the subjects: without renal dysfunction, 3.08 (2.00–4.73); with renal dysfunction, 2.05 (0.89–4.69); without hypertension, 2.89 (1.36–6.16); with hypertension, 2.60 (1.67–4.04); without hyperglycemia, 2.49 (1.62–3.84); and with hyperglycemia, 3.55 (1.43–8.79). Conclusions: Hypokalemia was significantly associated with the high prevalence of albuminuria in general population. Regardless of the presence/absence of renal dysfunction, hypertension, or hyperglycemia, hypokalemia was positively associated with the prevalence of albuminuria, and the associations were significant except for the subjects with renal dysfunction.

Potassium plays a key role in maintaining cell function, and the human body’s serum potassium (sK) is tightly regulated [1]. Hyperkalemia is a life-threatening abnormality, but hypokalemia is also a risk factor for high mortality. Both hyper- and hypokalemia can lead to cardiac arrhythmias and are associated with increased mortality [2]. A U-shaped association between sK and mortality is known: all-cause mortality risk was lowest between 4.0 and 5.0 mEq/L of sK [3, 4]. Hypokalemia is associated with an increased risk of chronic kidney disease (CKD) [5]. Animal experiments have shown that hypokalemic nephropathy is associated with alteration in intrarenal vasoactive substances, leading to vasoconstriction, salt sensitivity, and the progression of interstitial fibrosis [6, 7].

As hypokalemia is less common in the general population compared to hospital patients [8], the participants of the previous studies of hypokalemia were mostly individuals with a disorder such as CKD, diabetes, or endocrine diseases [9‒11].

Albuminuria is a marker of endothelial dysfunction that contributes to atherosclerotic cardiovascular disease [12, 13] and it is an early manifestation of CKD [14]. To examine the potential effects of hypokalemia on the kidney, we conducted the present study to investigate the association between hypokalemia and the prevalence of albuminuria in a general population.

Study Population

All of the subjects had undergone annual health checkups at the Health Management Center of Toranomon Hospital in Tokyo, Japan. The number of healthy subjects who had undergone annual health checkups in 2018 was 18,389. Almost all of the subjects were Japanese adults. Seventy percent of the subjects were employees who are required to undergo an annual health checkup under the Labor Standard Act. The rest of the subjects examine voluntarily for their health management.

To select the subjects with normokalemia (sK: 3.5–5.0 mEq/L) and mild hypokalemia (sK – 3.0–3.4 mEq/L) [15], 7 subjects with sK ≤3.0 mEq/L, and 78 subjects with sK ≥5.1 mEq/L were excluded from this study. Fifteen subjects whose data were defective were also excluded (Fig. 1). We performed a cross-sectional study using the data of the remaining 18,289 subjects. The study was approved by the Ethics Committee of Toranomon Hospital (approval no. 2106-K). The requirement for informed consent was waived because we used de-identified retrospective data.

Fig. 1.

Participants included and excluded from this study.

Fig. 1.

Participants included and excluded from this study.

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Clinical and Laboratory Measurements

Each subject’s demographic information was obtained through a self-administered questionnaire. Blood and urine samples were collected during hospital visits after an overnight fast and examined as comprehensive tests. The urinary albumin-to-creatinine ratio (U-ACR) was calculated as the ratio of urinary albumin to urinary creatinine. The estimated glomerular filtration rate (eGFR) was calculated using a version of the Modification of Diet in Renal Disease (MDRD) equation modified for Japanese: eGFR (mL/min/1.73 m2) = 194 × age−0.287 × serum creatinine [mg/dL, the enzymatic method]−1.094 × (0.739 if woman) [16].

Data Categorization

For our analysis of the association between the subjects’ sK concentrations and the prevalence of albuminuria, we classified the subjects into four groups according to their concentration of sK (mEq/L): (i) sK = 3.1–3.5 (n = 258), (ii) sK = 3.6–4.0 (n = 7,981), (iii) sK = 4.1–4.5 (n = 9,131), and (iv) sK = 4.6–5.0 (n = 919). Hypokalemia was defined as having an sK = 3.1–3.5 mEq/L. Albuminuria was defined as having a U-ACR >30 mg/g Cr. Micro-albuminuria and overt-albuminuria were defined by 30 mg/g Cr. ≤ U-ACR <300 mg/g Cr. and 300 mg/g Cr. ≤ U-ACR, respectively.

Hypertension, dyslipidemia, hyperglycemia, hyperuricemia, and obesity were defined as follows. The subjects with systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, and/or being on medication for hypertension were categorized as having hypertension. Dyslipidemia was defined as a serum concentration of low-density lipoprotein cholesterol ≥140 mg/dL, high-density lipoprotein cholesterol <40 mg/dL, triglyceride ≥150 mg/dL, and/or being on medication for dyslipidemia. Hyperglycemia was defined as a serum concentration of fasting blood glucose ≥126 mg/dL and/or being on medication for diabetes. Hyperuricemia was defined as a serum concentration of uric acid >7.0 mg/dL and/or being on medication for hyperuricemia. A body mass index (BMI) value ≥25 kg/m2 was classified as obesity following the criterion for the Asian population [17]. According to the K/DOQI-KDIGO guideline [18], an eGFR <60 mL/min/1.73 m2 was defined as renal dysfunction.

Statistical Analysis

To compare the baseline characteristics among category groups, we performed one-way analyses of variance for values and χ2 tests for proportions. By conducting multivariate logistic regression analyses, we calculated the odds ratios (ORs) and 95% confidence intervals (CIs) of the prevalence of albuminuria for each subject group. In these analyses, no covariate factors were adjusted in model 0. In model 1, age, sex, and obesity were adjusted as covariate factors. In model 2, for the adjustment of factors associated with albuminuria, we added hypertension, dyslipidemia, hyperglycemia, hyperuricemia, and renal dysfunction to the covariate factors of model 1. In model 3, BMI and eGFR as continuous variables were replaced with obesity and renal dysfunction as categorical variables in model 2. We used the subjects with sK = 4.1–4.5 mEq/L as a reference.

For subgroup analyses, the subjects were divided into pairs of groups: subjects with/without renal dysfunction, with/without hypertension, and with/without hyperglycemia. Multivariate logistic analyses identical to those used for model 2 were conducted. For examining the interactions between potassium and several factors, the interaction term (category of sK* the examining factor) was added as covariate in model 2. In model 4, category of sK*renal dysfunction was added to the model 2. The statistical analyses were performed using SPSS ver. 27 software (SPSS IBM, Chicago, IL, USA). A p value <0.05 was considered statistically significant.

Study Population

Our study population consisted of 18,389 subjects who underwent annual health checkups in 2018. After excluding 100 subjects, we analyzed the data of 18,289 subjects in a cross-sectional study (Fig. 1). Almost all of the subjects were Japanese; their baseline characteristics are summarized in Table 1. The characteristics of the four subject groups based on the concentrations of sK are shown in Table 2. All factors were significantly different, and the percentages of micro-albuminuria and overt-albuminuria in the hypokalemia group were the highest among the four groups.

Association between sK and Albuminuria

As illustrated in Figure 2, the percentages of albuminuria (the number of subjects with albuminuria/total) were 14.7% (38/258) in the sK = 3.1–3.5 mEq/L group, 6.0% (481/7,981) in the sK = 3.6–4.0 mEq/L group, 5.9% (542/9,131) in the sK = 4.1–4.5 mEq/L group, and 10.3% (95/919) in the sK = 4.6–5.0 mEq/L group. To determine the precise relationship between the sK and the prevalence of albuminuria, we analyzed the data of 18,289 subjects using a multivariate logistic regression analysis in which the category of sK = 4.1–4.5 was used as a reference (Table 3). In model 0, in which no covariate factors were adjusted, the crude ORs were significantly high in the subjects with sK = 3.1–3.5 mEq/L or sK = 4.6–5.0 mEq/L compared to the subjects with sK = 4.1–4.5 mEq/L. After adjusting for sex, age, and obesity as covariates (model 1), three groups showed significantly positive associations with albuminuria: the subjects with sK = 3.1–3.5, 3.6–4.0, or 4.6–5.0 mEq/L.

Fig. 2.

Prevalence of albuminuria. The percentages of albuminuria in each group based on serum concentration of potassium were shown.

Fig. 2.

Prevalence of albuminuria. The percentages of albuminuria in each group based on serum concentration of potassium were shown.

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In model 2, we added several covariate factors related to albuminuria: hypertension, dyslipidemia, hyperuricemia, hyperglycemia, and renal dysfunction. The results revealed that the significantly positive association remained in model 2. The ORs were 2.70 (95% CI: 1.84–3.96, p < 0.001) in the sK = 3.1–3.5 mEq/L group, 1.22 (95% CI: 1.06–1.39, p = 0.004) in the sK = 3.6–4.0 mEq/L group, and 1.39 (95% CI: 1.08–1.78, p = 0.01) in the sK = 4.6–5.0 mEq/L group. A multivariate logistic regression analysis in which BMI and eGFR were replaced as continuous variables instead of obesity and renal dysfunction as categorical variables (Model 3) presented similar result (online suppl. Table 1; for all online suppl. material, see www.karger.com/doi/10.1159/000529424).

Because of the low number of cases of hypokalemia, we performed an analysis in which the subjects were divided into another sK category (online suppl. Table 2). Result also showed a significantly higher prevalence of albuminuria in the lowest sK category (sK: 3.1–3.9 mEq/L).

Subgroup Analysis of the Association between Hypokalemia and Albuminuria

We performed a subgroup analysis to elucidate the association between sK and the prevalence of albuminuria. Figure 3 provides the ORs and 95% CIs of the subjects with hypokalemia (sK = 3.1–3.5 mEq/L) compared with the subjects with sK = 4.1–4.5 mEq/L. All groups showed a positive association between hypokalemia and the prevalence of albuminuria, and the associations were significant except for the subjects with renal dysfunction. The ORs (95% CI) of the subjects without and with renal dysfunction were 3.08 (2.00–4.73) and 2.05 (0.89–4.69), respectively (p for interaction = 0.006). As renal dysfunction was a significant interaction factor, category of sK*renal dysfunction was added as a covariate to model 2 (model 4). The OR of hypokalemia was significantly positive in the general population (online suppl. Table 3). Those of the subjects without and with hypertension were 2.89 (1.36–6.16) and 2.60 (1.67–4.04), respectively (p for interaction = 0.84). The ORs (95% CI) of the subjects without and with hyperglycemia were 2.49 (1.62–3.84) and 3.55 (1.43–8.79), respectively (p for interaction = 0.75).

Fig. 3.

Association between hypokalemia and the prevalence of albuminuria in subgroup subjects. After adjusting for age, sex, obesity, hypertension, dyslipidemia, hyperuricemia, hyperglycemia, and renal dysfunction (model 2), multivariate logistic regression analyses were performed in which the subjects with sK = 4.1–4.5 mEq/L were used as a reference. ORs of subjects with hypokalemia (sK = 3.1–3.5 mEq/L) were shown.

Fig. 3.

Association between hypokalemia and the prevalence of albuminuria in subgroup subjects. After adjusting for age, sex, obesity, hypertension, dyslipidemia, hyperuricemia, hyperglycemia, and renal dysfunction (model 2), multivariate logistic regression analyses were performed in which the subjects with sK = 4.1–4.5 mEq/L were used as a reference. ORs of subjects with hypokalemia (sK = 3.1–3.5 mEq/L) were shown.

Close modal

Albuminuria is an early manifestation of kidney damage and a risk factor for CKD and cardiovascular disease [19]. The prevalence of nephrotic range proteinuria has been reported to be high in CKD patients with sK values <3.5 mEq/L [20]. Our present cross-sectional study indicated that hypokalemia had a significantly positive association with early renal damage in a general population. Earlier investigations had revealed a U-shaped association between sK and mortality, with the lowest all-cause mortality in patients with sK concentration between 4.0 and 5.0 mEq/L [3, 4]. Similarly, our present subjects with sK = 4.1–4.5 mEq/L showed the lowest OR for albuminuria.

Potassium is the major intracellular cation and the determinant of the resting membrane potential [21]. The concentration of sK is normally regulated around the range of 3.5–5.0 mEq/L. Severe hypokalemia (sK <3 mEq/L) is well known as an urgent medical condition [8]. In our general population, only seven subjects showed severe hypokalemia; we excluded them and analyzed the subjects with mild hypokalemia (sK = 3.1–3.5 mEq/L). Despite this mildness, the OR of hypokalemia for the prevalence of albuminuria was considerably high in these subjects. Based on our present findings, we speculated that early kidney injury could commence even if an individual’s hypokalemia is mild.

Several experimental studies have showed that hypokalemia induces tubulointerstitial injury and develops hemodynamic and structural changes in the kidney [2, 6]. Though the injury which is a cause of albuminuria has not been obviously presented, some reports presented that vascular endothelial dysfunction could be involved in the renal tubulointerstitial injury [7, 22]. To clarify the mechanism of the relationship between hypokalemia and albuminuria, further examination is expected.

Our subgroup analyses of pairs of subject groups based on the presence/absence of conditions related to hypokalemia or albuminuria were also informative. The causes of hypokalemia were based on various conditions [8], and renal dysfunction, hypertension, and the use of diuretic drugs for hypertension are relatively frequent causes of an excessive excretion of potassium [23]. Albuminuria is one of the manifestations of diabetic nephropathy [24, 25]. Our present subgroup analyses thus concerned renal dysfunction, hypertension, and hyperglycemia, and the results demonstrated that hypokalemia had positive associations with the prevalence of albuminuria in the subjects with/without renal dysfunction, those with/without hypertension, and those with/without hyperglycemia; these associations were significant with the exception of the subjects with renal dysfunction. We speculated that the reason why the association of the subjects with renal dysfunction was not significant is that the number of subjects with hypokalemia and renal dysfunction was low.

There are several study limitations to consider. First, this was a cross-sectional retrospective investigation, and thus it was uncertain whether hyperkalemia was a cause of albuminuria. Second, several measurements of the urine concentration of albumin are recommended because of its fluctuation. However, our data were derived from annual health checkups, and it was not possible to observe the subjects repeatedly. We therefore analyzed the data of single measurements. Third, the association with albuminuria could differ depending on the etiology of hypokalemia; however, the cause of hypokalemia in each subject was not determined. Though medications such as drug inhibiting renin-angiotensin-aldosterone system, diuretic, and laxative are important causes of hypokalemia, detailed information such as drug names was not available. Therefore, they could not be added to the analyses as covariate factors. Finally, as subjects undergoing annual health checkups are more likely to be concerned about their health, the selection bias existed in our population.

In conclusion, we observed a significantly positive association between hypokalemia and the prevalence of albuminuria in a general population. Though the subjects with hypokalemia had higher eGFR, they presented higher prevalence of albuminuria which is an early manifestation of CKD. However, it is uncertain whether hypokalemia is a risk factor for albuminuria because our study was a cross-sectional investigation. Further studies, such as longitudinal studies, are needed to clarify whether an intervention is necessary for hypokalemia to prevent the development of albuminuria.

The authors are grateful to all of the participants, other physicians, medical staff, and other contributors to this study. We also thank Dr. Masanori Umeyama (Safety Research Institute for Chemical Compounds Co., Ltd) for advising about statistical analysis. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

This study was approved by the Ethics Committees at Toranomon Hospital, approval number 2106-K. The requirement for informed consent was waived because we used de-identified retrospective data.

The authors have no conflicts of interest to declare.

No funding sources were used for the conduct of this study.

Akiko Toda conceived and designed the work, analyzed the data, and wrote the manuscript. Shigeko Hara interpreted the result, contributed to the discussion, and reviewed the manuscript. Ritsuko Honda and Yasuji Arase contributed to the discussion and reviewed the manuscript. All authors gave important intellectual contributions, reviewed the manuscript, and provided final consent for the version to be published.

The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from the corresponding author (A.T.) upon reasonable request.

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