ABSTRACT
Primary aldosteronism (PA) independently increases renal impairment risk beyond blood pressure effects. Although hyperaldosteronism is known to mediate renal injury, associations between plasma aldosterone concentration (PAC) and early kidney damage biomarkers such as retinol‐binding protein (RBP) and β2‐microglobulin (β2‐MG) remain insufficiently explored. We investigated the association of PAC with renal function indicators—including RBP, β2‐MG, albumin‐to‐creatinine ratio (ACR), and estimated glomerular filtration rate (eGFR)—comparing matched patients with PA and essential hypertension (EH). In this cross‐sectional study, 546 PA patients and 546 propensity score‐matched EH patients were assessed. Spearman correlations and multivariate regression analyses assessed PAC‐renal marker associations, with interactions tested to determine differences between PA and EH groups. In PA, PAC strongly correlated with lower eGFR (r = −0.597, p < 0.001) and higher RBP (r = 0.559), β2‐MG (r = 0.632), and ACR (r = 0.583), persisting after adjustment. In contrast, EH patients showed only weak correlations between PAC and eGFR (r = −0.204, p < 0.001), without links with other markers. Interaction analysis confirmed stronger PAC‐biomarker associations in PA than EH (all p < 0.05). This study is the first to demonstrate robust associations between PAC and sensitive early renal damage biomarkers, especially RBP, in PA patients, distinct from matched EH patients. It highlights hyperaldosteronism's unique pathogenic role in renal impairment in PA, suggesting early biomarker monitoring and aldosterone‐targeted interventions could reduce chronic kidney disease risk in PA populations.
Keywords: aldosterone, essential hypertension, primary aldosteronism, renal damage
1. Introduction
Primary aldosteronism (PA) is a common endocrine disorder characterized by excessive, autonomous secretion of aldosterone. It is now recognized as a leading cause of secondary hypertension, with an estimated prevalence of up to 10% among individuals with hypertension [1, 2, 3]. Aldosterone, synthesized in the adrenal cortex, plays a pivotal role in maintaining fluid and electrolyte homeostasis by promoting sodium reabsorption and potassium excretion. In PA, chronic aldosterone excess not only contributes to resistant hypertension but also increases the risk of target organ damage, particularly renal impairment, compared to essential hypertension (EH) [4, 5, 6].
Mounting evidence indicates that the adverse effects of aldosterone on the kidneys extend beyond its impact on blood pressure. Through enhanced sodium retention and glomerular hyperfiltration, hyperaldosteronism induces progressive structural injury in the renal vasculature and interstitium, ultimately leading to chronic kidney disease (CKD) [4, 7]. The estimated glomerular filtration rate (eGFR), derived from serum creatinine (sCr), is the most commonly used clinical indicator for evaluating renal function and staging CKD [8]. However, eGFR has inherent limitations in detecting early renal impairment, as it is influenced by extrarenal factors such as muscle mass, age, and diet, and may remain within the normal range during early hyperfiltration stages [9, 10].
Alternative biomarkers have been proposed to enhance the detection of subclinical kidney damage. The urinary albumin‐to‐creatinine ratio (ACR) reflects microvascular injury and early glomerular leakage, and has been shown to correlate with aldosterone levels in PA [11, 12]. β2‐microglobulin (β2‐MG), a low‐molecular‐weight protein freely filtered by the glomerulus and reabsorbed by proximal tubules, increases in circulation when renal filtration is impaired, and has been suggested as a marker of early renal dysfunction [13]. Retinol‐binding protein (RBP), another low‐molecular‐weight protein primarily synthesized in the liver, is filtered by the glomerulus and reabsorbed in the proximal tubules [14, 15]. Emerging evidence indicates that elevated serum RBP levels demonstrate significant associations with obesity, insulin resistance (IR), and metabolic syndrome (MetS) [16, 17]. Furthermore, recent investigations have established that increased RBP levels have been associated with early renal tubular dysfunction, particularly in conditions involving tubular injury, and may offer added sensitivity in the detection of early renal impairment [18, 19].
Despite growing recognition of hyperaldosteronism's deleterious renal effects and the potential utility of novel biomarkers, there remains a critical gap in the comparative assessment of renal injury biomarkers between PA and EH populations. Specifically, the relationships between plasma aldosterone concentration (PAC) and early renal injury markers such as ACR, β2‐MG, and RBP have not been comprehensively characterized in a matched cohort of PA and EH patients. In clinical practice, this lack of clarity blocks the development of tailored monitoring strategies for patients with PA, particularly in the early stages of renal damage when eGFR remains within normal limits.
To address this knowledge gap, the present study aimed to investigate the associations between PAC and multiple renal injury biomarkers—eGFR, ACR, β2‐MG, and RBP—in a well‐matched cohort of patients with PA and EH. By evaluating the strength and consistency of these associations across subgroups, we sought to determine whether specific biomarkers, particularly RBP, may serve as sensitive indicators of early aldosterone‐related renal impairment, and to explore their potential utility in clinical monitoring and risk stratification.
2. Methods
2.1. Study Population
This study included 2457 patients who underwent screening for PA in the hypertension ward of the Department of Cardiovascular diseases at the Second Affiliated Hospital of Nanchang University between January 2020 and March 2023. In accordance with current PA diagnostic guidelines [20], participants were required to discontinue medications that could interfere with the PAC and plasma renin activity (PRA) ratio prior to testing. All enrolled patients met the standardized medication protocol: complete withdrawal of all antihypertensive medications and diuretics for 2–4 weeks prior to admission, or standardized transition to non‐dihydropyridine calcium channel blockers (CCBs) and/or alpha‐blockers for blood pressure management.
A diagnosis of PA was confirmed in patients with the aldosterone‐to‐renin ratio (ARR) > 30 (ng/dL)/(ng/mL/h) who tested positive on either the captopril challenge test or saline infusion test. A detailed description of the diagnostic protocol has been published previously by our group [21].
Essential hypertension (EH) was diagnosed according to the current Chinese hypertension guidelines [22], and all EH patients underwent biochemical evaluation to exclude secondary forms of hypertension.
Of the 2457 screened patients, 970 were excluded due to incomplete baseline data, 22 were younger than 18 years, and 135 had comorbidities that could affect renal function (e.g., chronic glomerulonephritis, acute urinary tract infection, kidney cancer, or renal artery stenosis). After applying these criteria, 716 patients with confirmed EH and 614 with confirmed PA were eligible for analysis. Propensity score matching (PSM) was performed at a 1:1 ratio based on sex, age, systolic blood pressure (SBP), diastolic blood pressure (DBP), and body mass index (BMI), using a caliper width of 0.01. This yielded a final matched cohort of 546 patients with EH and 546 with PA (Figure 1).
FIGURE 1.
Flow chart of screening participants.
The study protocol was approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University (Ethics Approval No. I‐2024‐083) and granted institutional approval (Project No. IIT‐I‐2024‐051), and registered with the Chinese Clinical Trial Registry (ChiCTR2200057297).
2.2. Clinical and Laboratory Data
Collected demographic and clinical data included sex, age, BMI, smoking and drinking status, diabetes mellitus, hypokalemia, CKD, SBP, DBP, and duration of hypertension (HTN). BMI was calculated as weight (kg) divided by height squared (m2). Blood pressure was measured in the seated position after admission with at least 10 min of rest using an automated electronic sphygmomanometer (Omron, Dalian, China), and the average of three readings was recorded.
CKD was defined according to current the Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group criteria as either an eGFR < 60 mL/min/1.73 m2 or albuminuria (ACR ≥ 30 mg/g) persisting for at least 3 months [23].
Laboratory parameters included homocysteine (Hcy), RBP, β2‐MG, eGFR, uric acid (UA), serum creatinine (sCr), ACR, blood urea nitrogen (BUN), fasting plasma glucose (FPG), and lipid profiles (total cholesterol [TC], triglycerides [TG], high‐density lipoprotein cholesterol [HDL‐c], and low‐density lipoprotein cholesterol [LDL‐c]). Above biochemical analyses were performed in the morning of the second day of admission when blood and urine samples were taken, according to standardized protocols in the central laboratory. In the morning of the second day of admission, after remaining in an upright position for 2 h, blood samples were collected to measure PAC and PRA. PAC and PRA were measured using a fully automated chemiluminescence immunoassay system (Maglumi 4000 Plus, Shenzhen New Industries Biomedical Engineering Co., Ltd.).
2.3. Statistical Analysis
Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range, IQR), depending on distribution, and compared using independent‐sample t‐tests or Mann–Whitney U tests. Categorical variables were summarized as counts and percentages and compared using chi‐square tests. Spearman correlation analysis was used to assess the relationships between PAC and biomarkers of renal injury. As PAC exhibited a skewed distribution, a natural logarithmic transformation (Ln PAC) was applied before further analysis. Participants were then stratified into quartiles based on Ln PAC levels. To explore dose‐response relationships, multivariable linear regression analyses were conducted, with renal biomarkers as dependent variables and PAC quartiles as the key independent variable. Three models were constructed: Model I (unadjusted), Model II (adjusted for age, sex, and BMI), and Model III (further adjusted for SBP, DBP, HTN, smoking and alcohol use, diabetes, Hcy, TC, TG, HDL‐c, LDL‐c, and hypokalemia). The β coefficients and corresponding 95% confidence intervals (CIs) were reported to estimate the magnitude of association between PAC and each renal biomarker. Trend tests across PAC quartiles were performed by modeling the median value of each quartile as a continuous variable. Interaction terms were included to assess whether the associations between PAC and renal biomarkers differed significantly by hypertension subtype (PA vs. EH).
All statistical analyses were two‐tailed, and a p value <0.05 was considered statistically significant. Analyses were performed using R (version 4.3.3; http://www.R‐project.org) and EmpowerStats (http://www.empowerstats.com).
3. Results
3.1. Baseline Characteristics and Renal Impairment Differences Between PA and EH Patients
A total of 1092 hypertensive patients (546 PA and 546 matched EH patients) were included in the analysis. As shown in Table 1, baseline characteristics such as age, sex, BMI, smoking, drinking status, diabetes prevalence, systolic and diastolic blood pressure were comparable between PA and EH groups (all p > 0.05). However, patients with PA exhibited significantly higher proportions of hypokalemia (30.59% vs. 10.26%, p < 0.001) and CKD (32.42% vs. 23.26%, p < 0.001) compared to EH patients. PA patients had significantly elevated levels of RBP (53.80 ± 22.72 mg/L vs. 45.71 ± 13.78 mg/L, p < 0.001), β2‐MG (median: 1.81 vs. 1.60 mg/L, p < 0.001), and ACR (median: 28.71 vs. 14.54 mg/g, p < 0.001), along with significantly reduced eGFR (94.18 ± 23.79 vs. 101.06 ± 23.05 mL/min/1.73 m2, p < 0.001).
TABLE 1.
Baseline characteristics of study participants.
| Characteristics | Total (n = 1092) | EH (n = 546) | PA (n = 546) | p value |
|---|---|---|---|---|
| Age (years) | 48.47 ± 10.58 | 48.16 ± 10.85 | 48.79 ± 10.31 | 0.328 |
| Gender (female, n %) | 453 (41.48) | 217 (39.74) | 236 (43.22) | 0.243 |
| BMI (kg/m2) | 25.92 ± 3.63 | 25.89 ± 3.78 | 25.94 ± 3.47 | 0.304 |
| Current smoker (%) | 243 (22.25) | 124 (22.71) | 119 (21.79) | 0.716 |
| Current drinker (%) | 196 (17.95) | 100 (18.32) | 96 (17.58) | 0.752 |
| Diabetes (%) | 228 (20.88) | 116 (21.25) | 112 (20.51) | 0.766 |
| Hypokalemia (%) | 223 (20.42) | 56 (10.26) | 167 (30.59) | <0.001 |
| CKD (%) | 304 (27.84) | 127 (23.26) | 177 (32.42) | <0.001 |
| HTN (months) | 48.00 (11.50–120.00) | 60.00 (12.00–120.00) | 36.00 (6.00–120.00) | 0.009 |
| SBP (mmHg) | 152.16 ± 19.64 | 151.81 ± 16.81 | 152.51 ± 22.12 | 0.921 |
| DBP (mmHg) | 92.96 ± 14.50 | 92.59 ± 12.95 | 93.34 ± 15.90 | 0.750 |
| Hcy (umol/L) | 11.74 (9.66–14.21) | 11.66 (9.78–13.95) | 11.82 (9.55–14.44) | 0.765 |
| RBP (mg/L) | 49.76 ± 19.21 | 45.71 ± 13.78 | 53.80 ± 22.72 | <0.001 |
| β2‐MG (mg/L) | 1.68 (1.40–2.10) | 1.60 (1.35–1.91) | 1.81 (1.48–2.29) | <0.001 |
| eGFR (mL/min/1.73 m2) | 97.62 ± 23.67 | 101.06 ± 23.05 | 94.18 ± 23.79 | <0.001 |
| UA (umol/L) | 373.73 ± 95.25 | 376.55 ± 97.77 | 370.91 ± 92.68 | 0.410 |
| sCr (umol/L) | 75.65 ± 22.28 | 73.94 ± 22.07 | 77.37 ± 22.38 | 0.034 |
| ACR (mg/g) | 22.44 (10.03–30.49) | 14.54 (7.71–23.84) | 28.71 (16.21–63.24) | <0.001 |
| BUN (mmol/L) | 5.04 (4.20–6.01) | 4.92 (4.21–5.79) | 5.17 (4.18–6.16) | 0.022 |
| FPG (mmol/L) | 5.16(4.70–5.82) | 5.24 (4.77–5.79) | 5.08 (4.59–5.82) | 0.003 |
| TC (mmol/L) | 4.90 ± 1.06 | 5.10 ± 1.04 | 4.70 ± 1.04 | <0.001 |
| TG (mmol/L) | 1.55 (1.11–2.24) | 1.55 (1.13–2.31) | 1.56 (1.09–2.18) | 0.372 |
| HDL‐c (mmol/L) | 1.11 (0.94–1.34) | 1.15 (0.97–1.39) | 1.08 (0.92–1.29) | <0.001 |
| LDL‐c (mmol/L) | 2.87 ± 0.85 | 3.05 ± 0.81 | 2.70 ± 0.85 | <0.001 |
| PAC (ng/dL) | 21.43 (15.63–28.35) | 17.66 (12.62–24.49) | 25.11 (19.38–33.25) | <0.001 |
| PRA (ng/mL/h) | 0.63 (0.12–2.11) | 2.05 (0.93–4.17) | 0.17 (0.02–0.48) | <0.001 |
Note: Data are mean ± SD, median [Q1‐Q3] for skewed variables or n (%) for categorical variables. The p value less than 0.05 was considered indicative of statistical significance.
Abbreviations: ACR, albumin‐creatinine ratio; BMI, body mass index; BUN, blood urea nitrogen; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; EH, essential hypertension; FPG, fasting plasma glucose; Hcy, homocysteine; HDL‐c, high density lipoprotein cholesterol; HTN, duration of hypertension; LDL‐c, low density lipoprotein cholesterol; PA, primary aldosteronism; PAC, plasma aldosterone concentration; PRA, plasma renin activity; RBP, retinol‐binding protein; SBP, systolic blood pressure; sCr, serum creatinine; TC, total cholesterol; TG, triglyceride; UA, uric acid; β2‐MG, β2‐microglobulin.
3.2. Univariate Associations of PAC With Renal Biomarkers in PA and EH Patients
Spearman correlation analysis indicated significant relationships between PAC and renal function biomarkers in the total hypertensive population (Table 2). Specifically, PAC negatively correlated with eGFR (r = −0.461, p < 0.001), and positively correlated with RBP (r = 0.463, p < 0.001), β2‐MG (r = 0.503, p < 0.001), and ACR (r = 0.546, p < 0.001). Stratified analysis revealed stronger correlations within the PA subgroup: eGFR (r = −0.597, p < 0.001), RBP (r = 0.559, p < 0.001), β2‐MG (r = 0.632, p < 0.001), and ACR (r = 0.583, p < 0.001). In contrast, EH patients demonstrated only a weak correlation between PAC and eGFR (r = −0.204, p < 0.001), without significant associations for RBP, β2‐MG, or ACR (all p > 0.05; Figure 2).
TABLE 2.
Spearman rank correlation between eGFR, RBP, β2‐MG, ACR, and aldosterone levels in hypertensive patients.
| eGFR | RBP | β2‐MG | ACR | |||||
|---|---|---|---|---|---|---|---|---|
| Research object | Correlation coefficient | p value | Correlation coefficient | p value | Correlation coefficient | p value | Correlation coefficient | p value |
| Total population | −0.461 | <0.001 | 0.463 | <0.001 | 0.503 | <0.001 | 0.546 | <0.001 |
| EH | −0.204 | <0.001 | 0.049 | 0.253 | 0.066 | 0.123 | 0.010 | 0.821 |
| PA | −0.597 | <0.001 | 0.559 | <0.001 | 0.632 | <0.001 | 0.583 | <0.001 |
Abbreviations: β2‐MG, β2‐microglobulin; ACR, albumin‐creatinine ratio; eGFR, estimated glomerular filtration rate; EH, essential hypertension; PA, primary aldosteronism; RBP, retinol‐binding protein.
FIGURE 2.
Relationship between the eGFR (A), RBP (B), β2‐MG (C), ACR (D), and aldosterone levels in PA patient
3.3. Relationship Between PAC Quartiles and Renal Impairment Markers
Multivariate regression analyses based on quartiles of log‐transformed plasma aldosterone concentration (Ln PAC) demonstrated significant dose‐response relationships between increasing aldosterone levels and markers of renal impairment in the overall hypertensive population (p for trend <0.001; Table 3). Specifically, when comparing the highest quartile (Q4) to the lowest quartile (Q1) of PAC, eGFR decreased by approximately 21% (β = –20.52 mL/min/1.73 m2), while RBP increased by 28% (β = 13.96 mg/L), β2‐MG increased by 0.43 mg/L, and ACR increased by 49.09 mg/g. These associations remained robust after adjustment for potential confounders including age, sex, BMI, blood pressure, HTN, metabolic parameters, and serum potassium.
TABLE 3.
Multivariate regression analysis of the eGFR, RBP, β2‐MG, ACR, and aldosterone levels in hypertensive patients.
| Dependent variable | Adjusted model I β (95% CI) | p value | Adjusted model II β (95% CI) | p value | Adjusted model III β (95% CI) | p value |
|---|---|---|---|---|---|---|
| eGFR | −0.77(−0.86, −0.68) | <0.001 | −0.73 (−0.82, −0.64) | <0.001 | −0.69 (−0.78, −0.60) | <0.001 |
| Subgroups | ||||||
| Q1 | Reference | Reference | Reference | |||
| Q2 | −1.58 (−5.27, 2.11) | 0.402 | −1.53 (−5.16, 2.09) | 0.408 | −1.95 (−5.45, 1.54) | 0.274 |
| Q3 | −6.68 (−10.47, −2.89) | <0.001 | −7.03 (−10.76, −3.31) | <0.001 | −6.65(−10.27, −3.03) | <0.001 |
| Q4 | −22.86 (−26.74, −18.99) | <0.001 | −21.83(−25.65, −18.01) | <0.001 | −20.52(−24.21, −16.83) | <0.001 |
| p for trend | <0.001 | <0.001 | <0.001 | |||
| RBP | 0.59 (0.52, 0.67) | <0.001 | 0.57 (0.50, 0.65) | <0.001 | 0.56 (0.48, 0.64) | <0.001 |
| Subgroups | ||||||
| Q1 | Reference | Reference | Reference | |||
| Q2 | 2.67 (−0.36, 5.71) | 0.085 | 2.65 (−0.34, 5.64) | 0.083 | 2.77 (−0.25, 5.78) | 0.072 |
| Q3 | 3.93 (0.81, 7.05) | 0.014 | 4.14 (1.07, 7.22) | 0.008 | 4.15 (1.03, 7.27) | 0.009 |
| Q4 | 15.46 (12.28, 18.65) | <0.001 | 14.85 (11.70, 17.99) | <0.001 | 13.96 (10.78, 17.14) | <0.001 |
| p for trend | <0.001 | <0.001 | <0.001 | |||
| β2‐MG | 0.02 (0.02, 0.02) | <0.001 | 0.02 (0.02, 0.02) | <0.001 | 0.02 (0.02, 0.02) | <0.001 |
| Subgroups | ||||||
| Q1 | Reference | Reference | Reference | |||
| Q2 | 0.07 (−0.03, 0.17) | 0.148 | 0.07 (−0.02, 0.17) | 0.140 | 0.08 (−0.01, 0.18) | 0.087 |
| Q3 | 0.07 (−0.03, 0.17) | 0.183 | 0.08 (−0.02, 0.18) | 0.104 | 0.08 (−0.02, 0.18) | 0.123 |
| Q4 | 0.50 (0.39, 0.60) | <0.001 | 0.46 (0.36, 0.56) | <0.001 | 0.43 (0.33, 0.53) | <0.001 |
| p for trend | <0.001 | <0.001 | <0.001 | |||
| ACR | 2.63 (2.36, 2.91) | <0.001 | 2.60 (2.33, 2.88) | <0.001 | 2.46 (2.18, 2.74) | <0.001 |
| Subgroups | ||||||
| Q1 | Reference | Reference | Reference | |||
| Q2 | 3.05 (−8.30, 14.40) | 0.599 | 3.03 (−8.30, 14.36) | 0.600 | 3.59 (−7.78, 14.96) | 0.537 |
| Q3 | 1.75 (−9.91, 13.41) | 0.768 | 2.14 (−9.50, 13.78) | 0.719 | 1.25 (−10.51, 13.01) | 0.835 |
| Q4 | 57.07 (45.16, 68.99) | <0.001 | 55.79 (43.87, 67.72) | <0.001 | 49.09 (37.10, 61.08) | <0.001 |
| p for trend | <0.001 | <0.001 | <0.001 | |||
Note: Adjusted model I: adjusted for none. Adjusted model II: adjusted for age, sex, BMI. Adjusted model III: adjusted for age, sex, BMI, SBP, DBP, HTN, smoking, drinking, diabetes, Hcy, TC, TG, HDL‐c, LDL‐c, hypokalemia.
Abbreviations: β2‐MG, β2‐microglobulin; ACR, albumin‐creatinine ratio; eGFR, estimated glomerular filtration rate; RBP, retinol‐binding protein.
Stratified analyses further revealed that these associations were more pronounced in patients with PA. In the PA subgroup, participants in the highest PAC quartile exhibited a 31.19 mL/min/1.73 m2 reduction in eGFR, and increases of 23.76 mg/L in RBP, 0.67 mg/L in β2‐MG, and 86.35 mg/g in ACR, compared to those in the lowest quartile (all p < 0.001; Table 4). In contrast, no significant dose‐response relationships were observed in patients with EH.
TABLE 4.
Multivariate regression analysis of the eGFR, RBP, β2‐MG, ACR, and aldosterone levels in EH and PA patients.
| Study population | Dependent variable | Adjusted model I β (95% CI) | p value | Adjusted model II β (95% CI) | p value | Adjusted model III β (95% CI) | p value |
|---|---|---|---|---|---|---|---|
| EH | eGFR | −0.49 (−0.69, −0.29) | <0.001 | −0.48 (−0.68, −0.29) | <0.001 | −0.54 (−0.71, −0.37) | <0.001 |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | −0.49 (−5.24, 4.26) | 0.841 | −0.52 (−5.27, 4.23) | 0.830 | −2.11 (−6.27, 2.06) | 0.322 | |
| Q3 | −3.38 (−8.75, 1.99) | 0.218 | −3.57 (−8.95, 1.81) | 0.195 | −2.88 (−7.50, 1.74) | 0.223 | |
| Q4 | −10.00 (−15.94, −4.06) | 0.001 | −9.72 (−15.67, −3.77) | 0.001 | −13.92(−19.10, −8.73) | <0.001 | |
| p for trend | <0.001 | 0.001 | <0.001 | ||||
| RBP | 0.07 (−0.05, 0.19) | 0.253 | 0.08 (−0.04, 0.20) | 0.195 | 0.07 (−0.05, 0.20) | 0.242 | |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | 2.75 (−0.11, 5.61) | 0.060 | 2.84 (0.01, 5.66) | 0.050 | 2.89 (−0.02, 5.80) | 0.052 | |
| Q3 | 0.10 (−3.13, 3.33) | 0.951 | 0.47 (−2.73, 3.67) | 0.772 | 0.36 (−2.87, 3.59) | 0.826 | |
| Q4 | 1.94 (−1.64, 5.52) | 0.288 | 2.21 (−1.32, 5.75) | 0.221 | 2.27 (−1.36, 5.90) | 0.220 | |
| p for trend | 0.465 | 0.347 | 0.378 | ||||
| β2‐MG | 0.01 (−0.00, 0.01) | 0.123 | 0.01 (−0.00, 0.01) | 0.132 | 0.01 (−0.00, 0.01) | 0.157 | |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | 0.07 (−0.04, 0.18) | 0.195 | 0.08 (−0.03, 0.18) | 0.147 | 0.10 (−0.01, 0.21) | 0.071 | |
| Q3 | 0.03 (−0.09, 0.15) | 0.603 | 0.04 (−0.07, 0.16) | 0.466 | 0.04 (−0.07, 0.16) | 0.469 | |
| Q4 | 0.09 (−0.04, 0.23) | 0.164 | 0.09 (−0.04, 0.22) | 0.194 | 0.10 (−0.03, 0.23) | 0.144 | |
| p for trend | 0.210 | 0.222 | 0.199 | ||||
| ACR | 0.01 (−0.11, 0.14) | 0.821 | 0.02 (−0.11, 0.14) | 0.790 | 0.02 (−0.10, 0.15) | 0.707 | |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | 2.89 (−0.07, 5.86) | 0.056 | 2.85 (−0.12, 5.81) | 0.060 | 2.98 (−0.08, 6.04) | 0.057 | |
| Q3 | 2.41 (−0.94, 5.76) | 0.159 | 2.18 (−1.18, 5.53) | 0.204 | 2.53 (−0.86, 5.92) | 0.144 | |
| Q4 | 0.82 (−2.89, 4.52) | 0.666 | 0.91 (−2.80, 4.62) | 0.631 | 1.27 (−2.54, 5.08) | 0.513 | |
| p for trend | 0551 | 0.547 | 0.429 | ||||
| PA | eGFR | −0.87 (−0.96, −0.77) | <0.001 | −0.78 (−0.88, −0.69) | <0.001 | −0.78 (−0.88, −0.68) | <0.001 |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | −7.49 (−13.43, −1.55) | 0.014 | −6.08 (−11.68, −0.47) | 0.034 | −6.51 (−12.40, −0.61) | 0.031 | |
| Q3 | −15.27 (−20.86, −9.69) | <0.001 | −14.45 (−19.72, −9.19) | <0.001 | −15.15 (−20.80, −9.50) | <0.001 | |
| Q4 | −35.24 (−40.72, −29.77) | <0.001 | −31.87 (−37.08, −26.66) | <0.001 | −31.19 (−36.75, −25.63) | <0.001 | |
| p for trend | <0.001 | <0.001 | <0.001 | ||||
| RBP | 0.77 (0.68, 0.87) | <0.001 | 0.73 (0.64, 0.83) | <0.001 | 0.71 (0.61, 0.81) | <0.001 | |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | 6.77 (0.68, 12.85) | 0.030 | 5.88 (−0.11, 11.87) | 0.055 | 5.22 (−0.91, 11.34) | 0.096 | |
| Q3 | 12.53 (6.82, 18.25) | <0.001 | 11.98 (6.36, 17.60) | <0.001 | 11.49 (5.62, 17.36) | <0.001 | |
| Q4 | 27.76 (22.15, 33.36) | <0.001 | 25.73 (20.17, 31.30) | <0.001 | 23.76 (17.99, 29.54) | <0.001 | |
| p for trend | <0.001 | <0.001 | <0.001 | ||||
| β2‐MG | 0.03 (0.02, 0.03) | <0.001 | 0.03(0.02, 0.03) | <0.001 | 0.02 (0.02, 0.03) | <0.001 | |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | 0.18 (−0.01, 0.37) | 0.059 | 0.14 (−0.04, 0.32) | 0.122 | 0.11 (−0.07, 0.30) | 0.230 | |
| Q3 | 0.25 (0.07, 0.43) | 0.007 | 0.23 (0.06, 0.40) | 0.010 | 0.20 (0.02, 0.38) | 0.029 | |
| Q4 | 0.83 (0.66, 1.01) | <0.001 | 0.74 (0.57, 0.90) | <0.001 | 0.67 (0.49, 0.84) | <0.001 | |
| p for trend | <0.001 | <0.001 | <0.001 | ||||
| ACR | 3.53 (3.12, 3.94) | <0.001 | 3.47 (3.04, 3.89) | <0.001 | 3.32 (2.88, 3.75) | <0.001 | |
| Subgroups | |||||||
| Q1 | Reference | Reference | Reference | ||||
| Q2 | 15.89 (−11.10, 42.88) | 0.249 | 13.87 (−13.07, 40.82) | 0.313 | 14.51 (−13.14, 42.16) | 0.304 | |
| Q3 | 19.96 (−5.39, 45.30) | 0.123 | 19.05 (−6.25, 44.34) | 0.141 | 17.68 (−8.83, 44.19) | 0.192 | |
| Q4 | 100.40 (75.56, 125.25) | <0.001 | 95.57 (70.53, 120.61) | <0.001 | 86.35 (60.26, 112.43) | <0.001 | |
| p for trend | <0.001 | <0.001 | <0.001 | ||||
Note: Adjusted model I: adjusted for none. Adjusted model II: adjusted for age, sex, BMI. Adjusted model III: adjusted for age, sex, BMI, SBP, DBP, HTN, smoking, drinking, diabetes, Hcy, TC, TG, HDL‐c, LDL‐c, and hypokalemia.
Abbreviations: ACR, albumin‐creatinine ratio; eGFR, estimated glomerular filtration rate; EH, essential hypertension; PA, primary aldosteronism; RBP, retinol‐binding protein; β2‐MG, β2‐microglobulin.
3.4. Effect Modification of the Association Between PAC and Renal Injury Biomarkers by Hypertension Subtype
Interaction analysis revealed significantly stronger associations between PAC and renal injury biomarkers in patients with PA compared to those with EH (Table 5). In fully adjusted models, PAC was more strongly associated with RBP (p for interaction <0.0001), β2‐MG (p for interaction <0.0001), and ACR (p for interaction <0.0001) in the PA group than in the EH group, suggesting that these relationships may be disease‐specific and independent of conventional clinical and metabolic confounders.
TABLE 5.
Interaction of hypertension type on the relationship between PAC and early renal damage biomarkers.
| Dependent variable | EH β (95% CI) | PA β (95% CI) | p for interaction |
|---|---|---|---|
| eGFR | −0.22 (−0.39, −0.05) | −0.46 (−0.57, −0.36) | 0.0135 |
| RBP | 0.15 (0.01, 0.30) | 0.62 (0.53, 0.71) | <0.0001 |
| β2‐MG | 0.01 (0.00, 0.01) | 0.01 (0.01, 0.01) | <0.0001 |
| ACR | 0.66 (−0.05, 1.36) | 2.84 (2.38, 3.29) | <0.0001 |
Note: Adjusted for age, sex, BMI, SBP, DBP, HTN, smoking, drinking, diabetes, Hcy, TC, TG, HDL‐c, LDL‐c, and hypokalemia.
Abbreviations: ACR, albumin‐creatinine ratio; EH, essential hypertension; PA, primary aldosteronism; RBP, retinol‐binding protein; β2‐MG, β2‐microglobulin.
3.5. Potential Role of RBP as a Sensitive Novel Biomarker
Among patients with PA, PAC was positively correlated with RBP (r = 0.559, p < 0.001), ACR (r = 0.583, p < 0.001), and β2‐ MG (r = 0.632, p < 0.001) (Table 2). In multivariable linear regression analyses (Table 4), RBP levels increased by 23.76 mg/L (95% CI: 17.99–29.54, p < 0.001) between the lowest and highest PAC quartiles. The corresponding increases were 0.67 mg/L for β2‐MG (95% CI: 0.49–0.84) and 86.35 mg/g for ACR (95% CI: 60.26–112.43), respectively. When considering baseline levels in the PA group (mean RBP: 53.80 mg/L; mean β2‐MG: 1.81 mg/L; median ACR: 28.71 mg/g), the relative increases across PAC quartiles were approximately 44% for RBP, 37% for β2‐MG, and 300% for ACR. Although ACR showed the largest absolute and relative increase, its low baseline value and skewed distribution may contribute to greater variability. In contrast, RBP showed a more stable and consistent increase across PAC quartiles with narrower confidence intervals, suggesting it may better reflect aldosterone‐related renal changes.
4. Discussion
This study demonstrates a significant and independent association between PAC and biomarkers of early renal injury—including RBP, β2‐MG, and ACR—in patients with PA, an association that was not observed in matched patients with EH. Specifically, individuals with PA exhibited significantly elevated levels of RBP, β2‐MG, and ACR, alongside reduced eGFR, compared with EH patients. These findings underscore the pathogenic role of aldosterone beyond its hemodynamic effects and address an important knowledge gap regarding the molecular underpinnings of early renal injury differences between PA and EH populations.
Our results are consistent with previous studies demonstrating hyperaldosteronism‐induced renal impairment independent of blood pressure levels or antihypertensive treatment regimens [24]. For example, Kawashima et al. reported a direct association between elevated PAC and renal injury markers, with evidence that aldosterone‐targeted interventions attenuate renal damage irrespective of blood pressure control [25]. PA patients exhibited significantly lower eGFR compared to EH controls, demonstrating an inverse correlation with PAC, which aligns with existing literature [26]. Notably, Fox et al. reported a weaker and non‐significant relationship between PAC and urinary ACR, possibly due to differing study populations or use of less responsive biomarkers [27]. Our findings support the hypothesis that hyperaldosteronism‐induced renal damage in PA may require alternative biomarkers (RBP and β2‐MG) that are capable of capturing subtle early changes.
RBP is a low‐molecular‐weight protein that transports vitamin A and is almost completely reabsorbed by proximal tubules under normal conditions. Importantly, RBP demonstrated strong clinical and statistical potential as an early biomarker [18, 19]. In our study, although the average RBP levels were within the normal range in both groups, PA patients had relatively higher levels, which may reflect an effect of aldosterone on tubular function. Given the reabsorptive role of the proximal tubule, RBP may reflect early tubular injury prior to the onset of overt glomerular dysfunction. However, whether such increases within the normal range truly indicate early kidney damage remains unclear. Therefore, these findings should be interpreted as evidence of an association between aldosterone levels and RBP, rather than as proof that RBP serves as an early diagnostic marker of subclinical tubular injury in PA patients. This result thus warrants further evaluation of RBP as a practical and responsive biomarker for hyperaldosteronism‐mediated renal damage.
The stronger associations between PAC and renal injury markers in PA patients may be explained by several mechanistic pathways. Elevated aldosterone levels promote glomerular hyperfiltration and structural injury through enhanced sodium retention, leading to intraglomerular hypertension and ultimately renal dysfunction [4, 7, 28]. This hyperaldosteronism‐induced glomerular hyperfiltration mechanism also accounts for the transient eGFR elevation observed in early‐stage PA [4, 25], eventually leads to structural renal damage and progressive eGFR decline under prolonged excessive exposure. In parallel, excess aldosterone activates transcriptional programs through mineralocorticoid receptor (MR) binding in renal tubular epithelial cells, promoting the expression of fibrosis and inflammation‐related factors [29, 30, 31]. Aldosterone has been shown to enhance the expression of serum‐ and glucocorticoid‐inducible protein kinase 1 (SGK1), which plays a significant role in glomerular fibrosis and inflammation. SGK1 activation by aldosterone leads to increased expression of intercellular adhesion molecule‐1 (ICAM‐1) and connective tissue growth factor (CTGF), both of which are critical in the inflammatory response and fibrotic processes within the kidney [32]. Additionally, previous studies have showed that fibrosis induced by excessive aldosterone also involves activation of the epidermal growth factor receptor (EGFR) signaling pathway and the AIF‐1/AKT/mTOR pathway [33, 34]. Moreover, chronic hypokalemia, which is prevalent in PA, may aggravate tubular dysfunction and accelerate renal injury beyond the effects of aldosterone alone [35]. These impairments in glomerular filtration function and tubular reabsorption lead to further accumulation of circulating RBP and β2‐MG, which provides a plausible mechanistic explanation for our observations. In contrast, the multifactorial pathophysiology of EH, where aldosterone plays a comparatively limited role, likely explains the attenuated associations observed in that group. Additionally, the lack of correlation between PAC and renal markers in EH patients may be explained by the physiological variability of aldosterone secretion in EH, which is influenced by circadian rhythm, stress, and other factors, in contrast to the relatively constant and autonomous secretion observed in PA.
From a clinical view, these findings highlight the value of comprehensive renal injury monitoring in PA. Combined assessment of RBP, β2‐MG, and ACR may improve the sensitivity for early detection of hyperaldosteronism‐induced renal impairment, especially in cases where eGFR remains within the normal range due to compensatory hyperfiltration [25, 36]. Incorporating these biomarkers into routine screening and follow‐up of PA patients—particularly those with treatment‐resistant hypertension—could facilitate earlier identification of renal injury and enable timely therapeutic intervention. Existing studies have demonstrated the efficacy of captopril, valsartan in reducing serum β2‐MG, and ACR levels in hypertensive patients [37, 38]. Since all participants in the current study were transitioned to standardized non‐dihydropyridine CCB and/or α‐blocker therapy 2–4 weeks prior to admission, the potential effects of this medication regimen on early renal injury biomarkers warrant further investigation. Furthermore, the robust associations between PAC and renal biomarkers strongly support early initiation of aldosterone‐targeted therapies, such as mineralocorticoid receptor antagonists (MRAs) or selective adrenal interventions, to prevent progression to overt CKD [21, 36, 39].
Despite the novel insights, several limitations should be acknowledged. First, the cross‐sectional design limits causal inference; longitudinal studies are needed to confirm the predictive value of RBP and other biomarkers following therapeutic intervention. Second, the potential influence of antihypertensive medications on biomarker levels was not accounted for. Third, generalizability may be limited, as the study cohort consisted exclusively of Chinese patients. Future studies in multi‐ethnic populations are warranted to assess the impact of genetic background and environmental factors, such as dietary sodium intake, on hyperaldosteronism‐mediated renal injury. Additionally, experimental studies involving renal histology or animal models would further elucidate the underlying mechanisms.
Looking ahead, future research should aim to establish predictive models of aldosterone‐driven renal injury incorporating PAC, RBP, β2‐MG, and ACR, and evaluate their utility in tracking treatment response. Advanced molecular techniques such as single‐cell RNA sequencing may also help describe the cellular distribution and receptor‐level effects of aldosterone within renal tissue. From a public health perspective, integrating PA‐specific renal monitoring protocols into hypertension management may prove cost‐effective and improve long‐term renal outcomes through earlier intervention and more precise risk stratification.
Author Contributions
Hai‐Long Liu conceived and designed the study, conducted the research, collected the data, analyzed and interpreted the data, drafted the manuscript, critically reviewed the manuscript for intellectual content, and performed the statistical analysis. These authors (Hai‐Long Liu, Qing‐Tian Zeng, and Yuan‐Yuan Xu) contributed equally to this work. Xiang‐Tao Zhang, Lin Li, Ning‐Peng Liang conducted the research, analyzed and interpreted the data, and performed the statistical analysis. Yi‐Fei Dong conceived and designed the experiments, conducted the research, collected the data, analyzed and interpreted the data, critically reviewed the manuscript for intellectual content, performed the statistical analysis, secured funding for the study, and provided administrative, technical, or material support.
Ethics Statement
This research, in accordance with the Declaration of Helsinki, received approval from the ethics committee of the second Affiliated Hospital of Nanchang University (IIT‐O‐2021‐032) and constituted a component of the Nanchang Primary Aldosteronism Study, registered on chictr.org (ChiCTR2200057297). Additionally, ethical approval was obtained from the Biomedical Research Ethics Committee of the second Affiliated Hospital of Nanchang University, as indicated by Ethics Approval No. 2022‐Medical Ethics Review‐05. The study protected patient privacy through data anonymization.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The author has nothing to report.
Liu H.‐L., Zeng Q.‐T., Xu Y.‐Y., et al. “Association of Plasma Aldosterone Concentration With Early Renal Injury Biomarkers in Primary Aldosteronism: A Propensity‐Matched Comparative Study.” The Journal of Clinical Hypertension 27, no. 8 (2025): 27, e70124. 10.1111/jch.70124
Funding: This work was funded by the Institutional Research Grant of the Second Affiliated Hospital of Nanchang University (2023efyA03).
Hai‐Long Liu, Qing‐Tian Zeng, and Yuan‐Yuan Xu contributed equally to this work.
Data Availability Statement
The data used in this study are available upon request from the corresponding author Yi‐Fei Dong.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used in this study are available upon request from the corresponding author Yi‐Fei Dong.