Biochemical and renal-metabolic profiles distinguishing primary aldosteronism from essential hypertension: a systematic review and meta-analysis with implications for endocrine hypertension screening

Abstract

Introduction:

To characterize electrolyte and renal-metabolic profiles in primary aldosteronism (PA) compared with essential hypertension (EH), and to examine differences between unilateral/lateralizing and bilateral PA.

Methods:

PubMed, Embase, the Cochrane Library, and Web of Science were searched from inception through 13 July 2026. Random-effects meta-analyses pooled standardized mean differences (SMD; Hedges' g), supplemented by clinical-unit mean-difference analyses, subgroup analyses, sensitivity analyses, and study-level meta-regression.

Results:

A total of 74 observational studies involving 26,143 participants were included. Compared with hypertensive controls, PA was associated with lower serum potassium (SMD = −1.15, 95% CI −1.27 to −1.02), higher serum sodium (SMD = 0.51, 95% CI 0.38 to 0.63), and lower uric acid (SMD = −0.23, 95% CI −0.35 to −0.12), whereas creatinine, estimated glomerular filtration rate (eGFR), and blood urea nitrogen (BUN) did not differ significantly. Corresponding PA-minus-control mean differences were −0.49 mmol/L for potassium, +1.20 mmol/L for sodium, and −20.7 μmol/L for uric acid. Potassium was lower in unilateral/lateralizing than in bilateral PA (SMD = −0.85). The potassium difference versus controls was greater in studies with high plasma aldosterone concentration (PAC) than in those with low PAC (SMD −1.44 vs. −1.03; p for subgroup difference = 0.010), although continuous PAC meta-regression was not significant. UACR/ACR and urinary albumin excretion were higher in PA (SMD = 0.49 and 0.73).

Discussion:

PA shows a persistent electrolyte phenotype and higher albuminuria despite similar cross-sectional filtration markers. These findings support renal assessment with UACR alongside routine filtration measures and reinforce that PA screening should not depend on overt hypokalemia alone.

Systematic Review Registration:

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261286832.

1 Introduction

Primary aldosteronism (PA) comprises disorders characterized by autonomous aldosterone secretion and is the most common cause of secondary hypertension (1, 2). PA affects approximately 5%–10% of the general hypertensive population and 15%–20% of patients with resistant hypertension (3–5). Even among patients with stage 1 hypertension, biochemically confirmed PA may be present in approximately 16%, underscoring substantial under-recognition (4). Current clinical terminology distinguishes unilateral/lateralizing PA from bilateral PA because this distinction guides treatment: unilateral/lateralizing disease may be amenable to adrenalectomy, whereas bilateral disease is generally managed medically (1, 2, 6–8). Historical labels such as aldosterone-producing adenoma (APA) and idiopathic hyperaldosteronism (IHA) remain common in the literature but do not fully encompass the contemporary clinical classification.

Aldosterone increases distal sodium reabsorption and potassium secretion through mineralocorticoid receptor signaling (9, 10). The wider use of aldosterone-to-renin ratio (ARR) screening, together with recognition of a continuum of renin-independent aldosterone production, has expanded the PA phenotype beyond the classic hypokalemic presentation (1, 2, 4, 6, 8, 11–14). This changing case spectrum complicates interpretation of older estimates of biochemical severity and target-organ involvement. Previous meta-analyses have examined cardiovascular, skeletal, glycemic, and renal outcomes (15–19), but important uncertainties remain. In particular, a 2020 meta-analysis of 22 studies reported higher estimated glomerular filtration rate (eGFR) in PA than in essential hypertension (EH) (18), whereas subsequent large studies have broadened the evidence base. Urinary albumin and serum uric acid may provide complementary information on renal hemodynamics and tubular handling, yet these outcomes have not been integrated with electrolyte and filtration-marker data (20–22).

We therefore synthesized electrolyte, renal-metabolic, and urinary albumin outcomes in PA versus EH, examined differences between unilateral/lateralizing and bilateral PA, and assessed whether study characteristics and hormonal profiles helped explain variation across studies.

Because this review evaluated a disease exposure rather than a therapeutic intervention, the question was structured using an exposure-based PICOS/PECOS framework: adults with hypertension constituted the population; biochemically confirmed PA was the exposure; EH, or bilateral PA in subtype analyses, was the comparator; electrolyte, renal-metabolic, urinary albumin, bicarbonate, and hormonal measures were the outcomes; and eligible designs were observational cross-sectional, case-control, and cohort studies.

2 Materials and methods

2.1 Search strategy

A systematic search of PubMed, Embase, the Cochrane Library, and Web of Science was conducted from database inception through July 13, 2026. Complete search strings for all databases appear in Supplementary Table S1. Search terms combined PA-related terms (“primary aldosteronism”, “Conn syndrome”, “aldosterone-producing adenoma”, and “idiopathic hyperaldosteronism”) with contemporary subtype terms (“unilateral”, “lateralizing”, “lateralized”, “lateralised”, and “bilateral”) and outcome-specific terms for potassium, sodium, creatinine, glomerular filtration rate, blood urea nitrogen, uric acid, albuminuria/proteinuria, bicarbonate, and alkalosis. No language restrictions were applied. Reference lists of all included studies and relevant systematic reviews were also hand-searched.

This systematic review and meta-analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline (23). The study protocol was registered in advance with PROSPERO (registration number: CRD420261286832).

2.2 Inclusion and exclusion criteria

Eligible studies met the following criteria (1): observational design (cross-sectional, cohort, or case-control) (2); biochemically confirmed PA based on a standardized confirmatory test, such as the captopril challenge test, saline infusion test, fludrocortisone suppression test, or oral sodium-loading test, using criteria consistent with the 2016 Endocrine Society guideline or its 2025 update (1, 2); and (3) at least one eligible outcome reported as a mean and standard deviation or in a convertible form. For PA-versus-EH analyses, the comparator comprised adults with EH. Studies without an external EH comparator were eligible when they directly compared unilateral/lateralizing with bilateral PA. Subtype classification was based preferentially on adrenal venous sampling (AVS), surgery, or pathology; imaging-only classifications were retained for sensitivity analyses (24–26).

All control groups in the included studies consisted of adults with hypertension from whom common secondary causes (including pheochromocytoma, Cushing syndrome, renovascular hypertension, and renal parenchymal disease when specified) had been explicitly excluded or ruled out according to standard clinical practice, thus meeting the definition of EH. Three studies designated their control group as low-renin essential hypertension (LREH); for this meta-analysis, all such control groups were uniformly classified as EH and pooled accordingly. Because low-renin selection could influence electrolyte and renin-related contrasts, sensitivity analyses were repeated after excluding LREH studies, and comparator-type subgroup analyses were undertaken when sufficient studies were available.

Exclusion criteria encompassed reviews, animal experiments, case reports, and conference abstracts; studies in which the PA diagnosis was unclear or not confirmed through a standardized confirmatory test; studies for which data could not be extracted or converted; and duplicate publications (the most complete version was retained). Retrieved records were loaded into reference management software for deduplication and screening.

2.3 Data extraction and quality assessment

Two reviewers (Xuan Zhao and Yajing Zhao) independently screened the literature and extracted data. Extracted items included first author, publication year, country or region, study design, sample size, participant age, sex distribution, and the mean and standard deviation of each outcome. Additional study-level variables included recruitment setting, diabetes, duration of hypertension, medication preparation or washout, PA screening and confirmation procedures, subtype method, control definition, renal exclusion criteria, plasma aldosterone concentration (PAC), plasma renin activity (PRA), or direct renin concentration, ARR, urinary aldosterone, and other reported hormonal markers. For continuous variables reported as medians with interquartile ranges or ranges, means and standard deviations were estimated using the methods of Luo et al. (27) and Wan et al. (28). Values reported as mean ± standard error of the mean (SEM) were converted to standard deviations using SD = SEM × √n. The between-group difference in male proportion (PA minus EH, percentage points) was calculated for study-level sex-balance analyses.

The risk-of-bias tool was selected according to the sampling framework and longitudinal design of each study. Case-control and cohort studies were evaluated using the Newcastle–Ottawa Scale (NOS) (29). A score of ≥6/9 was used as a descriptive threshold indicating higher methodological quality. Analytical cross-sectional studies were assessed using the 2017 eight-item Joanna Briggs Institute (JBI) critical appraisal checklist (30). Each item was rated Yes, No, Unclear, or Not applicable. Item-level NOS and JBI assessments and the overall appraisal summary are provided in Supplementary Tables S2-S5. The JBI checklist does not define a validated total score or universal cutoff; therefore, no numerical summary score was calculated. Two reviewers independently assessed risk of bias, with disagreements resolved through discussion or consultation with a third reviewer when necessary.

2.4 Statistical analysis

Statistical analyses were performed using R software (version 4.5.1) and the metafor package (31). Because laboratory assay methods and reporting units varied across studies, the standardized mean difference (SMD; Hedges’ g with small-sample correction) was selected as the primary effect-size metric. Pooled effect sizes were estimated with the DerSimonian–Laird random-effects model (32), and 95% confidence intervals (95% CI) were computed.

Heterogeneity across studies was assessed with Cochran’s Q test and the I² statistic (33): I² < 50% indicated low, 50%–75% moderate, and above 75% substantial heterogeneity. When substantial heterogeneity was identified, its potential sources were explored through subgroup analyses and meta-regression.

Egger’s regression test (34) was used to assess publication bias, supplemented by funnel-plot inspection and the trim-and-fill method (35) as a sensitivity check. Formal testing was applied when at least 10 studies were included. Each study’s influence on the pooled effect size was examined using leave-one-out analysis to assess the robustness of the pooled estimates. In addition, radial (Galbraith) plots (36) were generated to identify outlying studies that might exert a disproportionate influence on the pooled estimate.

All eligible studies were retained in the primary pooled analyses. Because Galetta et al., 2009 reported implausibly small SDs and exerted disproportionate influence in several study-level models, post hoc exclusion analyses were performed as robustness checks. These analyses were not prespecified in PROSPERO, and complete-data and exclusion results are reported in parallel.

Subgroup analyses examined geographic region (East Asia vs. non-East Asia), publication period, comparator type, and, for eGFR, exclusion of preexisting renal disease. Recruitment-setting analyses were conducted for potassium and sodium, and the PA-minus-EH male-percentage difference was modeled per 10-percentage-point increment for potassium, sodium, creatinine, and eGFR. Publication year was modeled continuously. Study-level PAC was harmonized to ng/dL and examined continuously and with an exploratory 30-ng/dL cutoff. PAC, PRA, direct renin concentration, raw ARR, log-transformed ARR, and urinary aldosterone were analyzed separately by assay and scale; ARR was not derived by dividing group mean PAC by group mean renin. Hormone-outcome meta-regression was performed when at least 10 studies were available, treated as exploratory when 6–9 studies were available, and omitted when fewer than 6 were available. Urinary albumin measures were classified as urinary albumin-to-creatinine ratio/albumin-to-creatinine ratio (UACR/ACR), urinary albumin excretion, albumin concentration, or total protein/urinary protein-to-creatinine ratio (UPCR), and bicarbonate measures were separated according to direct measurement or calculation method. When units were harmonizable, parallel random-effects mean-difference (MD) analyses were performed for potassium, sodium, uric acid, and eGFR. Descriptive SMD back-conversions used the median study-specific pooled within-group SD and were not interpreted as directly pooled MDs (Supplementary Table S6). All tests were two-sided, with p < 0.05 denoting statistical significance.

3 Results

3.1 Literature search results

Overall, 11,473 records were retrieved from the four databases, and 9,275 remained after removal of 2,198 duplicates. Following title-and-abstract screening, 162 reports were sought for retrieval; 41 could not be retrieved, leaving 121 reports for full-text eligibility assessment. After exclusion of 47 reports, 74 studies were included in the systematic review and meta-analysis (Figure 1). Search strings appear in Supplementary Table S1.

Figure 1

3.2 Study characteristics and quality assessment

All 74 included studies were observational. Study characteristics are summarized in Table 1, including participant demographics, diabetes status, hypertension duration, medication preparation, PA screening and confirmation procedures, subtype methods, renal exclusion criteria, PAC categories, and appraisal results. Detailed item-level assessments and the overall appraisal summary are provided in Supplementary Tables S2-S5. All 54 studies assessed using the NOS met the descriptive threshold of ≥6/9. Among the 20 analytical cross-sectional studies assessed using the JBI checklist, 17 received Yes judgments for all eight items; two did not identify or address confounding factors, and one had an unclear strategy for addressing confounding. No study was excluded solely on the basis of critical appraisal. Publication years ranged from 2005 to 2026, and studies were conducted across East Asia, Europe, North Africa, and Oceania. The number of studies and sample sizes for each outcome are presented in Table 2.

3.3 Serum potassium

Fifty-nine studies (PA: 7,063; hypertensive controls: 14,621) were included in the comparison of serum potassium. PA patients had significantly lower serum potassium than controls (SMD = −1.15, 95% CI: −1.27 to −1.02, I² = 91.8%, p < 0.001) (Figure 2A). The parallel clinical-unit sensitivity analysis yielded an MD of −0.49 mmol/L (95% CI: −0.53 to −0.44) (Supplementary Figure S1A). In the subtype analysis, 17 studies (unilateral/lateralizing PA: 1,117; bilateral PA: 999) showed lower potassium in unilateral/lateralizing PA (SMD = −0.85, 95% CI: −1.13 to −0.57, I² = 87.8%, p < 0.001) (Figure 2B).

Figure 2

After excluding Galetta et al., 2009, meta-regression showed that the potassium difference narrowed with advancing publication year (k = 58; slope = 0.038/year, 95% CI: 0.021 to 0.055, p < 0.001, R² = 18.2%) (Figure 3); the complete 59-study analysis was similar (slope = 0.042/year, R² = 16.5%) and is shown in Supplementary Figure S2. Studies published in or before 2015 had a larger potassium difference than those published later (SMD = −1.50 vs. −0.96; Qbetween = 17.04, p < 0.001) (Figure 4A), whereas East Asian and non-East Asian studies did not differ (SMD = −1.07 vs. −1.28; p = 0.148) (Figure 4B). Recruitment-setting subgroups also did not differ (p = 0.104), although only two community/primary-care-linked studies contributed potassium data. A 10-percentage-point increase in the PA-minus-EH male proportion was associated with a more negative potassium SMD in both the complete dataset (β = −0.274, p < 0.001) and the exclusion analysis (β = −0.241, p = 0.002) (Supplementary Figures S3B, C).

Figure 3

Figure 4

3.4 Serum sodium

Thirty-one studies (PA: 3,380; hypertensive controls: 6,062) were included. Serum sodium concentrations were significantly elevated in PA (SMD = 0.51, 95% CI: 0.38 to 0.63, I² = 84.0%, p < 0.001) (Figure 5A). The parallel clinical-unit sensitivity analysis yielded an MD of +1.20 mmol/L (95% CI: +0.90 to +1.50) (Supplementary Figure S1B). A subtype analysis of 7 studies (unilateral/lateralizing PA: 607; bilateral PA: 580) showed higher sodium in unilateral/lateralizing PA (SMD = 0.43, 95% CI: 0.22 to 0.64, I² = 66.6%, p < 0.001) (Figure 5B). Publication year was not a significant moderator (slope = 0.013/year, p = 0.265). Bicarbonate evidence was limited: one study reported directly measured bicarbonate (MD = 3.00 mmol/L), two studies reported standard calculated bicarbonate (pooled MD = 0.79 mmol/L, 95% CI: −0.34 to 1.93), and one study reported potassium-adjusted calculated bicarbonate (MD = 0.85 mmol/L). These measures were not pooled together.

Figure 5

Publication-year and geographic analyses for sodium, creatinine, eGFR, and uric acid are shown in Supplementary Figures S4 and S5; the corresponding acid–base analyses are shown in Supplementary Figures S6C–E.

3.5 Serum creatinine

Forty-two studies (PA: 5,439; hypertensive controls: 10,788) were included. Serum creatinine did not differ significantly between groups (SMD = 0.01, 95% CI: −0.05 to 0.07, I² = 61.2%, p = 0.803) (Figure 6A). Thirteen studies comparing unilateral/lateralizing with bilateral PA also showed no significant difference (SMD = −0.07, 95% CI: −0.24 to 0.10, I² = 64.3%). Strict and expanded subtype sensitivity analyses are shown in Supplementary Figures S7 and S8.

Figure 6

3.6 Estimated glomerular filtration rate

Thirty-six studies (PA: 5,307; hypertensive controls: 9,991) were included. Overall eGFR showed no significant difference between the two groups (SMD = 0.02, 95% CI: −0.05 to 0.09, I² = 69.4%, p = 0.625) (Figure 6B). The parallel MD-based analysis was also null (MD = 0.34 mL/min/1.73 m², 95% CI: −1.07 to 1.75, I² = 69.4%, p = 0.637) (Figure 7). In studies excluding renal disease (k = 15), the pooled SMD was −0.04 (95% CI: −0.14 to 0.06), whereas studies not explicitly excluding renal disease (k = 21) yielded 0.06 (95% CI: −0.03 to 0.16); the subgroup difference was not significant (Qbetween = 2.01, p = 0.156) (Figure 8). Six subtype studies showed no eGFR difference between unilateral/lateralizing and bilateral PA (SMD = 0.05, 95% CI: −0.09 to 0.19).

Figure 7

Figure 8

3.7 Urinary albumin outcomes

Urinary albumin outcomes were analyzed according to measurement type. UACR/ACR was higher in PA (k = 8; SMD = 0.49, 95% CI: 0.33 to 0.66, I² = 72.8%, p < 0.001) (Figure 9A), as was urinary albumin excretion (k = 9; SMD = 0.73, 95% CI: 0.22 to 1.25, I² = 94.9%, p = 0.005) (Figure 9B). A sensitivity analysis retaining one preferred albumin-specific indicator per study yielded SMD = 0.60 (k = 19; 95% CI: 0.38 to 0.82) (Figure 9C). Total urinary protein/UPCR was analyzed separately (k = 3; SMD = 0.17).

Figure 9

Additional urinary albumin and total urinary protein analyses are shown in Supplementary Figures S6A, B.

3.8 Blood urea nitrogen

Eight studies (PA: 1,271; hypertensive controls: 2,392) were included. BUN showed no significant difference between PA and controls (SMD = −0.07, 95% CI: −0.21 to 0.06, I² = 61.0%, p = 0.287) (Figure 6C).

3.9 Serum uric acid

Twenty-three studies (PA: 2,857; hypertensive controls: 4,293) were included. Serum uric acid was significantly reduced in PA (SMD = −0.23, 95% CI: −0.35 to −0.12, I² = 78.1%, p < 0.001) (Figure 10A). The parallel clinical-unit sensitivity analysis yielded an MD of −20.7 μmol/L (95% CI: −29.9 to −11.6) (Supplementary Figure S1C). The direction and significance were preserved in leave-one-out analyses. Three studies compared unilateral/lateralizing with bilateral PA, and the difference was not statistically significant (SMD = −0.19, 95% CI: −0.40 to 0.03, I² = 13.7%, p = 0.094) (Figure 10B).

Figure 10

3.10 Hormone profiles and study-level meta-regression

Across compatible datasets, PA was associated with higher PAC (k = 40; SMD = 1.45), higher raw ARR (k = 27; SMD = 2.18), and higher urinary aldosterone (k = 6; SMD = 1.03), together with lower PRA (k = 26; SMD = −1.34) and lower direct renin concentration (k = 10; SMD = −0.67); 95% CIs are shown in Supplementary Figure S9. Original study-level hormone measurements and reported units are summarized in Supplementary Table S7. PRA and direct renin were analyzed separately, as were raw and log-transformed ARR. Unilateral/lateralizing PA showed higher PAC and ARR and lower PRA than bilateral PA. Detailed hormone-profile comparisons between unilateral/lateralizing and bilateral PA are shown in Supplementary Figure S10. Continuous PAC was not associated with potassium, sodium, or eGFR effect sizes. In complete-data potassium models, greater PRA suppression and larger ARR contrasts were associated with more negative potassium SMDs, but both associations disappeared after exclusion of Galetta et al., 2009 (Supplementary Figures S11D–E, S11G–H). For sodium, greater PRA suppression and larger ARR contrasts were associated with smaller positive sodium SMDs (Supplementary Figures S11F, S11I); ARR contrast was not associated with eGFR (p = 0.250) (Supplementary Figure S11J). Angiotensin I/II and catecholamines were too sparsely reported for pooling.

3.11 Sensitivity analysis and publication bias

Leave-one-out analyses did not materially alter the direction of the principal pooled estimates (Supplementary Figure S12). Excluding low-renin EH studies yielded similar estimates for potassium, sodium, creatinine, and BUN (Supplementary Figure S13). Comparator-type subgroups differed for potassium (p = 0.002) but not sodium or creatinine (Supplementary Figure S14). The cumulative analysis showed stabilization of the potassium estimate toward SMD = −1.15 (95% CI: −1.27 to −1.02) (Figure 11). Egger’s test was non-significant for sodium, creatinine, eGFR, and uric acid; BUN was not assessed because only eight studies were available (Figure 12). Funnel-plot asymmetry was present for potassium in the complete dataset (p < 0.001) and remained after excluding Galetta et al., 2009 (p = 0.007), but trim-and-fill imputed no studies, and the pooled SMD remained −1.12 (95% CI: −1.24 to −1.00) (Figure 13). The high-PAC subgroup had a larger potassium difference than the low-PAC subgroup (SMD = −1.44 vs. −1.03; Qbetween = 6.65, p = 0.010) (Figure 14). An expanded potassium analysis including studies with median or converted PAC values is shown in Supplementary Figure S15A; additional PAC-stratified analyses for sodium, eGFR, and UACR/ACR are presented in Supplementary Figures S15B–D.

Figure 11

Figure 12

Figure 13

Figure 14

4 Discussion

This meta-analysis of 74 studies and 26,143 participants identifies a coherent but evolving biochemical phenotype of PA. Compared with hypertensive controls, PA was associated with lower potassium, modestly higher sodium, and lower uric acid. The electrolyte contrast was greater in unilateral/lateralizing PA and in older studies, while creatinine, eGFR, and BUN were similar between groups. In contrast, UACR/ACR and urinary albumin excretion were higher in PA, revealing a separation between routine filtration markers and albuminuric renal injury.

The potassium and sodium findings are biologically consistent with mineralocorticoid receptor activation in the distal nephron, which increases epithelial sodium channel and Na+/K+-ATPase activity and promotes sodium retention and potassium secretion (9, 10). On the clinical scale, PA was associated with a mean potassium difference of −0.49 mmol/L and a sodium difference of +1.20 mmol/L. These group-average shifts are clinically meaningful without implying that every patient has hypokalemia or hypernatremia.

This distinction matters because contemporary screening increasingly identifies milder, normokalemic PA (2, 14). The larger potassium difference in studies published in or before 2015, together with attenuation across publication year, is compatible with a shift from highly selected referral phenotypes toward broader case detection. This finding supports screening strategies that do not rely on overt hypokalemia.

Unilateral/lateralizing PA showed larger potassium and sodium differences than bilateral PA, consistent with greater aldosterone autonomy in many unilateral cases (7, 37). These biochemical differences may help characterize disease severity, but treatment-directed subtyping still requires AVS and appropriate imaging (24–26).

The PAC-stratified analysis provided a similar signal: high-PAC studies showed a larger potassium contrast than low-PAC studies. Continuous PAC meta-regression was null, indicating that the categorical finding is supportive rather than evidence of a linear patient-level relationship.

The renal findings add a distinct contribution. Neither creatinine, eGFR, nor BUN differed significantly between PA and hypertensive controls, and the updated clinical-unit eGFR analysis did not reproduce the higher eGFR reported in the 2020 meta-analysis (18). The broader evidence base and inclusion of milder PA phenotypes may partly explain this difference.

A null cross-sectional eGFR result does not resolve whether renal tissue is unaffected. Aldosterone-related volume expansion and hyperfiltration can preserve or elevate filtration at diagnosis, whereas chronic mineralocorticoid excess promotes glomerular and tubulointerstitial injury (18–20). Longitudinal studies before and after targeted treatment remain necessary to separate hemodynamic from structural effects.

Against this background, the higher UACR/ACR values and urinary albumin excretion in PA are clinically informative. Albuminuria may detect glomerular or microvascular injury before a decline in creatinine-based filtration measures becomes apparent. These findings support assessment of UACR alongside creatinine and eGFR in the renal evaluation of PA.

Serum uric acid was modestly lower in PA, with a mean difference of −20.7 μmol/L. Volume expansion, hemodilution, altered renal hemodynamics, and tubular urate handling are plausible contributors (18, 21), although the available data do not distinguish among these mechanisms. Lower uric acid is therefore best viewed as part of the renal-metabolic phenotype rather than as evidence of preserved renal health.

Evidence for bicarbonate and metabolic alkalosis remained sparse and methodologically heterogeneous. Recruitment-setting analyses helped describe between-study variation but were limited by the number of studies and available community/primary-care evidence. Sex-balance and hormone-outcome analyses were further limited by assay heterogeneity and their study-level design.

The principal strength of this review is the integrated analysis of electrolytes, clinical-unit effects, PA subtypes, filtration markers, albuminuria, uric acid, and study-level hormonal profiles. Separate handling of PRA and direct renin, raw and log-transformed ARR, distinct urinary albumin measures, and directly measured versus calculated bicarbonate reduced inappropriate pooling across non-equivalent outcomes.

Several limitations remain. All included studies were observational, the majority were cross-sectional, and substantial heterogeneity persisted for several outcomes. Medication preparation, assay methods, diagnostic pathways, and case mix varied across studies, and individual-patient data were unavailable. The post hoc exclusion of Galetta et al., 2009 was not prespecified, although complete-data and exclusion analyses were reported in parallel.

Generalizability is greatest for referral and tertiary-care populations in East Asia and Europe, which contributed the majority of the evidence. Future studies should recruit broader primary-care populations, standardize medication preparation and biochemical measurements, and longitudinally assess UACR and filtration markers. Treatment response should also be evaluated using appropriate biomarkers during medical therapy (38) and standardized clinical and biochemical outcomes after adrenalectomy (39).

5 Conclusion

PA is associated with a recognizable electrolyte and renal-metabolic profile: lower potassium, modestly higher sodium, lower uric acid, and higher albuminuria than in hypertensive controls. The electrolyte contrast is more pronounced in unilateral/lateralizing PA and appears smaller in more recent studies, consistent with broader recognition of milder disease. Similar cross-sectional creatinine, eGFR, and BUN values do not exclude renal involvement; UACR may provide a more sensitive baseline signal. These findings support PA screening beyond overt hypokalemia and renal assessment that includes albuminuria alongside routine filtration markers.

References

• 1

  FunderJWCareyRMManteroFMuradMHReinckeMShibataHet al. The management of primary aldosteronism: Case detection, diagnosis, and treatment: An endocrine society clinical practice guideline. J Clin Endocrinol Metab. (2016) 101:1889–916. doi: 10.1210/jc.2015-4061

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  AdlerGKStowasserMCorreaRRKhanNKlineGMcGowanMJet al. Primary aldosteronism: An endocrine society clinical practice guideline. J Clin Endocrinol Metab. (2025) 110:2453–95. doi: 10.1210/clinem/dgaf284

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  MonticoneSBurrelloJTizzaniDBertelloCViolaABuffoloFet al. Prevalence and clinical manifestations of primary aldosteronism encountered in primary care practice. J Am Coll Cardiol. (2017) 69:1811–20. doi: 10.1016/j.jacc.2017.01.052

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  BrownJMSiddiquiMCalhounDACareyRMHopkinsPNWilliamsGHet al. The unrecognized prevalence of primary aldosteronism: A cross-sectional study. Ann Intern Med. (2020) 173:10–20. doi: 10.7326/M20-0065

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  HuangMLiJZhaoXFuRLiXJiangW. Global and regional prevalence and cardiovascular risk of primary aldosteronism: A systematic review and meta-analysis. Curr Probl Cardiol. (2024) 49:102791. doi: 10.1016/j.cpcardiol.2024.102791

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  NaruseMKatabamiTShibataHSoneMTakahashiKTanabeAet al. Japan endocrine society clinical practice guideline for the diagnosis and management of primary aldosteronism 2021. Endocr J. (2022) 69:327–59. doi: 10.1507/endocrj.ej21-0508

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  WilliamsTAGomez-SanchezCERaineyWEGiordanoTJLamAKMarkerAet al. International histopathology consensus for unilateral primary aldosteronism. J Clin Endocrinol Metab. (2021) 106:42–54. doi: 10.1210/clinem/dgaa484

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  MulateroPSechiLAWilliamsTALendersJWMReinckeMSatohFet al. Subtype diagnosis, treatment, complications and outcomes of primary aldosteronism and future direction of research: A position statement and consensus of the working group on endocrine hypertension of the European Society of Hypertension. J Hypertens. (2020) 38:1929–36. doi: 10.1097/hjh.0000000000002520

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  BoothREJohnsonJPStockandJD. Aldosterone. Adv Physiol Educ. (2002) 26:8–20. doi: 10.1152/advan.00051.2001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 10

  TsilosaniAGaoCZhangW. Aldosterone-regulated sodium transport and blood pressure. Front Physiol. (2022) 13:770375. doi: 10.3389/fphys.2022.770375

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  McEvoyJWMcCarthyCPBrunoRMBrouwersSCanavanMDCeconiCet al. 2024 ESC guidelines for the management of elevated blood pressure and hypertension. G Ital Cardiol (Rome). (2024) 25:e1–e107. doi: 10.1714/4361.43518

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  HaJParkJHKimKJKimJHJungKYLeeJet al. 2023 Korean endocrine society consensus guidelines for the diagnosis and management of primary aldosteronism. Endocrinol Metab (Seoul). (2023) 38:597–618. doi: 10.3803/enm.2023.1789

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  ParksookWWBrownJMOmataKTezukaYOnoYSatohFet al. The spectrum of dysregulated aldosterone production: An international human physiology study. J Clin Endocrinol Metab. (2024) 109:2220–32. doi: 10.1210/clinem/dgae145

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  BurrelloJMonticoneSLosanoICavagliàGBuffoloFTettiMet al. Prevalence of hypokalemia and primary aldosteronism in 5100 patients referred to a tertiary hypertension unit. Hypertension. (2020) 75:1025–33. doi: 10.1161/hypertensionaha.119.14063

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  MonticoneSD'AscenzoFMorettiCWilliamsTAVeglioFGaitaFet al. Cardiovascular events and target organ damage in primary aldosteronism compared with essential hypertension: A systematic review and meta-analysis. Lancet Diabetes Endocrinol. (2018) 6:41–50. doi: 10.1016/s2213-8587(17)30319-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  WangAWangYLiuHHuXLiJXuHet al. Bone and mineral metabolism in patients with primary aldosteronism: A systematic review and meta-analysis. Front Endocrinol (Lausanne). (2022) 13:1027841. doi: 10.3389/fendo.2022.1027841

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  ManosroiWAtthakomolPWattanawitawasPBuranapinS. Differences in glycemic abnormalities between primary aldosteronism and essential hypertension: A systematic review and meta-analysis. Front Endocrinol (Lausanne). (2022) 13:870047. doi: 10.3389/fendo.2022.870047

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  MonticoneSSconfienzaED'AscenzoFBuffoloFSatohFSechiLAet al. Renal damage in primary aldosteronism: A systematic review and meta-analysis. J Hypertens. (2020) 38:3–12. doi: 10.1097/HJH.0000000000002216

  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  HundemerGLCurhanGCYozampNWangMVaidyaA. Renal outcomes in medically and surgically treated primary aldosteronism. Hypertension. (2018) 72:658–66. doi: 10.1161/hypertensionaha.118.11568

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  SechiLANovelloMLapennaRBaroselliSNadaliniEColussiGLet al. Long-term renal outcomes in patients with primary aldosteronism. Jama. (2006) 295:2638–45. doi: 10.1001/jama.295.22.2638

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  DuLZongYLiHWangQXieLYangBet al. Hyperuricemia and its related diseases: Mechanisms and advances in therapy. Signal Transduct Target Ther. (2024) 9:212. doi: 10.1038/s41392-024-01916-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  ZhaoXJiaoHZhangRZhaoHMaLJiaZet al. Association between serum uric acid levels and primary aldosteronism. BMC Nephrol. (2025) 26:618. doi: 10.1186/s12882-025-04504-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  PageMJMcKenzieJEBossuytPMBoutronIHoffmannTCMulrowCDet al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Bmj. (2021) 372:n71. doi: 10.1136/bmj.n71

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 24

  DekkersTPrejbiszAKoolLJSGroenewoudHVelemaMSpieringWet al. Adrenal vein sampling versus CT scan to determine treatment in primary aldosteronism: An outcome-based randomised diagnostic trial. Lancet Diabetes Endocrinol. (2016) 4:739–46. doi: 10.1016/s2213-8587(16)30100-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  RossiGPRossittoGAmarLAziziMRiesterAReinckeMet al. Clinical outcomes of 1625 patients with primary aldosteronism subtyped with adrenal vein sampling. Hypertension. (2019) 74:800–8. doi: 10.1161/hypertensionaha.119.13463

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  WilliamsTABurrelloJSechiLAFardellaCEMatrozovaJAdolfCet al. Computed tomography and adrenal venous sampling in the diagnosis of unilateral primary aldosteronism. Hypertension. (2018) 72:641–9. doi: 10.1161/hypertensionaha.118.11382

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27

  LuoDWanXLiuJTongT. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res. (2018) 27:1785–805. doi: 10.1177/0962280216669183

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  WanXWangWLiuJTongT. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. (2014) 14:135. doi: 10.1186/1471-2288-14-135

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  WellsGSheaBO'ConnellDPetersonJWelchVLososMet al. The Newcastle–Ottawa Scale (NOS) for assessing the quality of non-randomized studies in meta-analysis. In: Ottawa Hospital Research Institute. Ottawa: Ottawa Hospital Research Institute (2000).

  • Google Scholar
  • View reference in article

• 30

  MoolaSMunnZTufanaruCAromatarisESearsKSfetcuRet al. Chapter 7: Systematic reviews of etiology and risk. In: AromatarisEMunnZ editors. Joanna Briggs Institute Reviewer's Manual. Adelaide: The Joanna Briggs Institute. (2017).

  • Google Scholar
  • View reference in article

• 31

  ViechtbauerW. Conducting meta-analyses in R with the metafor package. J Stat Software. (2010) 36:1–48. doi: 10.18637/jss.v036.i03

  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  DerSimonianRLairdN. Meta-analysis in clinical trials. Controlled Clin Trials. (1986) 7:177–88. doi: 10.1016/0197-2456(86)90046-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 33

  HigginsJPThompsonSGDeeksJJAltmanDG. Measuring inconsistency in meta-analyses. Bmj. (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  EggerMDavey SmithGSchneiderMMinderC. Bias in meta-analysis detected by a simple, graphical test. Bmj. (1997) 315:629–34. doi: 10.1136/bmj.315.7109.629

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  DuvalSTweedieR. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. (2000) 56:455–63. doi: 10.1111/j.0006-341X.2000.00455.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  GalbraithRF. A note on graphical presentation of estimated odds ratios from several clinical trials. Stat Med. (1988) 7:889–94. doi: 10.1002/sim.4780070807

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  Abd RahmanANAzizanEA. Somatic mutations associated with aldosterone-producing adenomas (APAs). Genes (Basel). (2025) 16:778. doi: 10.3390/genes16070778

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  HundemerGLLeungAAKlineGABrownJMTurcuAFVaidyaA. Biomarkers to guide medical therapy in primary aldosteronism. Endocr Rev. (2024) 45:69–94. doi: 10.1210/endrev/bnad024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  WilliamsTALendersJWMMulateroPBurrelloJRottenkolberMAdolfCet al. Outcomes after adrenalectomy for unilateral primary aldosteronism: An international consensus on outcome measures and analysis of remission rates in an international cohort. Lancet Diabetes Endocrinol. (2017) 5:689–99. doi: 10.1016/s2213-8587(17)30135-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article