Focus on Renal Morphology, Chronic Kidney Disease, and Urinary System Malignancies in Acromegaly: Report on Data Collected Over a 40-year Period

Focus on Renal Morphology, Chronic Kidney Disease, and Urinary System Malignancies in Acromegaly: Report on Data Collected Over a 40-year Period | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Focus on Renal Morphology, Chronic Kidney Disease, and Urinary System Malignancies in Acromegaly: Report on Data Collected Over a 40-year Period Polat Ercan, Busra Firlatan Yazgan, Suleyman Nahit Sendur, Seda Hanife Oguz, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9171899/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Purpose While the cardiovascular and metabolic morbidities of acromegaly are well-established, data regarding long-term morphological and functional renal changes remain limited. This study evaluates the prevalence and independent predictors of renal cysts and chronic kidney disease (CKD), and additionally examines the frequency of urinary system malignancies relative to the general population in a large acromegaly cohort. Methods We retrospectively evaluated medical records, radiological findings, and clinical parameters of 394 patients with acromegaly monitored at a single tertiary center over four decades. Independent predictors of renal cysts and CKD were assessed using multivariate logistic regression. Standardized incidence ratios (SIRs) for urinary system cancers were calculated using Turkish population data as the reference. Results The cohort (202 male, 192 female) had a median disease duration of 17 years. Renal cysts were detected in 41% of patients, of which 47.4% were bilateral. CKD and nephrolithiasis were present in 16.1% and 15.1% of patients, respectively; notably, nearly two-thirds (64.9%) of CKD patients had preserved eGFR, with CKD diagnosed on the basis of albuminuria or structural abnormalities. Multivariate regression identified advanced age, nephrolithiasis, liver cysts, and multiple neoplasms as independent risk factors for renal cyst formation. Notably, higher baseline serum potassium was inversely associated with cyst development (OR: 0.37 per 1 mEq/L increase, p = 0.013). Advanced age, male sex, hypertension, and nephrolithiasis were independent predictors of CKD. Cross-sectional GH and absolute IGF-1 levels were not directly associated with CKD or cyst prevalence. Urinary system cancers were among the most frequent malignancies after thyroid cancer, with a greater than seven-fold excess compared to the general population (SIR: 7.38, 95% CI: 2.97–15.21; p < 0.001). Conclusions Renal cysts, CKD, and urinary system malignancies are prevalent in acromegaly. Structural and functional renal alterations may be related to cumulative hormonal exposure and metabolic comorbidities rather than cross-sectional GH/IGF-1 measurements alone, though the absence of matched controls limits causal inference. The inverse association between baseline potassium and cyst risk, and the excess of urinary system cancers, support the need for dedicated renal surveillance in long-term acromegaly management. Acromegaly pituitary adenoma growth hormone IGF-1 chronic kidney disease renal cyst nephrolithiasis urinary neoplasm Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Acromegaly is a rare endocrine disorder characterized by chronic overproduction of growth hormone (GH) and insulin-like growth factor-1 (IGF-1). Its total prevalence ranges from 2.8 to 13.7 cases per 100 000 persons, with an annual incidence of 0.2 to 1.1 per 100 000 (1). In 95% of the patients, acromegaly is caused by pituitary somatotroph adenomas. Without appropriate treatment, acromegaly leads to increased morbidity and mortality owing to elevated levels of GH and IGF-1. While the cardiovascular, respiratory, neoplastic and metabolic consequences of prolonged GH/IGF-1 excess are extensively documented, the long-term impact on kidney morphology and function remains poorly characterized. In the early stages of acromegaly, glomerular filtration rate (GFR) increases before sustained kidney damage (2). Renal manifestations can include kidney hypertrophy, albuminuria, glomerular hypertrophy, focal segmental glomerulosclerosis, and advanced global glomerulosclerosis (2–5). Several studies have reported a higher incidence of renal cysts and micronephrolithiasis in acromegaly patients (2–7). However, while treatment of acromegaly may acutely normalize glomerular hyperfiltration (8), long-term renal function trajectories remain poorly characterized, and recent evidence suggests that patients with acromegaly experience an accelerated chronic decline in estimated GFR that exceeds age-related loss (9). This study aimed to evaluate the prevalence and independent predictors of renal morphological abnormalities and chronic kidney disease, and to assess the frequency of urinary system malignancies, in a large cohort of patients with acromegaly followed at a single tertiary center over four decades. METHODS Study Design and Patient Selection This retrospective cohort study evaluated patients managed for acromegaly at the Department of Endocrinology and Metabolism at Hacettepe University School of Medicine between 1980 and 2023. The study protocol was approved by the institutional Non-Interventional Clinical Research Ethics Committee (Decision No: 2023/08-15, Project No: GO 23/381). Patients were eligible for inclusion if they were aged 18 years or older and had a confirmed diagnosis of acromegaly in accordance with Endocrine Society clinical practice guidelines (6). Patients were excluded if they lacked sufficient clinical, laboratory, or radiological data in the hospital's electronic medical records or physical archives to establish baseline disease characteristics. Because of the historical nature of the cohort, the evaluable denominator for specific renal outcomes varied based on the availability of long-term laboratory and imaging data. Endocrine Assessment and Biochemical Assays The clinical diagnosis of active acromegaly and subsequent disease remission were established based on standard biochemical criteria, including a lack of growth hormone (GH) suppression to less than 1 ng/mL during a 75-g oral glucose tolerance test (OGTT). Of note, this threshold was applied throughout the study period; although current guidelines recommend a more stringent cutoff of <0.4 µg/L with ultrasensitive assays, the majority of historical measurements in our cohort were obtained using older-generation immunoassays for which the 1 ng/mL criterion was the accepted diagnostic standard. Serum GH levels were measured using chemiluminescence immunoassay methods. Prior to 2008, serum IGF-1 levels were measured via radioimmunoassay; subsequently, immunoradiometric assay methods were employed. Due to the 43-year span of the cohort, specific assay platforms and manufacturers may have varied across procurement cycles. For the quantitative data evaluation and all statistical analyses in this study, absolute serum IGF-1 levels (expressed in ng/mL) were utilized directly. Of note, due to the 43-year longitudinal nature of the cohort, legacy assay sensitivities and coefficients of variation were not universally available. Assessment of Renal Morphology and Function The presence and characteristics of renal cysts were evaluated retrospectively using available radiology reports from abdominal or renal ultrasonography (USG), computed tomography (CT), or magnetic resonance imaging (MRI). When available, imaging data were directly examined to confirm cyst size, number, and laterality. Formal Bosniak classification was not systematically applied; however, all detected cysts met radiological criteria for simple cysts (thin-walled, anechoic or homogeneously hypodense, without septations, calcifications, or solid components). Chronic kidney disease (CKD) was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, which require either structural or functional kidney impairment (e.g., proteinuria/albuminuria) or a decreased glomerular filtration rate (GFR) persisting for more than three months (10). Estimated GFR (eGFR) was calculated using the 2021 CKD-EPI creatinine equation. Data Extraction Clinical and demographic data were systematically extracted from hospital electronic medical records and physical archives. The primary extracted variables included age at symptom onset, age at diagnosis, estimated disease duration (defined as the total interval from the reported onset of symptoms to the most recent follow-up visit, encompassing both active and remission phases of the disease), and specific treatment modalities (surgical history, radiotherapy, and medical therapy including somatostatin receptor ligands, dopamine agonists, and pegvisomant). We also recorded detailed pituitary adenoma characteristics (maximum diameter, suprasellar extension, and cavernous sinus invasion). Furthermore, patient records were reviewed for the presence of acromegaly-associated comorbidities at the time of the most recent follow-up, including hypertension, diabetes mellitus, hyperlipidemia, nodular goiter, obstructive sleep apnea, and the diagnosis of any benign or malignant neoplasms. Statistical Analysis All analyses were performed using R version 4.4.0 and IBM® SPSS version 27. Figures were generated using Python version 3 with the Matplotlib library. Descriptive statistics for categorical variables are presented as frequencies and percentages, whereas continuous variables are expressed as mean ± standard deviation (SD) or median (range). The normality of continuous variables was evaluated both visually (histograms and probability plots) and analytically (Kolmogorov-Smirnov/Shapiro-Wilk tests). For independent group comparisons of categorical variables, the χ² test or Fisher’s exact test was used as appropriate. When significant differences were detected in comparisons involving three or more groups, post-hoc pairwise comparisons were performed with Bonferroni correction. The Student’s t-test or Mann-Whitney U test was used for continuous variables, depending on normality. Preoperative and postoperative laboratory values were compared using paired-sample t-tests. The GH and IGF-1 levels measured preoperatively, postoperatively, and at the current time points were compared using the Friedman test. Risk factors for chronic kidney disease and renal cysts were examined using logistic regression analysis. Variables with p<0.05 in univariate analysis were considered as candidates for inclusion in multivariate models, alongside variables deemed clinically relevant a priori (age, sex, and disease duration). For each outcome, two multivariate models were constructed. Model 1 was fitted to the larger evaluable cohort (n=278 for renal cysts; n=353 for CKD) and included all candidate predictors with broadly available data. Model 2 incorporated additional variables that restricted the sample size due to limited data availability: baseline serum potassium for renal cysts (n=244) and nephrolithiasis for CKD (n=283). This dual-model approach was adopted to demonstrate robustness of core predictors across sample sizes. Multicollinearity among candidate predictors was assessed using variance inflation factors (VIF) derived from linear regression; all VIF values were below 1.7, indicating no problematic collinearity. Standardized incidence ratios (SIRs) for urinary system cancers (ICD-10 codes C64–C68, ascertained through pathology reports) were calculated using the indirect method. Person-time was calculated according to calendar year and attained age; patients entering or exiting the cohort within the same calendar year were assigned 0.5 person-years using the mid-year approximation. Expected case counts were derived from age- and sex-stratified national cancer incidence rates (converted to person-year denominators). Exact Poisson 95% confidence intervals and p values were computed for the observed-to-expected ratio. Statistical significance was defined as a two-sided p-value <0.05. RESULTS Patient Characteristics and Treatment Outcomes A total of 394 patients with acromegaly (202 men, 192 women) were evaluated. The patient flow diagram detailing evaluable denominators for each analysis is presented in Figure 1. The cohort demonstrated a mean age at diagnosis of 41.1±12.3 years and a median estimated disease duration of 17 years (range, 5–51). Pituitary macroadenomas were present in 77.1% of patients, with a median maximum tumor diameter of 15 mm. The vast majority of the cohort (96.4%) underwent pituitary surgery, with 24.3% requiring re-operation. Multimodal therapy was common; radiation therapy was administered to 21.9% of patients, and 67.9% received somatostatin receptor ligands (SRLs) for a median duration of 11 years. Across the cohort, multimodal management resulted in significant biochemical improvement, with median GH levels declining from 12.1 ng/mL at diagnosis to 0.88 ng/mL at the latest follow-up (p<0.001). Median absolute IGF-1 levels showed a parallel significant decrease from 891.6 ng/mL to 193.6 ng/mL over the same period (p<0.001) (Supplementary Figures 1 and 2). The cohort exhibited a high burden of acromegaly-associated metabolic and structural comorbidities. Beyond nodular goiter (73.6%), the most prevalent conditions included hyperlipidemia (59.4%), diabetes mellitus (44.7%), and hypertension (43.7%). Furthermore, neoplasms were identified in 37.8% of patients, with histologically confirmed malignancy present in 16.5%. Detailed baseline demographics, adenoma characteristics, treatment modalities, and laboratory changes are summarized in Tables 1–2. Baseline Renal Morphology and Primary Outcomes Detailed renal imaging and laboratory data were evaluated to assess structural and functional changes (Table 3). The mean longitudinal kidney dimensions for the cohort were 108.8±12.9 mm on the right and 111.7±13.3 mm on the left. One patient presented with a congenital horseshoe kidney. Regarding our primary endpoints, structural evaluation of 283 patients with available imaging revealed that 41% (n=116) harbored at least one renal cyst. Functional and clinical assessments demonstrated that chronic kidney disease (CKD) was present in 16.1% (57/354) of the evaluable cohort, while nephrolithiasis was detected in 15.1% (43/284). Among the 57 patients with CKD, staging based on latest follow-up eGFR revealed that the majority had preserved glomerular filtration: 7 (12.3%) were KDIGO stage G1 and 30 (52.6%) were stage G2, indicating that CKD was diagnosed on the basis of albuminuria or structural renal abnormalities rather than reduced eGFR. The remaining patients were classified as stage G3a (11 patients, 19.3%), G3b (7 patients, 12.3%), and G5 (2 patients, 3.5%, both receiving regular hemodialysis); no patients were classified as stage G4. Risk factors for renal cyst Patients who developed renal cysts were significantly older at the time of acromegaly diagnosis and exhibited a higher baseline prevalence of metabolic and structural comorbidities, including diabetes mellitus, hypertension, hyperlipidemia, nephrolithiasis, and liver cysts (Supplementary Tables 1 and 2). Notably, parameters reflecting acromegaly disease activity—including maximum pituitary adenoma diameter, historical or current somatostatin receptor ligand (SRL) usage, and cross-sectional GH or IGF-1 levels (whether at diagnosis or latest follow-up)—did not differ significantly between patients with and without renal cysts. To identify independent predictors of cyst formation, variables demonstrating significance in univariate analyses were evaluated in multivariate logistic regression models. In Model 1 (n=278), which included all patients with available imaging data, advanced age (OR: 1.049, 95% CI: 1.022–1.076; p<0.001), nephrolithiasis (OR: 2.455, 95% CI: 1.170–5.153; p=0.018), liver cysts (OR: 2.657, 95% CI: 1.291–5.467; p=0.008), and the presence of multiple neoplasms (OR: 1.915, 95% CI: 1.015–3.612; p=0.045) emerged as independent risk factors. Model 2 (n=244) additionally incorporated baseline serum potassium, which was available for a subset of patients. In this model, the same core predictors retained significance: advanced age (OR: 1.057, 95% CI: 1.028–1.088; p<0.001), nephrolithiasis (OR: 3.714, 95% CI: 1.583–8.715; p=0.003), liver cysts (OR: 3.284, 95% CI: 1.484–7.266; p=0.003), and multiple neoplasms (OR: 2.130, 95% CI: 1.058–4.289; p=0.034). Notably, baseline serum potassium demonstrated a significant inverse association with cyst risk: each 1 mEq/L increase was independently associated with a 62.7% reduction in cyst development (OR: 0.373, 95% CI: 0.171–0.815; p=0.013) (Table 4, Figure 2). Risk factors for chronic kidney disease Patients who developed CKD were significantly older, predominantly male (66.7% vs. 33.3%, p=0.007), and had a longer estimated duration of acromegaly compared to those without CKD (Supplementary Table 3). Morphologically and functionally, the CKD cohort exhibited significantly smaller right and left longitudinal kidney dimensions, alongside predictably lower estimated glomerular filtration rates (eGFR) at both diagnosis and final follow-up (Supplementary Table 4, Figure 3). The comorbidity burden was also substantially higher in the CKD cohort, particularly regarding the prevalence of diabetes mellitus, hypertension, cardiovascular disease, and nephrolithiasis (Supplementary Table 5). Consistent with our findings on renal cysts, neither historical nor current GH and IGF-1 levels, nor pituitary adenoma size or medical therapy choices, correlated with the presence of CKD. In Model 1 of the multivariate logistic regression analysis (n=353), which included all patients with available CKD data, advanced age (OR: 1.052, 95% CI: 1.015–1.089; p=0.005), male sex (OR: 3.575, 95% CI: 1.811–7.056; p<0.001), and hypertension (OR: 2.786, 95% CI: 1.316–5.901; p=0.007) emerged as independent predictors. Model 2 (n=283), which additionally incorporated nephrolithiasis data available for a subset of patients, confirmed these core predictors: advanced age (OR: 1.044, 95% CI: 1.003–1.086; p=0.033), male sex (OR: 3.134, 95% CI: 1.470–6.681; p=0.003), and hypertension (OR: 2.371, 95% CI: 1.036–5.427; p=0.041). Furthermore, nephrolithiasis emerged as an additional independent predictor of CKD (OR: 3.095, 95% CI: 1.371–6.987; p=0.007) (Table 5, Figure 4). Urinary system malignancies An evaluation of the 65 patients (16.5%) diagnosed with cancer within the cohort revealed a notable secondary finding regarding renal pathology. Urinary system malignancies (ICD-10 C64–C68)—including two patients with ureteral cancer, two patients with bladder cancer and three renal cell carcinomas— collectively represented 10.8% of all cancers and ranked second in frequency among cancer types, after thyroid cancer. Notably, all three RCC patients harbored synchronous or metachronous secondary malignancies (pancreatic, thyroid, and colorectal cancer, respectively), and one of these patients also had a coexisting horseshoe kidney anomaly. When compared to age- and sex-specific Turkish population incidence rates, the standardized incidence ratio for urinary system cancers was 7.38 (95% CI: 2.97–15.21; p<0.001), with a particularly pronounced excess in males (SIR: 8.34; p<0.001) (Table 6). DISCUSSION In this single-center retrospective analysis of 394 acromegaly patients followed over four decades, we observed a high prevalence of renal cysts (41%) and nephrolithiasis (15.1%), alongside a substantial burden of chronic kidney disease (16.1%). Notably, cross-sectional GH and IGF-1 levels at diagnosis, post-operatively, or at the latest follow-up were not associated with these renal pathologies. This pattern is consistent with the hypothesis that the renal consequences of acromegaly may be related to cumulative hormonal exposure and the synergistic effects of metabolic comorbidities rather than cross-sectional hormone measurements alone. Supporting this interpretation, Auriemma et al. reported that acromegalic patients exhibited enlarged kidney dimensions alongside increased microalbuminuria and micronephrolithiasis, and that these alterations persisted even after biochemical remission was achieved (7). Similarly, Fujio et al. demonstrated that the elevated eGFR observed in active acromegaly was significantly higher than in age- and sex-matched patients with nonfunctioning pituitary adenomas, and that surgical remission led to a significant decline in eGFR—an effect not observed in the control group—suggesting that hyperfiltration is a direct consequence of GH/IGF-1 excess rather than a nonspecific surgical artifact (8). More recently, Castagna et al. reported a mean chronic eGFR decline of − 1.28 mL/min/year in treated acromegaly patients over a mean follow-up of 11 years, a rate approximately twice that of the healthy population, with 21% developing CKD by end of follow-up (9). Renal cysts Renal cysts are common in the general population, with prevalence increasing with age and ranging from 7.7% to 27% globally (11–15). In our cohort, the prevalence was 41%, which numerically exceeds the rates reported in previous acromegaly cohorts, such as the 32.4% observed by Yamamoto et al. (16) and the 28.8% reported by Bostan et al. (17). However, this comparison should be interpreted with caution, as our cohort’s mean age (56.9 years) and high comorbidity burden, and the prevalence of simple renal cysts in the general population can approach 30–40% in individuals over 50 years of age. In the absence of an age- and comorbidity-matched control group, the degree to which acromegaly itself contributes to cyst formation beyond age-related changes cannot be definitively established. Consistent with established risk factors, advanced age and nephrolithiasis were identified as independent predictors of cyst formation (18). Interestingly, cross-sectional GH and IGF-1 levels did not correlate with the presence of renal cysts. While this aligns with the findings of Bostan et al. (17), it contrasts with Yamamoto et al. (16), who noted a correlation between nadir and basal GH levels and cyst number. The absence of a direct biochemical correlation in our study may reflect the fact that cystogenesis is a consequence of long-term, cumulative hormonal exposure that is not captured by cross-sectional measurements at isolated time points. This hypothesis is indirectly supported by the identification of multiple neoplasms and liver cysts as independent risk factors for renal cyst development. Although we were unable to directly quantify cumulative GH/IGF-1 exposure, the independent association between multiple neoplasms and renal cyst formation may indirectly reflect prolonged hormonal excess, given the well-established mitogenic effects of GH and IGF-1 on tissue proliferation. Under this interpretation, the development of multiple neoplasms would serve as a biological indicator of total hormonal burden over time. Notably, this association remained significant after adjustment for age, suggesting that it captures something beyond chronological aging alone. However, this inference should be considered speculative, as the association may also be explained by shared surveillance patterns—patients with longer follow-up and more frequent imaging are more likely to have both neoplasms and cysts detected—or by underlying genetic susceptibility to neoplasia that is independent of hormonal exposure. Experimental models of autosomal dominant polycystic kidney disease (ADPKD) have demonstrated that somatostatin receptor ligands (SRLs) reduce both kidney and liver cyst formation (19–22), implying a role for the GH/IGF-1 axis in cystogenesis. In our cohort, at least one hepatic lesion was identified in 25% of patients, with hepatic cysts present in 15.7%—a rate at the upper end of general population estimates (0.06–17.8%)(23). The co-occurrence of hepatic and renal cysts may thus reflect a shared, chronic susceptibility driven by prolonged GH and IGF-1 elevation. Nevertheless, the high prevalence of renal cysts should also be interpreted in the context of the typical age at acromegaly diagnosis (fourth to fifth decade) and the associated comorbidity burden, both of which independently predispose to cyst formation. An additional finding in this study was the independent, inverse association between baseline serum potassium levels and the risk of renal cysts. Even though mean potassium levels remained within the normal physiological range, each 1 mEq/L increase at diagnosis was associated with a 62.7% reduction in cyst risk. This observation may provide clinical context for IGF-1-mediated tubular alterations. Elevated IGF-1 is known to enhance the expression of the epithelial sodium channel (ENaC) in the distal renal tubules, promoting sodium retention and subsequent urinary potassium excretion (2). A study by Kamenicky et al. (24) supported this mechanism by demonstrating that IGF-1-mediated body fluid expansion could be inhibited by amiloride. Chronic potassium depletion has been associated with structural renal changes, including cyst formation. Although we did not observe a direct correlation between pre-treatment IGF-1 and potassium levels, the association between higher baseline potassium and reduced cyst risk is consistent with a potential mechanistic link to tubular dysfunction in acromegaly. However, this finding should be interpreted with considerable caution: baseline potassium was measured at a single time point, data on concurrent potassium-altering medications were unavailable, the proposed mechanistic chain involves multiple untested inferential steps, and lower baseline potassium may alternatively serve as a surrogate marker for more severe disease. Prospective studies with serial potassium measurements and detailed medication records are needed to clarify this association. Chronic kidney disease and nephrolithiasis Chronic kidney disease is a pervasive global health concern, with an estimated prevalence of approximately 10% (25, 26). In a large Turkish multicenter study, the population prevalence was reported at 15.7%, with a higher frequency observed in women (18.4% vs. 12.8%) (27). In our acromegaly cohort, the overall prevalence of CKD was 16.1%, which is numerically similar to the 15.7% reported in the general Turkish population. However, this comparison should be interpreted cautiously, as the CREDIT study was population-based and included a substantially younger demographic; the similar headline prevalence may therefore mask a truly elevated risk in acromegaly patients once age and comorbidity burden are accounted for. The demographic distribution also differed: 66.7% of acromegalic patients who developed CKD were male, and male sex emerged as an independent risk factor. While expected metabolic drivers such as advanced age and hypertension were also independently predictive of CKD in our cohort, the male predominance observed here represents a divergence from general population trends and may reflect the higher burden of metabolic comorbidities or differences in healthcare-seeking behavior among male patients with acromegaly. Given the high burden of comorbidities like hypertension and diabetes in acromegaly, significant renal impairment might be anticipated; indeed, recent data from Hong et al. demonstrated a 4.35-fold higher risk of progression to end-stage kidney disease in acromegaly patients compared to matched controls (28). The same research group subsequently confirmed these renal findings within a broader analysis of acromegaly-associated systemic complications using Korean nationwide data, demonstrating that the increased risk of ESKD persisted even after adjusting for diabetes and hypertension as mediators (29). Notably, while our data identified hypertension as an independent CKD predictor, Castagna et al., found that diabetes—rather than hypertension—was the dominant independent risk factor for CKD (OR: 5.66, p = 0.011) in their cohort (9). This divergence may reflect differences in cohort composition, CKD definitions, or the relative burden of metabolic comorbidities, and underscores that multiple pathways converge on renal injury in acromegaly. Beyond traditional metabolic risk factors, nephrolithiasis—detected in 15.1% of our cohort—proved to be a major independent predictor of CKD. The relationship between kidney stones and renal functional decline is well-documented in the general population (30–32). GH-enhanced calcitriol synthesis and IGF-1-promoted renal phosphorus reabsorption collectively increase intestinal mineral absorption; the resulting hypercalciuria and phosphate-driven secondary parathyroid stimulation create a highly lithogenic environment (33–36). Importantly, our multivariate analysis demonstrated that nephrolithiasis increased the risk of CKD development approximately three-fold, an effect that remained significant after adjusting for age, male sex, hypertension, and other metabolic comorbidities. Overall renal impact Collectively, our observations—spanning shifts from baseline to final follow-up in calcium, phosphorus, and parathyroid hormone, the inverse relationship between baseline potassium and cystogenesis, and the elevated prevalence of renal cysts—are consistent with the hypothesis that acromegaly may preferentially affect tubular function and renal morphology rather than causing primary glomerular failure, although the absence of a matched control group limits definitive conclusions. While the mean estimated glomerular filtration rate (eGFR) in our cohort declined from diagnosis to the final follow-up, this likely reflects the resolution of the glomerular hyperfiltration characteristically seen in active acromegaly, compounded by natural, age-related decline. Further supporting the tubular hypothesis, KDIGO staging of the 57 CKD patients revealed that nearly two-thirds (64.9%) had preserved eGFR (stages G1–G2), with CKD diagnosed on the basis of albuminuria or structural abnormalities rather than reduced glomerular filtration. The pattern of findings suggests that the long-term renal burden of GH and IGF-1 excess may be predominantly structural and tubular, whereas its impact on isolated glomerular function appears less pronounced. Cancer Prolonged elevation of GH and IGF-1 is a well-established driver of neoplastic risk in patients with acromegaly (37, 38). In our cohort, cancer was diagnosed in 16.5% of patients; while thyroid cancer was the most prevalent (49.2%), urinary system cancers (ICD-10 C64–C68) accounted for 10.8% of all malignancies, a frequency comparable to breast cancer (10.8%) and exceeding colorectal cancer (6.15%). Furthermore, several cases of renal cell carcinoma occurred synchronously or metachronously with other primary malignancies. The frequency of urinary system malignancies observed here represents a notable clinical finding that has not been widely emphasized in previous acromegaly literature. Given the recognized risk of second primary tumors in this population, these findings are particularly relevant when compared to national cancer patterns. For context, the most commonly reported cancers in Turkey between 2015 and 2020 were lung, breast, colorectal, prostate, and thyroid cancers (39); the prominence of urinary system malignancies in our acromegaly cohort is therefore disproportionate to the national pattern. To quantify this observation, we calculated standardized incidence ratios (SIRs) for urinary system cancers using age- and sex-specific Turkish population incidence rates as the reference. The overall SIR was 7.38 (95% CI: 2.97–15.21; p < 0.001), confirming a greater than seven-fold excess risk. This excess was driven predominantly by male patients (SIR: 8.34, 95% CI: 3.06–18.14; p < 0.001), whereas the female subgroup did not reach statistical significance (SIR: 4.37, 95% CI: 0.11–24.37; p = 0.409), likely reflecting limited statistical power given the small number of events. Collectively, these observations support a proactive approach to cancer screening in acromegaly (40), with routine renal and urinary tract assessment at diagnosis and periodic surveillance guided by disease activity. Limitations This study has several limitations. The retrospective, single-center design inherently limits causality assessment and introduces referral and survivorship biases. The absence of an age-, sex-, and comorbidity-matched control group is a major limitation; given the cohort's mean age and substantial comorbidity burden, prevalence comparisons with general population data may overestimate the contribution of acromegaly itself to renal pathology. A key analytical limitation is our reliance on cross-sectional GH and IGF-1 measurements rather than measures of cumulative hormonal exposure, compounded by the use of absolute IGF-1 values rather than age- and sex-adjusted upper limits of normal—a constraint imposed by the unavailability of historical assay-specific normative data. This potential misclassification of disease activity may be a primary explanation for the absence of an association between IGF-1 levels and renal outcomes, and the negative IGF-1 findings should not be interpreted as evidence that disease activity is unrelated to renal pathology. The evaluable denominators varied across outcomes due to differential data availability, particularly in patients diagnosed in earlier decades; however, the consistency of independent predictors across dual multivariate models supports the robustness of our findings. Additionally, data on concurrent potassium-altering medications were unavailable, and quantitative albuminuria data were limited to 157 patients, potentially underestimating CKD prevalence and precluding complete KDIGO risk stratification. CONCLUSION This large-scale retrospective cohort study demonstrates that renal cysts and chronic kidney disease are prevalent in patients with acromegaly. Our findings suggest that these structural and functional renal alterations may be related to cumulative hormonal exposure and associated metabolic comorbidities rather than cross-sectional hormone levels, although methodological limitations preclude definitive conclusions. The inverse association between baseline serum potassium and cyst risk, though requiring prospective validation with serial measurements and medication data, points toward tubular dysfunction as a potential contributor to renal injury, a hypothesis further supported by the predominance of preserved eGFR among CKD patients. The independent associations of male sex and nephrolithiasis with CKD, alongside the notable frequency of urinary system malignancies, reinforce the clinical importance of dedicated renal surveillance in acromegaly management. Future prospective, multicenter studies incorporating age- and sex-adjusted IGF-1 values, measures of cumulative hormonal exposure, and appropriately matched control groups are needed to confirm these observations and refine surveillance strategies. Declarations Funding: The authors did not receive support from any organization for the submitted work. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Ethics Approval: This retrospective study was approved by the Hacettepe University Non-Interventional Clinical Research Ethics Committee (Decision No: 2023/08-15, Project No: GO 23/381) and was performed in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments. Consent to Participate: Informed consent was waived due to the retrospective nature of the study, as approved by the ethics committee. Consent to Publish: Not applicable. Data Availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions: Polat Ercan: conceptualization, data collection, formal analysis, and writing — original draft. Busra Fırlatan Yazgan: data collection. Suleyman Nahit Sendur, Seda Hanife Oguz, and Selcuk Dagdelen: supervision, writing — review and editing. Tomris Erbas: conceptualization, supervision, writing — review and editing. All authors read and approved the final manuscript. References Lavrentaki A, Paluzzi A, Wass JA, Karavitaki N. Epidemiology of acromegaly: review of population studies. Pituitary. 2017;20(1):4-9. Kamenicky P, Mazziotti G, Lombes M, Giustina A, Chanson P. Growth hormone, insulin-like growth factor-1, and the kidney: pathophysiological and clinical implications. Endocr Rev. 2014;35(2):234-81. Hoogenberg K, Sluiter WJ, Dullaart RP. Effect of growth hormone and insulin-like growth factor I on urinary albumin excretion: studies in acromegaly and growth hormone deficiency. Acta Endocrinol (Copenh). 1993;129(2):151-7. Takai M, Izumino K, Oda Y, Terada Y, Inoue H, Takata M. Focal segmental glomerulosclerosis associated with acromegaly. Clin Nephrol. 2001;56(1):75-7. Yoshida H, Akikusa B, Saeki N, Hasegawa S, Iesato K, Yamamoto S, et al. Effect of pituitary microsurgery on acromegaly complicated nephrotic syndrome with focal segmental glomerulosclerosis: report of a rare clinical case. Am J Kidney Dis. 1999;33(6):1158-63. Katznelson L, Laws ER, Jr., Melmed S, Molitch ME, Murad MH, Utz A, et al. Acromegaly: an endocrine society clinical practice guideline. J Clin Endocrinol Metab. 2014;99(11):3933-51. Auriemma RS, Galdiero M, De Martino MC, De Leo M, Grasso LF, Vitale P, et al. The kidney in acromegaly: renal structure and function in patients with acromegaly during active disease and 1 year after disease remission. Eur J Endocrinol. 2010;162(6):1035-42. Fujio S, Takano K, Arimura H, Habu M, Bohara M, Hirano H, et al. Treatable glomerular hyperfiltration in patients with active acromegaly. Eur J Endocrinol. 2016;175(4):325-33. Castagna G, Ippolito S, Cassibba S, Cortesi L, Costi E, Harb A, et al. Kidney function in acromegaly: evidence from a long-term observational study. Pituitary. 2025;28(3):56. Kidney Disease: Improving Global Outcomes CKDWG. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117-S314. Terada N, Ichioka K, Matsuta Y, Okubo K, Yoshimura K, Arai Y. The natural history of simple renal cysts. J Urol. 2002;167(1):21-3. Chang CC, Kuo JY, Chan WL, Chen KK, Chang LS. Prevalence and clinical characteristics of simple renal cyst. J Chin Med Assoc. 2007;70(11):486-91. Mensel B, Kuhn JP, Kracht F, Volzke H, Lieb W, Dabers T, et al. Prevalence of renal cysts and association with risk factors in a general population: an MRI-based study. Abdom Radiol (NY). 2018;43(11):3068-74. Rule AD, Sasiwimonphan K, Lieske JC, Keddis MT, Torres VE, Vrtiska TJ. Characteristics of renal cystic and solid lesions based on contrast-enhanced computed tomography of potential kidney donors. Am J Kidney Dis. 2012;59(5):611-8. Ozveren B, Onganer E, Turkeri LN. Simple Renal Cysts: Prevalence, Associated Risk Factors and Follow-Up in a Health Screening Cohort. Urol J. 2016;13(1):2569-75. Yamamoto M, Matsumoto R, Fukuoka H, Iguchi G, Takahashi M, Nishizawa H, et al. Prevalence of Simple Renal Cysts in Acromegaly. Intern Med. 2016;55(13):1685-90. Bostan H, Kizilgul M, Calapkulu M, Kalkisim HK, Topcu FBG, Gul U, et al. The prevalence and associated risk factors of detectable renal morphological abnormalities in acromegaly. Pituitary. 2023. Ravine D, Gibson RN, Donlan J, Sheffield LJ. An ultrasound renal cyst prevalence survey: specificity data for inherited renal cystic diseases. Am J Kidney Dis. 1993;22(6):803-7. Messchendorp AL, Casteleijn NF, Meijer E, Gansevoort RT. Somatostatin in renal physiology and autosomal dominant polycystic kidney disease. Nephrol Dial Transplant. 2020;35(8):1306-16. Masyuk TV, Masyuk AI, Torres VE, Harris PC, Larusso NF. Octreotide inhibits hepatic cystogenesis in a rodent model of polycystic liver disease by reducing cholangiocyte adenosine 3',5'-cyclic monophosphate. Gastroenterology. 2007;132(3):1104-16. Caroli A, Perico N, Perna A, Antiga L, Brambilla P, Pisani A, et al. Effect of longacting somatostatin analogue on kidney and cyst growth in autosomal dominant polycystic kidney disease (ALADIN): a randomised, placebo-controlled, multicentre trial. Lancet. 2013;382(9903):1485-95. Meijer E, Visser FW, van Aerts RMM, Blijdorp CJ, Casteleijn NF, D'Agnolo HMA, et al. Effect of Lanreotide on Kidney Function in Patients With Autosomal Dominant Polycystic Kidney Disease: The DIPAK 1 Randomized Clinical Trial. JAMA. 2018;320(19):2010-9. Kaltenbach TE, Engler P, Kratzer W, Oeztuerk S, Seufferlein T, Haenle MM, et al. Prevalence of benign focal liver lesions: ultrasound investigation of 45,319 hospital patients. Abdom Radiol (NY). 2016;41(1):25-32. Kamenicky P, Blanchard A, Frank M, Salenave S, Letierce A, Azizi M, et al. Body fluid expansion in acromegaly is related to enhanced epithelial sodium channel (ENaC) activity. J Clin Endocrinol Metab. 2011;96(7):2127-35. Vart P, Heerspink HJL. Progress and opportunities in measuring the burden of Chronic Kidney Disease. Lancet Reg Health Eur. 2022;20:100447. Mills KT, Xu Y, Zhang W, Bundy JD, Chen CS, Kelly TN, et al. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int. 2015;88(5):950-7. Suleymanlar G, Utas C, Arinsoy T, Ates K, Altun B, Altiparmak MR, et al. A population-based survey of Chronic REnal Disease In Turkey--the CREDIT study. Nephrol Dial Transplant. 2011;26(6):1862-71. Hong S, Kim KS, Han K, Park CY. A cohort study found a high risk of end-stage kidney disease associated with acromegaly. Kidney Int. 2023;104(4):820-7. Hong S, Han K, Park CY. Long-Term Prognosis and Systemic Impact of Acromegaly: Analyses Utilizing Korean National Health Insurance Data. Endocrinol Metab (Seoul). 2025;40(1):1-9. Rule AD, Bergstralh EJ, Melton LJ, 3rd, Li X, Weaver AL, Lieske JC. Kidney stones and the risk for chronic kidney disease. Clin J Am Soc Nephrol. 2009;4(4):804-11. Dhondup T, Kittanamongkolchai W, Vaughan LE, Mehta RA, Chhina JK, Enders FT, et al. Risk of ESRD and Mortality in Kidney and Bladder Stone Formers. Am J Kidney Dis. 2018;72(6):790-7. Alexander RT, Hemmelgarn BR, Wiebe N, Bello A, Morgan C, Samuel S, et al. Kidney stones and kidney function loss: a cohort study. BMJ. 2012;345:e5287. Pines A, Olchovsky D. Urolithiasis in acromegaly. Urology. 1985;26(3):240-2. Heilberg IP, Czepielewski MA, Ajzen H, Ramos OL, Schor N. Metabolic factors for urolithiasis in acromegalic patients. Braz J Med Biol Res. 1991;24(7):687-96. Parkinson C, Kassem M, Heickendorff L, Flyvbjerg A, Trainer PJ. Pegvisomant-induced serum insulin-like growth factor-I normalization in patients with acromegaly returns elevated markers of bone turnover to normal. J Clin Endocrinol Metab. 2003;88(12):5650-5. Kamenicky P, Blanchard A, Gauci C, Salenave S, Letierce A, Lombes M, et al. Pathophysiology of renal calcium handling in acromegaly: what lies behind hypercalciuria? J Clin Endocrinol Metab. 2012;97(6):2124-33. Terzolo M, Reimondo G, Berchialla P, Ferrante E, Malchiodi E, De Marinis L, et al. Acromegaly is associated with increased cancer risk: a survey in Italy. Endocr Relat Cancer. 2017;24(9):495-504. Dagdelen S, Cinar N, Erbas T. Increased thyroid cancer risk in acromegaly. Pituitary. 2014;17(4):299-306. Ervik M LF, Laversanne M, Ferlay J, Bray F. Global Cancer Observatory: Cancer Over Time. https://gco.iarc.fr/2021 Oguz SH, Firlatan B, Sendur SN, Dagdelen S, Erbas T. Follow, consider, and catch: second primary tumors in acromegaly patients. Endocrine. 2023;80(1):160-73. Tables Table 1. Baseline demographic and clinical characteristics of patients (n=394) Frequency (%) Age, years (mean ±SD) 56.9±12.5 (27-89) Male / Female 202 (51.3) / 192 (48.7) Onset age of symptoms, year (mean±SD) 37.8±12.3 (16-69) Diagnosis age, year (mean±SD) 41.1±12.3 (16-74) Duration of symptomatic period before diagnosis, years (median (min-max)) 2 (0-35) Estimated duration of the disease, years (median (min-max)) 17 (5-51) Smoking status, (n=259) 103 (39.8) Alcohol use, (n=256) 33 (12.9) BMI, kg/m 2 , (mean±SD) (n=257) Normal weight (18.5-24.9 kg/m 2 ) Overweight (25-29.9 kg/m 2 ) Obese (30-34.9 kg/m 2 ) Severely obese (35-39.9 kg/m 2 ) Morbidly obese (≥40 kg/m 2 ) 30±5.8 44 (17.1) 105 (40.9) 72 (28) 18 (7) 18 (7) SD: standard deviation, BMI: body mass index. Table 2. Comparison of laboratory findings at diagnosis and at the last follow-up Parameters, mean±SD At diagnosis Last follow-up p value Hemoglobin, g/dL 13±1.6 13.3±1.5 0.001 Fasting plasma glucose, mg/dL 111±42.6 104.7±28 0.002 HbA1C, % 6.18±1.75 6.19±1.01 0.937 Blood urea nitrogen, mg/dL 13.4±5.1 15.3±6.4 < 0.001 Creatinine, mg/dL 0.7±0.2 0.81±0.4 < 0.001 eGFR, mL/min/1.73m 2 100.6±16.3 92.4±19.2 <0.001 Sodium, mEq/L 140.4±3.2 140.8±3 0.097 Potassium, mEq/L 4.32±0.4 4.35±0.4 0.272 Protein, g/dL 7.2±0.5 7.2±0.6 0.648 Albumin, g/dL 4.3±0.4 4.3±0.4 0.426 Uric acid, mg/dL 4.8±1.3 5.1±1.3 < 0.001 Calcium, mg/dL 9.63±0.5 9.56±0.5 0.022 Phosphorus, mg/dL 4.3±0.7 3.8±0.6 < 0.001 Cholecalciferol, (min-max), ng/mL 16.4 (1.3-74.5) 19 (5-54) 0.214 Parathyroid hormone, (min-max), pg/mL 42.9 (8.9-267) 52.8 (3.0-930.9) < 0.001 Urine albumin/creatinine ratio, (min-max), mg/g 12.7 (0.2-1030.9) 10 (0.0-2048.0) 0.280 SD: standard deviation, eGFR: estimated glomerular filtration rate, *according to the CKD-EPI 2021 formula Table 3. Evaluation of kidney pathologies in the study cohort (n=394 patients) Frequency (%) Chronic kidney disease, n=354 57 (16.1) Nephrolithiasis, n=284 43 (15.1) Renal cyst, n=283 116 (41) Cyst localization, n=116 Right Left Bilateral 26 (22.4) 35 (30.2) 55 (47.4) Number of renal cysts, median (min-max) Right, n=81 Left, n=90 2 (1-25) 2 (1-25) Largest diameter of the cyst, median (min-max), mm Right, n=81 Left, n=90 14 (2-90) 15 (2-85) Right kidney longitudinal length, mean±SD, mm, n=222 108.81±12.94 Right kidney transverse length, mean±SD, mm, n=222 52.89±7.05 Left kidney longitudinal length, mean±SD, mm, n=222 111.72±13.26 Left kidney transverse length, mean±SD, mm, n=222 57.85±8.04 SD: Standard deviation Table 4. Multivariate logistic regression analysis for renal cyst risk factors in acromegaly Risk factors Univariate analysis Multivariate Analysis, Model 1, n=278 Multivariate Analysis, Model 2, n=244 OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value Age 1.055 (1.032-1.078) <0.001 1.049 (1.022-1.076) <0.001 1.057 (1.028-1.088) <0.001 Male gender 1.504 (0.932-2.426) 0.094 - - - - Diagnosis age 1.043 (1.021-1.065) <0.001 - - - - Hyperlipidemia 1.685 (1.014-2.802) 0.044 1.025 (0.573-1.836) 0.933 0.902 (0.469-1.736) 0.758 Diabetes mellitus 1.697 (1.052-2.739) 0.030 1.192 (0.684-2.080) 0.536 1.547 (0.825-2.902) 0.174 Hypertension 1.774 (1.099-2.864) 0.019 0.972 (0.546-1.729) 0.922 0.664 (0.344-1.279) 0.221 Nephrolithiasis 2.726 (1.388-5.356) 0.004 2.455 (1.170-5.153) 0.018 3.714 (1.583-8.715) 0.003 Liver cysts 3.012 (1.542-5.881) 0.001 2.657 (1.291-5.467) 0.008 3.284 (1.484-7.266) 0.003 Left ventricular hypertrophy 1.815 (0.996-3.305) 0.051 - - - - Coronary artery disease 1.740 (0.986-3.068) 0.056 - - - - Multiple neoplasms (>1) 2.471 (1.371-4.452) 0.003 1.915 (1.015-3.612) 0.045 2.130 (1.058-4.289) 0.034 K + at diagnosis 0.510 (0.265-0.980) 0.043 - - 0.373 (0.171-0.815) 0.013 Model 1 includes all patients with available imaging data. Model 2 additionally incorporates baseline serum potassium, restricting the analysis to patients with available data for this variable. Hosmer-Lemeshow goodness-of-fit: Model 1 χ²=7.252, df=8, p=0.510; Model 2 χ²=3.953, df=8, p=0.861. Nagelkerke R²: Model 1=0.207; Model 2=0.279. CI: Confidence Interval, OR: Odds Ratio, K: Potassium Table 5. Multivariate logistic regression analysis for CKD risk factors in acromegaly Risk factors Univariate analysis Multivariate analysis, Model 1, n=353 Multivariate analysis, Model 2, n=283 OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value Age 1.075 (1.045-1.105) <0.001 1.052 (1.015-1.089) 0.005 1.044 (1.003-1.086) 0.033 Male gender 2.243 (1.236-4.070) 0.008 3.575 (1.811-7.056) <0.001 3.134 (1.470-6.681) 0.003 Diagnosis age 1.040 (1.016-1.065) 0.001 - - - - Estimated duration of the disease 1.048 (1.015-1.081) 0.004 1.018 (0.982-1.055) 0.338 1.025 (0.985-1.067) 0.226 Nephrolithiasis 4.286 (2.092-8.782) <0.001 - - 3.095 (1.371-6.987) 0.007 Hyperlipidemia 2.460 (1.271-4.760) 0.008 1.583 (0.747-3.355) 0.231 1.523 (0.654-3.549) 0.329 Diabetes mellitus 2.529 (1.407-4.543) 0.002 1.205 (0.613-2.369) 0.588 1.469 (0.694-3.112) 0.315 Hypertension 4.369 (2.318-8.236) <0.001 2.786 (1.316-5.901) 0.007 2.371 (1.036-5.427) 0.041 Left ventricular hypertrophy 3.212 (1.656-6.231) <0.001 - - - - Coronary artery disease 3.103 (1.666-5.779) <0.001 1.101 (0.528-2.296) 0.797 1.052 (0.471-2.350) 0.903 Neoplasm 2.407 (1.349-4.293) 0.003 1.800 (0.944-3.432) 0.074 2.013 (0.956-4.237) 0.065 Cancer 1.975 (1.027-3.798) 0.041 - - - - Model 1 includes all patients with available CKD data. Model 2 additionally incorporates nephrolithiasis, restricting the analysis to patients with available data for this variable. Hosmer-Lemeshow goodness-of-fit: Model 1 χ²=3.948, df=8, p=0.862; Model 2 χ²=11.907, df=8, p=0.155. Nagelkerke R²: Model 1=0.268; Model 2=0.301. CI: Confidence Interval, OR: Odds Ratio. Table 6. Standardized incidence ratios for urinary system cancers in the acromegaly cohort (Poisson-based analysis) Group N PY Obs Exp SIR 95% CI p Total 393 3967.0 7 0.95 7.38 2.97–15.21 <0.001 Female 193 2035.5 1 0.23 4.37 0.11–24.37 0.409 Male 200 1931.5 6 0.72 8.34 3.06–18.14 <0.001 Male/Female SIR ratio – – – – 1.91 0.23–15.85 0.550 N: number of patients; PY: person-years; Obs: observed cases; Exp: expected cases; SIR: standardized incidence ratio; CI: confidence interval. Expected cases were calculated using age- and sex-specific Turkish population cancer incidence data as the reference. Additional Declarations No competing interests reported. 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Its total prevalence ranges from 2.8 to 13.7 cases per 100 000 persons, with an annual incidence of 0.2 to 1.1 per 100 000 (1). In 95% of the patients, acromegaly is caused by pituitary somatotroph adenomas. Without appropriate treatment, acromegaly leads to increased morbidity and mortality owing to elevated levels of GH and IGF-1. While the cardiovascular, respiratory, neoplastic and metabolic consequences of prolonged GH/IGF-1 excess are extensively documented, the long-term impact on kidney morphology and function remains poorly characterized.\u003c/p\u003e \u003cp\u003eIn the early stages of acromegaly, glomerular filtration rate (GFR) increases before sustained kidney damage (2). Renal manifestations can include kidney hypertrophy, albuminuria, glomerular hypertrophy, focal segmental glomerulosclerosis, and advanced global glomerulosclerosis (2\u0026ndash;5). Several studies have reported a higher incidence of renal cysts and micronephrolithiasis in acromegaly patients (2\u0026ndash;7). However, while treatment of acromegaly may acutely normalize glomerular hyperfiltration (8), long-term renal function trajectories remain poorly characterized, and recent evidence suggests that patients with acromegaly experience an accelerated chronic decline in estimated GFR that exceeds age-related loss (9).\u003c/p\u003e \u003cp\u003e This study aimed to evaluate the prevalence and independent predictors of renal morphological abnormalities and chronic kidney disease, and to assess the frequency of urinary system malignancies, in a large cohort of patients with acromegaly followed at a single tertiary center over four decades.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Patient Selection\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study evaluated patients managed for acromegaly at the Department of Endocrinology and Metabolism at Hacettepe University School of Medicine between 1980 and 2023. The study protocol was approved by the institutional Non-Interventional Clinical Research Ethics Committee (Decision No: 2023/08-15, Project No: GO 23/381).\u003c/p\u003e\n\u003cp\u003ePatients were eligible for inclusion if they were aged 18 years or older and had a confirmed diagnosis of acromegaly in accordance with Endocrine Society clinical practice guidelines (6). Patients were excluded if they lacked sufficient clinical, laboratory, or radiological data in the hospital\u0026apos;s electronic medical records or physical archives to establish baseline disease characteristics. Because of the historical nature of the cohort, the evaluable denominator for specific renal outcomes varied based on the availability of long-term laboratory and imaging data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndocrine Assessment and Biochemical Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical diagnosis of active acromegaly and subsequent disease remission were established based on standard biochemical criteria, including a lack of growth hormone (GH) suppression to less than 1 ng/mL during a 75-g oral glucose tolerance test (OGTT). Of note, this threshold was applied throughout the study period; although current guidelines recommend a more stringent cutoff of \u0026lt;0.4 \u0026micro;g/L with ultrasensitive assays, the majority of historical measurements in our cohort were obtained using older-generation immunoassays for which the 1 ng/mL criterion was the accepted diagnostic standard.\u003c/p\u003e\n\u003cp\u003eSerum GH levels were measured using chemiluminescence immunoassay methods. Prior to 2008, serum IGF-1 levels were measured via radioimmunoassay; subsequently, immunoradiometric assay methods were employed. Due to the 43-year span of the cohort, specific assay platforms and manufacturers may have varied across procurement cycles.\u003c/p\u003e\n\u003cp\u003eFor the quantitative data evaluation and all statistical analyses in this study, absolute serum IGF-1 levels (expressed in ng/mL) were utilized directly. Of note, due to the 43-year longitudinal nature of the cohort, legacy assay sensitivities and coefficients of variation were not universally available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Renal Morphology and Function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe presence and characteristics of renal cysts were evaluated retrospectively using available radiology reports from abdominal or renal ultrasonography (USG), computed tomography (CT), or magnetic resonance imaging (MRI). When available, imaging data were directly examined to confirm cyst size, number, and laterality. Formal Bosniak classification was not systematically applied; however, all detected cysts met radiological criteria for simple cysts (thin-walled, anechoic or homogeneously hypodense, without septations, calcifications, or solid components).\u003c/p\u003e\n\u003cp\u003eChronic kidney disease (CKD) was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, which require either structural or functional kidney impairment (e.g., proteinuria/albuminuria) or a decreased glomerular filtration rate (GFR) persisting for more than three months (10). Estimated GFR (eGFR) was calculated using the 2021 CKD-EPI creatinine equation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and demographic data were systematically extracted from hospital electronic medical records and physical archives. The primary extracted variables included age at symptom onset, age at diagnosis, estimated disease duration (defined as the total interval from the reported onset of symptoms to the most recent follow-up visit, encompassing both active and remission phases of the disease), and specific treatment modalities (surgical history, radiotherapy, and medical therapy including somatostatin receptor ligands, dopamine agonists, and pegvisomant). We also recorded detailed pituitary adenoma characteristics (maximum diameter, suprasellar extension, and cavernous sinus invasion).\u003c/p\u003e\n\u003cp\u003eFurthermore, patient records were reviewed for the presence of acromegaly-associated comorbidities at the time of the most recent follow-up, including hypertension, diabetes mellitus, hyperlipidemia, nodular goiter, obstructive sleep apnea, and the diagnosis of any benign or malignant neoplasms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R version 4.4.0 and IBM\u0026reg; SPSS version 27. Figures were generated using Python version 3 with the Matplotlib library. Descriptive statistics for categorical variables are presented as frequencies and percentages, whereas continuous variables are expressed as mean \u0026plusmn; standard deviation (SD) or median (range). The normality of continuous variables was evaluated both visually (histograms and probability plots) and analytically (Kolmogorov-Smirnov/Shapiro-Wilk tests).\u003c/p\u003e\n\u003cp\u003eFor independent group comparisons of categorical variables, the \u0026chi;\u0026sup2; test or Fisher\u0026rsquo;s exact test was used as appropriate. When significant differences were detected in comparisons involving three or more groups, post-hoc pairwise comparisons were performed with Bonferroni correction. The Student\u0026rsquo;s t-test or Mann-Whitney U test was used for continuous variables, depending on normality. Preoperative and postoperative laboratory values were compared using paired-sample t-tests. The GH and IGF-1 levels measured preoperatively, postoperatively, and at the current time points were compared using the Friedman test.\u003c/p\u003e\n\u003cp\u003eRisk factors for chronic kidney disease and renal cysts were examined using logistic regression analysis. Variables with p\u0026lt;0.05 in univariate analysis were considered as candidates for inclusion in multivariate models, alongside variables deemed clinically relevant a priori (age, sex, and disease duration). For each outcome, two multivariate models were constructed. Model 1 was fitted to the larger evaluable cohort (n=278 for renal cysts; n=353 for CKD) and included all candidate predictors with broadly available data. Model 2 incorporated additional variables that restricted the sample size due to limited data availability: baseline serum potassium for renal cysts (n=244) and nephrolithiasis for CKD (n=283). This dual-model approach was adopted to demonstrate robustness of core predictors across sample sizes. Multicollinearity among candidate predictors was assessed using variance inflation factors (VIF) derived from linear regression; all VIF values were below 1.7, indicating no problematic collinearity. Standardized incidence ratios (SIRs) for urinary system cancers (ICD-10 codes C64\u0026ndash;C68, ascertained through pathology reports) were calculated using the indirect method. Person-time was calculated according to calendar year and attained age; patients entering or exiting the cohort within the same calendar year were assigned 0.5 person-years using the mid-year approximation. Expected case counts were derived from age- and sex-stratified national cancer incidence rates (converted to person-year denominators). Exact Poisson 95% confidence intervals and p values were computed for the observed-to-expected ratio. Statistical significance was defined as a two-sided p-value \u0026lt;0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics and Treatment Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 394 patients with acromegaly (202 men, 192 women) were evaluated. The patient flow diagram detailing evaluable denominators for each analysis is presented in Figure 1. The cohort demonstrated a mean age at diagnosis of 41.1\u0026plusmn;12.3 years and a median estimated disease duration of 17 years (range, 5\u0026ndash;51). Pituitary macroadenomas were present in 77.1% of patients, with a median maximum tumor diameter of 15 mm.\u003c/p\u003e\n\u003cp\u003eThe vast majority of the cohort (96.4%) underwent pituitary surgery, with 24.3% requiring re-operation. Multimodal therapy was common; radiation therapy was administered to 21.9% of patients, and 67.9% received somatostatin receptor ligands (SRLs) for a median duration of 11 years. Across the cohort, multimodal management resulted in significant biochemical improvement, with median GH levels declining from 12.1 ng/mL at diagnosis to 0.88 ng/mL at the latest follow-up (p\u0026lt;0.001). Median absolute IGF-1 levels showed a parallel significant decrease from 891.6 ng/mL to 193.6 ng/mL over the same period (p\u0026lt;0.001) (Supplementary Figures 1 and 2).\u003c/p\u003e\n\u003cp\u003eThe cohort exhibited a high burden of acromegaly-associated metabolic and structural comorbidities. Beyond nodular goiter (73.6%), the most prevalent conditions included hyperlipidemia (59.4%), diabetes mellitus (44.7%), and hypertension (43.7%). Furthermore, neoplasms were identified in 37.8% of patients, with histologically confirmed malignancy present in 16.5%. Detailed baseline demographics, adenoma characteristics, treatment modalities, and laboratory changes are summarized in Tables 1\u0026ndash;2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Renal Morphology and Primary Outcomes\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDetailed renal imaging and laboratory data were evaluated to assess structural and functional changes (Table 3). The mean longitudinal kidney dimensions for the cohort were 108.8\u0026plusmn;12.9 mm on the right and 111.7\u0026plusmn;13.3 mm on the left. One patient presented with a congenital horseshoe kidney.\u003c/p\u003e\n\u003cp\u003eRegarding our primary endpoints, structural evaluation of 283 patients with available imaging revealed that 41% (n=116) harbored at least one renal cyst. Functional and clinical assessments demonstrated that chronic kidney disease (CKD) was present in 16.1% (57/354) of the evaluable cohort, while nephrolithiasis was detected in 15.1% (43/284). Among the 57 patients with CKD, staging based on latest follow-up eGFR revealed that the majority had preserved glomerular filtration: 7 (12.3%) were KDIGO stage G1 and 30 (52.6%) were stage G2, indicating that CKD was diagnosed on the basis of albuminuria or structural renal abnormalities rather than reduced eGFR. The remaining patients were classified as stage G3a (11 patients, 19.3%), G3b (7 patients, 12.3%), and G5 (2 patients, 3.5%, both receiving regular hemodialysis); no patients were classified as stage G4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors for renal cyst\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc155033309\"\u003ePatients who developed renal cysts were significantly older at the time of acromegaly diagnosis and exhibited a higher baseline prevalence of metabolic and structural comorbidities, including diabetes mellitus, hypertension, hyperlipidemia, nephrolithiasis, and liver cysts (Supplementary Tables 1 and 2). Notably, parameters reflecting acromegaly disease activity\u0026mdash;including maximum pituitary adenoma diameter, historical or current somatostatin receptor ligand (SRL) usage, and cross-sectional GH or IGF-1 levels (whether at diagnosis or latest follow-up)\u0026mdash;did not differ significantly between patients with and without renal cysts.\u003c/p\u003e\n\u003cp\u003eTo identify independent predictors of cyst formation, variables demonstrating significance in univariate analyses were evaluated in multivariate logistic regression models. In Model 1 (n=278), which included all patients with available imaging data, advanced age (OR: 1.049, 95% CI: 1.022\u0026ndash;1.076; p\u0026lt;0.001), nephrolithiasis (OR: 2.455, 95% CI: 1.170\u0026ndash;5.153; p=0.018), liver cysts (OR: 2.657, 95% CI: 1.291\u0026ndash;5.467; p=0.008), and the presence of multiple neoplasms (OR: 1.915, 95% CI: 1.015\u0026ndash;3.612; p=0.045) emerged as independent risk factors. Model 2 (n=244) additionally incorporated baseline serum potassium, which was available for a subset of patients. In this model, the same core predictors retained significance: advanced age (OR: 1.057, 95% CI: 1.028\u0026ndash;1.088; p\u0026lt;0.001), nephrolithiasis (OR: 3.714, 95% CI: 1.583\u0026ndash;8.715; p=0.003), liver cysts (OR: 3.284, 95% CI: 1.484\u0026ndash;7.266; p=0.003), and multiple neoplasms (OR: 2.130, 95% CI: 1.058\u0026ndash;4.289; p=0.034). Notably, baseline serum potassium demonstrated a significant inverse association with cyst risk: each 1 mEq/L increase was independently associated with a 62.7% reduction in cyst development (OR: 0.373, 95% CI: 0.171\u0026ndash;0.815; p=0.013) (Table 4, Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors for chronic kidney disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients who developed CKD were significantly older, predominantly male (66.7% vs. 33.3%, p=0.007), and had a longer estimated duration of acromegaly compared to those without CKD (Supplementary Table 3). Morphologically and functionally, the CKD cohort exhibited significantly smaller right and left longitudinal kidney dimensions, alongside predictably lower estimated glomerular filtration rates (eGFR) at both diagnosis and final follow-up (Supplementary Table 4, Figure 3). The comorbidity burden was also substantially higher in the CKD cohort, particularly regarding the prevalence of diabetes mellitus, hypertension, cardiovascular disease, and nephrolithiasis (Supplementary Table 5). Consistent with our findings on renal cysts, neither historical nor current GH and IGF-1 levels, nor pituitary adenoma size or medical therapy choices, correlated with the presence of CKD.\u003c/p\u003e\n\u003cp\u003eIn Model 1 of the multivariate logistic regression analysis (n=353), which included all patients with available CKD data, advanced age (OR: 1.052, 95% CI: 1.015\u0026ndash;1.089; p=0.005), male sex (OR: 3.575, 95% CI: 1.811\u0026ndash;7.056; p\u0026lt;0.001), and hypertension (OR: 2.786, 95% CI: 1.316\u0026ndash;5.901; p=0.007) emerged as independent predictors. Model 2 (n=283), which additionally incorporated nephrolithiasis data available for a subset of patients, confirmed these core predictors: advanced age (OR: 1.044, 95% CI: 1.003\u0026ndash;1.086; p=0.033), male sex (OR: 3.134, 95% CI: 1.470\u0026ndash;6.681; p=0.003), and hypertension (OR: 2.371, 95% CI: 1.036\u0026ndash;5.427; p=0.041). Furthermore, nephrolithiasis emerged as an additional independent predictor of CKD (OR: 3.095, 95% CI: 1.371\u0026ndash;6.987; p=0.007) (Table 5, Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrinary system malignancies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn evaluation of the 65 patients (16.5%) diagnosed with cancer within the cohort revealed a notable secondary finding regarding renal pathology. Urinary system malignancies (ICD-10 C64\u0026ndash;C68)\u0026mdash;including two patients with ureteral cancer, two patients with bladder cancer and three renal cell carcinomas\u0026mdash; collectively represented 10.8% of all cancers and ranked second in frequency among cancer types, after thyroid cancer. Notably, all three RCC patients harbored synchronous or metachronous secondary malignancies (pancreatic, thyroid, and colorectal cancer, respectively), and one of these patients also had a coexisting horseshoe kidney anomaly. When compared to age- and sex-specific Turkish population incidence rates, the standardized incidence ratio for urinary system cancers was 7.38 (95% CI: 2.97\u0026ndash;15.21; p\u0026lt;0.001), with a particularly pronounced excess in males (SIR: 8.34; p\u0026lt;0.001) (Table 6).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this single-center retrospective analysis of 394 acromegaly patients followed over four decades, we observed a high prevalence of renal cysts (41%) and nephrolithiasis (15.1%), alongside a substantial burden of chronic kidney disease (16.1%). Notably, cross-sectional GH and IGF-1 levels at diagnosis, post-operatively, or at the latest follow-up were not associated with these renal pathologies. This pattern is consistent with the hypothesis that the renal consequences of acromegaly may be related to cumulative hormonal exposure and the synergistic effects of metabolic comorbidities rather than cross-sectional hormone measurements alone. Supporting this interpretation, Auriemma et al. reported that acromegalic patients exhibited enlarged kidney dimensions alongside increased microalbuminuria and micronephrolithiasis, and that these alterations persisted even after biochemical remission was achieved (7). Similarly, Fujio et al. demonstrated that the elevated eGFR observed in active acromegaly was significantly higher than in age- and sex-matched patients with nonfunctioning pituitary adenomas, and that surgical remission led to a significant decline in eGFR\u0026mdash;an effect not observed in the control group\u0026mdash;suggesting that hyperfiltration is a direct consequence of GH/IGF-1 excess rather than a nonspecific surgical artifact (8). More recently, Castagna et al. reported a mean chronic eGFR decline of \u0026minus;\u0026thinsp;1.28 mL/min/year in treated acromegaly patients over a mean follow-up of 11 years, a rate approximately twice that of the healthy population, with 21% developing CKD by end of follow-up (9).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRenal cysts\u003c/h2\u003e \u003cp\u003eRenal cysts are common in the general population, with prevalence increasing with age and ranging from 7.7% to 27% globally (11\u0026ndash;15). In our cohort, the prevalence was 41%, which numerically exceeds the rates reported in previous acromegaly cohorts, such as the 32.4% observed by Yamamoto et al. (16) and the 28.8% reported by Bostan et al. (17). However, this comparison should be interpreted with caution, as our cohort\u0026rsquo;s mean age (56.9 years) and high comorbidity burden, and the prevalence of simple renal cysts in the general population can approach 30\u0026ndash;40% in individuals over 50 years of age. In the absence of an age- and comorbidity-matched control group, the degree to which acromegaly itself contributes to cyst formation beyond age-related changes cannot be definitively established. Consistent with established risk factors, advanced age and nephrolithiasis were identified as independent predictors of cyst formation (18).\u003c/p\u003e \u003cp\u003eInterestingly, cross-sectional GH and IGF-1 levels did not correlate with the presence of renal cysts. While this aligns with the findings of Bostan et al. (17), it contrasts with Yamamoto et al. (16), who noted a correlation between nadir and basal GH levels and cyst number. The absence of a direct biochemical correlation in our study may reflect the fact that cystogenesis is a consequence of long-term, cumulative hormonal exposure that is not captured by cross-sectional measurements at isolated time points. This hypothesis is indirectly supported by the identification of multiple neoplasms and liver cysts as independent risk factors for renal cyst development. Although we were unable to directly quantify cumulative GH/IGF-1 exposure, the independent association between multiple neoplasms and renal cyst formation may indirectly reflect prolonged hormonal excess, given the well-established mitogenic effects of GH and IGF-1 on tissue proliferation. Under this interpretation, the development of multiple neoplasms would serve as a biological indicator of total hormonal burden over time. Notably, this association remained significant after adjustment for age, suggesting that it captures something beyond chronological aging alone. However, this inference should be considered speculative, as the association may also be explained by shared surveillance patterns\u0026mdash;patients with longer follow-up and more frequent imaging are more likely to have both neoplasms and cysts detected\u0026mdash;or by underlying genetic susceptibility to neoplasia that is independent of hormonal exposure.\u003c/p\u003e \u003cp\u003eExperimental models of autosomal dominant polycystic kidney disease (ADPKD) have demonstrated that somatostatin receptor ligands (SRLs) reduce both kidney and liver cyst formation (19\u0026ndash;22), implying a role for the GH/IGF-1 axis in cystogenesis. In our cohort, at least one hepatic lesion was identified in 25% of patients, with hepatic cysts present in 15.7%\u0026mdash;a rate at the upper end of general population estimates (0.06\u0026ndash;17.8%)(23). The co-occurrence of hepatic and renal cysts may thus reflect a shared, chronic susceptibility driven by prolonged GH and IGF-1 elevation. Nevertheless, the high prevalence of renal cysts should also be interpreted in the context of the typical age at acromegaly diagnosis (fourth to fifth decade) and the associated comorbidity burden, both of which independently predispose to cyst formation.\u003c/p\u003e \u003cp\u003eAn additional finding in this study was the independent, inverse association between baseline serum potassium levels and the risk of renal cysts. Even though mean potassium levels remained within the normal physiological range, each 1 mEq/L increase at diagnosis was associated with a 62.7% reduction in cyst risk. This observation may provide clinical context for IGF-1-mediated tubular alterations. Elevated IGF-1 is known to enhance the expression of the epithelial sodium channel (ENaC) in the distal renal tubules, promoting sodium retention and subsequent urinary potassium excretion (2). A study by Kamenicky et al. (24) supported this mechanism by demonstrating that IGF-1-mediated body fluid expansion could be inhibited by amiloride. Chronic potassium depletion has been associated with structural renal changes, including cyst formation. Although we did not observe a direct correlation between pre-treatment IGF-1 and potassium levels, the association between higher baseline potassium and reduced cyst risk is consistent with a potential mechanistic link to tubular dysfunction in acromegaly. However, this finding should be interpreted with considerable caution: baseline potassium was measured at a single time point, data on concurrent potassium-altering medications were unavailable, the proposed mechanistic chain involves multiple untested inferential steps, and lower baseline potassium may alternatively serve as a surrogate marker for more severe disease. Prospective studies with serial potassium measurements and detailed medication records are needed to clarify this association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eChronic kidney disease and nephrolithiasis\u003c/h2\u003e \u003cp\u003eChronic kidney disease is a pervasive global health concern, with an estimated prevalence of approximately 10% (25, 26). In a large Turkish multicenter study, the population prevalence was reported at 15.7%, with a higher frequency observed in women (18.4% vs. 12.8%) (27). In our acromegaly cohort, the overall prevalence of CKD was 16.1%, which is numerically similar to the 15.7% reported in the general Turkish population. However, this comparison should be interpreted cautiously, as the CREDIT study was population-based and included a substantially younger demographic; the similar headline prevalence may therefore mask a truly elevated risk in acromegaly patients once age and comorbidity burden are accounted for. The demographic distribution also differed: 66.7% of acromegalic patients who developed CKD were male, and male sex emerged as an independent risk factor. While expected metabolic drivers such as advanced age and hypertension were also independently predictive of CKD in our cohort, the male predominance observed here represents a divergence from general population trends and may reflect the higher burden of metabolic comorbidities or differences in healthcare-seeking behavior among male patients with acromegaly. Given the high burden of comorbidities like hypertension and diabetes in acromegaly, significant renal impairment might be anticipated; indeed, recent data from Hong et al. demonstrated a 4.35-fold higher risk of progression to end-stage kidney disease in acromegaly patients compared to matched controls (28). The same research group subsequently confirmed these renal findings within a broader analysis of acromegaly-associated systemic complications using Korean nationwide data, demonstrating that the increased risk of ESKD persisted even after adjusting for diabetes and hypertension as mediators (29). Notably, while our data identified hypertension as an independent CKD predictor, Castagna et al., found that diabetes\u0026mdash;rather than hypertension\u0026mdash;was the dominant independent risk factor for CKD (OR: 5.66, p\u0026thinsp;=\u0026thinsp;0.011) in their cohort (9). This divergence may reflect differences in cohort composition, CKD definitions, or the relative burden of metabolic comorbidities, and underscores that multiple pathways converge on renal injury in acromegaly.\u003c/p\u003e \u003cp\u003eBeyond traditional metabolic risk factors, nephrolithiasis\u0026mdash;detected in 15.1% of our cohort\u0026mdash;proved to be a major independent predictor of CKD. The relationship between kidney stones and renal functional decline is well-documented in the general population (30\u0026ndash;32). GH-enhanced calcitriol synthesis and IGF-1-promoted renal phosphorus reabsorption collectively increase intestinal mineral absorption; the resulting hypercalciuria and phosphate-driven secondary parathyroid stimulation create a highly lithogenic environment (33\u0026ndash;36). Importantly, our multivariate analysis demonstrated that nephrolithiasis increased the risk of CKD development approximately three-fold, an effect that remained significant after adjusting for age, male sex, hypertension, and other metabolic comorbidities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eOverall renal impact\u003c/h2\u003e \u003cp\u003eCollectively, our observations\u0026mdash;spanning shifts from baseline to final follow-up in calcium, phosphorus, and parathyroid hormone, the inverse relationship between baseline potassium and cystogenesis, and the elevated prevalence of renal cysts\u0026mdash;are consistent with the hypothesis that acromegaly may preferentially affect tubular function and renal morphology rather than causing primary glomerular failure, although the absence of a matched control group limits definitive conclusions. While the mean estimated glomerular filtration rate (eGFR) in our cohort declined from diagnosis to the final follow-up, this likely reflects the resolution of the glomerular hyperfiltration characteristically seen in active acromegaly, compounded by natural, age-related decline. Further supporting the tubular hypothesis, KDIGO staging of the 57 CKD patients revealed that nearly two-thirds (64.9%) had preserved eGFR (stages G1\u0026ndash;G2), with CKD diagnosed on the basis of albuminuria or structural abnormalities rather than reduced glomerular filtration. The pattern of findings suggests that the long-term renal burden of GH and IGF-1 excess may be predominantly structural and tubular, whereas its impact on isolated glomerular function appears less pronounced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCancer\u003c/h2\u003e \u003cp\u003eProlonged elevation of GH and IGF-1 is a well-established driver of neoplastic risk in patients with acromegaly (37, 38). In our cohort, cancer was diagnosed in 16.5% of patients; while thyroid cancer was the most prevalent (49.2%), urinary system cancers (ICD-10 C64\u0026ndash;C68) accounted for 10.8% of all malignancies, a frequency comparable to breast cancer (10.8%) and exceeding colorectal cancer (6.15%). Furthermore, several cases of renal cell carcinoma occurred synchronously or metachronously with other primary malignancies. The frequency of urinary system malignancies observed here represents a notable clinical finding that has not been widely emphasized in previous acromegaly literature. Given the recognized risk of second primary tumors in this population, these findings are particularly relevant when compared to national cancer patterns. For context, the most commonly reported cancers in Turkey between 2015 and 2020 were lung, breast, colorectal, prostate, and thyroid cancers (39); the prominence of urinary system malignancies in our acromegaly cohort is therefore disproportionate to the national pattern. To quantify this observation, we calculated standardized incidence ratios (SIRs) for urinary system cancers using age- and sex-specific Turkish population incidence rates as the reference. The overall SIR was 7.38 (95% CI: 2.97\u0026ndash;15.21; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming a greater than seven-fold excess risk. This excess was driven predominantly by male patients (SIR: 8.34, 95% CI: 3.06\u0026ndash;18.14; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the female subgroup did not reach statistical significance (SIR: 4.37, 95% CI: 0.11\u0026ndash;24.37; p\u0026thinsp;=\u0026thinsp;0.409), likely reflecting limited statistical power given the small number of events. Collectively, these observations support a proactive approach to cancer screening in acromegaly (40), with routine renal and urinary tract assessment at diagnosis and periodic surveillance guided by disease activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. The retrospective, single-center design inherently limits causality assessment and introduces referral and survivorship biases. The absence of an age-, sex-, and comorbidity-matched control group is a major limitation; given the cohort's mean age and substantial comorbidity burden, prevalence comparisons with general population data may overestimate the contribution of acromegaly itself to renal pathology. A key analytical limitation is our reliance on cross-sectional GH and IGF-1 measurements rather than measures of cumulative hormonal exposure, compounded by the use of absolute IGF-1 values rather than age- and sex-adjusted upper limits of normal\u0026mdash;a constraint imposed by the unavailability of historical assay-specific normative data. This potential misclassification of disease activity may be a primary explanation for the absence of an association between IGF-1 levels and renal outcomes, and the negative IGF-1 findings should not be interpreted as evidence that disease activity is unrelated to renal pathology. The evaluable denominators varied across outcomes due to differential data availability, particularly in patients diagnosed in earlier decades; however, the consistency of independent predictors across dual multivariate models supports the robustness of our findings. Additionally, data on concurrent potassium-altering medications were unavailable, and quantitative albuminuria data were limited to 157 patients, potentially underestimating CKD prevalence and precluding complete KDIGO risk stratification.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis large-scale retrospective cohort study demonstrates that renal cysts and chronic kidney disease are prevalent in patients with acromegaly. Our findings suggest that these structural and functional renal alterations may be related to cumulative hormonal exposure and associated metabolic comorbidities rather than cross-sectional hormone levels, although methodological limitations preclude definitive conclusions. The inverse association between baseline serum potassium and cyst risk, though requiring prospective validation with serial measurements and medication data, points toward tubular dysfunction as a potential contributor to renal injury, a hypothesis further supported by the predominance of preserved eGFR among CKD patients. The independent associations of male sex and nephrolithiasis with CKD, alongside the notable frequency of urinary system malignancies, reinforce the clinical importance of dedicated renal surveillance in acromegaly management. Future prospective, multicenter studies incorporating age- and sex-adjusted IGF-1 values, measures of cumulative hormonal exposure, and appropriately matched control groups are needed to confirm these observations and refine surveillance strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding: The authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eEthics Approval: This retrospective study was approved by the Hacettepe University Non-Interventional Clinical Research Ethics Committee (Decision No: 2023/08-15, Project No: GO 23/381) and was performed in accordance with the principles of the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003eConsent to Participate: Informed consent was waived due to the retrospective nature of the study, as approved by the ethics committee.\u003c/p\u003e\n\u003cp\u003eConsent to Publish: Not applicable.\u003c/p\u003e\n\u003cp\u003eData Availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions: Polat Ercan: conceptualization, data collection, formal analysis, and writing \u0026mdash; original draft. Busra Fırlatan Yazgan: data collection. Suleyman Nahit Sendur, Seda Hanife Oguz, and Selcuk Dagdelen: supervision, writing \u0026mdash; review and editing. Tomris Erbas: conceptualization, supervision, writing \u0026mdash; review and editing. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLavrentaki A, Paluzzi A, Wass JA, Karavitaki N. Epidemiology of acromegaly: review of population studies. Pituitary. 2017;20(1):4-9.\u003c/li\u003e\n\u003cli\u003eKamenicky P, Mazziotti G, Lombes M, Giustina A, Chanson P. Growth hormone, insulin-like growth factor-1, and the kidney: pathophysiological and clinical implications. Endocr Rev. 2014;35(2):234-81.\u003c/li\u003e\n\u003cli\u003eHoogenberg K, Sluiter WJ, Dullaart RP. Effect of growth hormone and insulin-like growth factor I on urinary albumin excretion: studies in acromegaly and growth hormone deficiency. Acta Endocrinol (Copenh). 1993;129(2):151-7.\u003c/li\u003e\n\u003cli\u003eTakai M, Izumino K, Oda Y, Terada Y, Inoue H, Takata M. Focal segmental glomerulosclerosis associated with acromegaly. Clin Nephrol. 2001;56(1):75-7.\u003c/li\u003e\n\u003cli\u003eYoshida H, Akikusa B, Saeki N, Hasegawa S, Iesato K, Yamamoto S, et al. Effect of pituitary microsurgery on acromegaly complicated nephrotic syndrome with focal segmental glomerulosclerosis: report of a rare clinical case. Am J Kidney Dis. 1999;33(6):1158-63.\u003c/li\u003e\n\u003cli\u003eKatznelson L, Laws ER, Jr., Melmed S, Molitch ME, Murad MH, Utz A, et al. Acromegaly: an endocrine society clinical practice guideline. J Clin Endocrinol Metab. 2014;99(11):3933-51.\u003c/li\u003e\n\u003cli\u003eAuriemma RS, Galdiero M, De Martino MC, De Leo M, Grasso LF, Vitale P, et al. The kidney in acromegaly: renal structure and function in patients with acromegaly during active disease and 1 year after disease remission. Eur J Endocrinol. 2010;162(6):1035-42.\u003c/li\u003e\n\u003cli\u003eFujio S, Takano K, Arimura H, Habu M, Bohara M, Hirano H, et al. Treatable glomerular hyperfiltration in patients with active acromegaly. Eur J Endocrinol. 2016;175(4):325-33.\u003c/li\u003e\n\u003cli\u003eCastagna G, Ippolito S, Cassibba S, Cortesi L, Costi E, Harb A, et al. Kidney function in acromegaly: evidence from a long-term observational study. Pituitary. 2025;28(3):56.\u003c/li\u003e\n\u003cli\u003eKidney Disease: Improving Global Outcomes CKDWG. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117-S314.\u003c/li\u003e\n\u003cli\u003eTerada N, Ichioka K, Matsuta Y, Okubo K, Yoshimura K, Arai Y. The natural history of simple renal cysts. J Urol. 2002;167(1):21-3.\u003c/li\u003e\n\u003cli\u003eChang CC, Kuo JY, Chan WL, Chen KK, Chang LS. Prevalence and clinical characteristics of simple renal cyst. J Chin Med Assoc. 2007;70(11):486-91.\u003c/li\u003e\n\u003cli\u003eMensel B, Kuhn JP, Kracht F, Volzke H, Lieb W, Dabers T, et al. Prevalence of renal cysts and association with risk factors in a general population: an MRI-based study. Abdom Radiol (NY). 2018;43(11):3068-74.\u003c/li\u003e\n\u003cli\u003eRule AD, Sasiwimonphan K, Lieske JC, Keddis MT, Torres VE, Vrtiska TJ. Characteristics of renal cystic and solid lesions based on contrast-enhanced computed tomography of potential kidney donors. Am J Kidney Dis. 2012;59(5):611-8.\u003c/li\u003e\n\u003cli\u003eOzveren B, Onganer E, Turkeri LN. Simple Renal Cysts: Prevalence, Associated Risk Factors and Follow-Up in a Health Screening Cohort. Urol J. 2016;13(1):2569-75.\u003c/li\u003e\n\u003cli\u003eYamamoto M, Matsumoto R, Fukuoka H, Iguchi G, Takahashi M, Nishizawa H, et al. Prevalence of Simple Renal Cysts in Acromegaly. Intern Med. 2016;55(13):1685-90.\u003c/li\u003e\n\u003cli\u003eBostan H, Kizilgul M, Calapkulu M, Kalkisim HK, Topcu FBG, Gul U, et al. The prevalence and associated risk factors of detectable renal morphological abnormalities in acromegaly. Pituitary. 2023.\u003c/li\u003e\n\u003cli\u003eRavine D, Gibson RN, Donlan J, Sheffield LJ. An ultrasound renal cyst prevalence survey: specificity data for inherited renal cystic diseases. Am J Kidney Dis. 1993;22(6):803-7.\u003c/li\u003e\n\u003cli\u003eMesschendorp AL, Casteleijn NF, Meijer E, Gansevoort RT. Somatostatin in renal physiology and autosomal dominant polycystic kidney disease. Nephrol Dial Transplant. 2020;35(8):1306-16.\u003c/li\u003e\n\u003cli\u003eMasyuk TV, Masyuk AI, Torres VE, Harris PC, Larusso NF. Octreotide inhibits hepatic cystogenesis in a rodent model of polycystic liver disease by reducing cholangiocyte adenosine 3\u0026apos;,5\u0026apos;-cyclic monophosphate. Gastroenterology. 2007;132(3):1104-16.\u003c/li\u003e\n\u003cli\u003eCaroli A, Perico N, Perna A, Antiga L, Brambilla P, Pisani A, et al. Effect of longacting somatostatin analogue on kidney and cyst growth in autosomal dominant polycystic kidney disease (ALADIN): a randomised, placebo-controlled, multicentre trial. Lancet. 2013;382(9903):1485-95.\u003c/li\u003e\n\u003cli\u003eMeijer E, Visser FW, van Aerts RMM, Blijdorp CJ, Casteleijn NF, D\u0026apos;Agnolo HMA, et al. Effect of Lanreotide on Kidney Function in Patients With Autosomal Dominant Polycystic Kidney Disease: The DIPAK 1 Randomized Clinical Trial. JAMA. 2018;320(19):2010-9.\u003c/li\u003e\n\u003cli\u003eKaltenbach TE, Engler P, Kratzer W, Oeztuerk S, Seufferlein T, Haenle MM, et al. Prevalence of benign focal liver lesions: ultrasound investigation of 45,319 hospital patients. Abdom Radiol (NY). 2016;41(1):25-32.\u003c/li\u003e\n\u003cli\u003eKamenicky P, Blanchard A, Frank M, Salenave S, Letierce A, Azizi M, et al. Body fluid expansion in acromegaly is related to enhanced epithelial sodium channel (ENaC) activity. J Clin Endocrinol Metab. 2011;96(7):2127-35.\u003c/li\u003e\n\u003cli\u003eVart P, Heerspink HJL. Progress and opportunities in measuring the burden of Chronic Kidney Disease. Lancet Reg Health Eur. 2022;20:100447.\u003c/li\u003e\n\u003cli\u003eMills KT, Xu Y, Zhang W, Bundy JD, Chen CS, Kelly TN, et al. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int. 2015;88(5):950-7.\u003c/li\u003e\n\u003cli\u003eSuleymanlar G, Utas C, Arinsoy T, Ates K, Altun B, Altiparmak MR, et al. A population-based survey of Chronic REnal Disease In Turkey--the CREDIT study. Nephrol Dial Transplant. 2011;26(6):1862-71.\u003c/li\u003e\n\u003cli\u003eHong S, Kim KS, Han K, Park CY. A cohort study found a high risk of end-stage kidney disease associated with acromegaly. Kidney Int. 2023;104(4):820-7.\u003c/li\u003e\n\u003cli\u003eHong S, Han K, Park CY. Long-Term Prognosis and Systemic Impact of Acromegaly: Analyses Utilizing Korean National Health Insurance Data. Endocrinol Metab (Seoul). 2025;40(1):1-9.\u003c/li\u003e\n\u003cli\u003eRule AD, Bergstralh EJ, Melton LJ, 3rd, Li X, Weaver AL, Lieske JC. Kidney stones and the risk for chronic kidney disease. Clin J Am Soc Nephrol. 2009;4(4):804-11.\u003c/li\u003e\n\u003cli\u003eDhondup T, Kittanamongkolchai W, Vaughan LE, Mehta RA, Chhina JK, Enders FT, et al. Risk of ESRD and Mortality in Kidney and Bladder Stone Formers. Am J Kidney Dis. 2018;72(6):790-7.\u003c/li\u003e\n\u003cli\u003eAlexander RT, Hemmelgarn BR, Wiebe N, Bello A, Morgan C, Samuel S, et al. Kidney stones and kidney function loss: a cohort study. BMJ. 2012;345:e5287.\u003c/li\u003e\n\u003cli\u003ePines A, Olchovsky D. Urolithiasis in acromegaly. Urology. 1985;26(3):240-2.\u003c/li\u003e\n\u003cli\u003eHeilberg IP, Czepielewski MA, Ajzen H, Ramos OL, Schor N. Metabolic factors for urolithiasis in acromegalic patients. Braz J Med Biol Res. 1991;24(7):687-96.\u003c/li\u003e\n\u003cli\u003eParkinson C, Kassem M, Heickendorff L, Flyvbjerg A, Trainer PJ. Pegvisomant-induced serum insulin-like growth factor-I normalization in patients with acromegaly returns elevated markers of bone turnover to normal. J Clin Endocrinol Metab. 2003;88(12):5650-5.\u003c/li\u003e\n\u003cli\u003eKamenicky P, Blanchard A, Gauci C, Salenave S, Letierce A, Lombes M, et al. Pathophysiology of renal calcium handling in acromegaly: what lies behind hypercalciuria? J Clin Endocrinol Metab. 2012;97(6):2124-33.\u003c/li\u003e\n\u003cli\u003eTerzolo M, Reimondo G, Berchialla P, Ferrante E, Malchiodi E, De Marinis L, et al. Acromegaly is associated with increased cancer risk: a survey in Italy. Endocr Relat Cancer. 2017;24(9):495-504.\u003c/li\u003e\n\u003cli\u003eDagdelen S, Cinar N, Erbas T. Increased thyroid cancer risk in acromegaly. Pituitary. 2014;17(4):299-306.\u003c/li\u003e\n\u003cli\u003eErvik M LF, Laversanne M, Ferlay J, Bray F. Global Cancer Observatory: Cancer Over Time. https://gco.iarc.fr/2021\u003c/li\u003e\n\u003cli\u003eOguz SH, Firlatan B, Sendur SN, Dagdelen S, Erbas T. Follow, consider, and catch: second primary tumors in acromegaly patients. Endocrine. 2023;80(1):160-73.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline demographic and clinical characteristics of patients (n=394)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eAge, years (mean \u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e56.9\u0026plusmn;12.5 (27-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eMale / Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e202 (51.3) / 192 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eOnset age of symptoms, year (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e37.8\u0026plusmn;12.3 (16-69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eDiagnosis age, year (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e41.1\u0026plusmn;12.3 (16-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eDuration of symptomatic period before diagnosis, years (median (min-max))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e2 (0-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eEstimated duration of the disease, years (median (min-max))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e17 (5-51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eSmoking status, (n=259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e103 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eAlcohol use, (n=256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e33 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 427px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, (mean\u0026plusmn;SD) (n=257)\u003c/p\u003e\n \u003cp\u003eNormal weight (18.5-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eOverweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eObese (30-34.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eSeverely obese (35-39.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eMorbidly obese (\u0026ge;40 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e30\u0026plusmn;5.8\u003c/p\u003e\n \u003cp\u003e44 (17.1)\u003c/p\u003e\n \u003cp\u003e105 (40.9)\u003c/p\u003e\n \u003cp\u003e72 (28)\u003c/p\u003e\n \u003cp\u003e18 (7)\u003c/p\u003e\n \u003cp\u003e18 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: standard deviation, BMI: body mass index.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of laboratory findings at diagnosis and at the last follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAt diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLast follow-up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e13\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e13.3\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eFasting plasma glucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e111\u0026plusmn;42.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e104.7\u0026plusmn;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eHbA1C, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e6.18\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e6.19\u0026plusmn;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eBlood urea nitrogen, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e13.4\u0026plusmn;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e15.3\u0026plusmn;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.7\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e100.6\u0026plusmn;16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e92.4\u0026plusmn;19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eSodium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e140.4\u0026plusmn;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e140.8\u0026plusmn;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003ePotassium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4.32\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e4.35\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eProtein, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e7.2\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e7.2\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4.3\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e4.3\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eUric acid, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4.8\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e5.1\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e9.63\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e9.56\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003ePhosphorus, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e4.3\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e3.8\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eCholecalciferol, (min-max), ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e16.4 (1.3-74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e19 (5-54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eParathyroid hormone, (min-max), pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e42.9 (8.9-267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e52.8 (3.0-930.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 283px;\"\u003e\n \u003cp\u003eUrine albumin/creatinine ratio, (min-max), mg/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e12.7 (0.2-1030.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e10 (0.0-2048.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: standard deviation, eGFR: estimated glomerular filtration rate, *according to the CKD-EPI 2021 formula\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Evaluation of kidney pathologies in the study cohort (n=394 patients)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eChronic kidney disease, n=354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e57 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eNephrolithiasis, n=284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e43 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eRenal cyst, n=283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e116 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eCyst localization, n=116\u003c/p\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003cp\u003eBilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (22.4)\u003c/p\u003e\n \u003cp\u003e35 (30.2)\u003c/p\u003e\n \u003cp\u003e55 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eNumber of renal cysts, median (min-max)\u003c/p\u003e\n \u003cp\u003eRight, n=81\u003c/p\u003e\n \u003cp\u003eLeft, n=90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (1-25)\u003c/p\u003e\n \u003cp\u003e2 (1-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eLargest diameter of the cyst, median (min-max), mm\u003c/p\u003e\n \u003cp\u003eRight, n=81\u003c/p\u003e\n \u003cp\u003eLeft, n=90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (2-90)\u003c/p\u003e\n \u003cp\u003e15 (2-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eRight kidney longitudinal length, mean\u0026plusmn;SD, mm, n=222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e108.81\u0026plusmn;12.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eRight kidney transverse length, mean\u0026plusmn;SD, mm, n=222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e52.89\u0026plusmn;7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eLeft kidney longitudinal length, mean\u0026plusmn;SD, mm, n=222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e111.72\u0026plusmn;13.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003eLeft kidney transverse length, mean\u0026plusmn;SD, mm, n=222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e57.85\u0026plusmn;8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSD: Standard deviation\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariate logistic regression analysis for renal cyst risk factors in acromegaly\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"127%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate Analysis,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1, n=278\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate Analysis,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2, n=244\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.055 (1.032-1.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.049 (1.022-1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.057 (1.028-1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eMale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.504 (0.932-2.426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eDiagnosis age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.043 (1.021-1.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.685 (1.014-2.802)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.044\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.025 (0.573-1.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.902 (0.469-1.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.697 (1.052-2.739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.030\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.192 (0.684-2.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.547 (0.825-2.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.774 (1.099-2.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.019\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.972 (0.546-1.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.664 (0.344-1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eNephrolithiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.726 (1.388-5.356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.455 (1.170-5.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3.714 (1.583-8.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eLiver cysts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.012 (1.542-5.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e2.657 (1.291-5.467)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.284 (1.484-7.266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eLeft ventricular hypertrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.815 (0.996-3.305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.740 (0.986-3.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMultiple neoplasms (\u0026gt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.471 (1.371-4.452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.915 (1.015-3.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.045\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.130 (1.058-4.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.034\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.510 (0.265-0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.043\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.373 (0.171-0.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1 includes all patients with available imaging data. Model 2 additionally incorporates baseline serum potassium, restricting the analysis to patients with available data for this variable. Hosmer-Lemeshow goodness-of-fit: Model 1 \u0026chi;\u0026sup2;=7.252, df=8, p=0.510; Model 2 \u0026chi;\u0026sup2;=3.953, df=8, p=0.861. Nagelkerke R\u0026sup2;: Model 1=0.207; Model 2=0.279. CI: Confidence Interval, OR: Odds Ratio, K: Potassium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariate logistic regression analysis for CKD risk factors in acromegaly\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"125%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1, n=353\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2, n=283\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.075 (1.045-1.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.052 (1.015-1.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.044 (1.003-1.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.033\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.243 (1.236-4.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.575 (1.811-7.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.134 (1.470-6.681)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eDiagnosis age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.040 (1.016-1.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eEstimated duration of the disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.048 (1.015-1.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.018 (0.982-1.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.025 (0.985-1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNephrolithiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4.286 (2.092-8.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e3.095 (1.371-6.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.460 (1.271-4.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.583 (0.747-3.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.523 (0.654-3.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.529 (1.407-4.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.205 (0.613-2.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.469 (0.694-3.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4.369 (2.318-8.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.786 (1.316-5.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.371 (1.036-5.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.041\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eLeft ventricular hypertrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.212 (1.656-6.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.103 (1.666-5.779)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.101 (0.528-2.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.052 (0.471-2.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNeoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.407 (1.349-4.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.800 (0.944-3.432)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.013 (0.956-4.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.975 (1.027-3.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.041\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1 includes all patients with available CKD data. Model 2 additionally incorporates nephrolithiasis, restricting the analysis to patients with available data for this variable. Hosmer-Lemeshow goodness-of-fit: Model 1 \u0026chi;\u0026sup2;=3.948, df=8, p=0.862; Model 2 \u0026chi;\u0026sup2;=11.907, df=8, p=0.155. Nagelkerke R\u0026sup2;: Model 1=0.268; Model 2=0.301. CI: Confidence Interval, OR: Odds Ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Standardized incidence ratios for urinary system cancers in the acromegaly cohort (Poisson-based analysis)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3967.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.97\u0026ndash;15.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2035.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.11\u0026ndash;24.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1931.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.06\u0026ndash;18.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eMale/Female SIR ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.23\u0026ndash;15.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN: number of patients; PY: person-years; Obs: observed cases; Exp: expected cases; SIR: standardized incidence ratio; CI: confidence interval. Expected cases were calculated using age- and sex-specific Turkish population cancer incidence data as the reference.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pituitary","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pitu","sideBox":"Learn more about [Pituitary]()","snPcode":"11102","submissionUrl":"https://submission.nature.com/new-submission/11102/3","title":"Pituitary","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acromegaly, pituitary adenoma, growth hormone, IGF-1, chronic kidney disease, renal cyst, nephrolithiasis, urinary neoplasm","lastPublishedDoi":"10.21203/rs.3.rs-9171899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9171899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eWhile the cardiovascular and metabolic morbidities of acromegaly are well-established, data regarding long-term morphological and functional renal changes remain limited. This study evaluates the prevalence and independent predictors of renal cysts and chronic kidney disease (CKD), and additionally examines the frequency of urinary system malignancies relative to the general population in a large acromegaly cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively evaluated medical records, radiological findings, and clinical parameters of 394 patients with acromegaly monitored at a single tertiary center over four decades. Independent predictors of renal cysts and CKD were assessed using multivariate logistic regression. Standardized incidence ratios (SIRs) for urinary system cancers were calculated using Turkish population data as the reference.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe cohort (202 male, 192 female) had a median disease duration of 17 years. Renal cysts were detected in 41% of patients, of which 47.4% were bilateral. CKD and nephrolithiasis were present in 16.1% and 15.1% of patients, respectively; notably, nearly two-thirds (64.9%) of CKD patients had preserved eGFR, with CKD diagnosed on the basis of albuminuria or structural abnormalities. Multivariate regression identified advanced age, nephrolithiasis, liver cysts, and multiple neoplasms as independent risk factors for renal cyst formation. Notably, higher baseline serum potassium was inversely associated with cyst development (OR: 0.37 per 1 mEq/L increase, p\u0026thinsp;=\u0026thinsp;0.013). Advanced age, male sex, hypertension, and nephrolithiasis were independent predictors of CKD. Cross-sectional GH and absolute IGF-1 levels were not directly associated with CKD or cyst prevalence. Urinary system cancers were among the most frequent malignancies after thyroid cancer, with a greater than seven-fold excess compared to the general population (SIR: 7.38, 95% CI: 2.97\u0026ndash;15.21; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRenal cysts, CKD, and urinary system malignancies are prevalent in acromegaly. Structural and functional renal alterations may be related to cumulative hormonal exposure and metabolic comorbidities rather than cross-sectional GH/IGF-1 measurements alone, though the absence of matched controls limits causal inference. The inverse association between baseline potassium and cyst risk, and the excess of urinary system cancers, support the need for dedicated renal surveillance in long-term acromegaly management.\u003c/p\u003e","manuscriptTitle":"Focus on Renal Morphology, Chronic Kidney Disease, and Urinary System Malignancies in Acromegaly: Report on Data Collected Over a 40-year Period","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 06:58:41","doi":"10.21203/rs.3.rs-9171899/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T06:32:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T18:02:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:26:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T18:17:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185341148049133017803569993908976216138","date":"2026-05-04T15:32:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52463924318522012993595462868777262134","date":"2026-05-04T00:14:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140927049420209874226110324745320099867","date":"2026-05-03T21:44:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311502967089677000070181034941168857","date":"2026-03-30T08:03:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T18:07:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T05:22:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T05:22:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pituitary","date":"2026-03-19T17:03:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pituitary","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pitu","sideBox":"Learn more about [Pituitary]()","snPcode":"11102","submissionUrl":"https://submission.nature.com/new-submission/11102/3","title":"Pituitary","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f57eca2f-1056-4e57-a5bd-9cf9d254eba6","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T06:32:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T18:02:06+00:00","index":19,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T21:26:50+00:00","index":18,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T18:17:21+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"185341148049133017803569993908976216138","date":"2026-05-04T15:32:08+00:00","index":16,"fulltext":""},{"type":"reviewerAgreed","content":"52463924318522012993595462868777262134","date":"2026-05-04T00:14:38+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"140927049420209874226110324745320099867","date":"2026-05-03T21:44:17+00:00","index":14,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:40:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 06:58:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9171899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9171899","identity":"rs-9171899","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}