Metabolic and inflammatory parameters in relation to baseline characterization and treatment outcome in patients with prolactinoma: insights from a retrospective cohort study at a single tertiary center.

Background: Prolactinomas (PRLs) are prevalent pituitary adenomas associated with metabolic changes and increased cardiovascular morbidity. This study examined clinical, endocrine, metabolic, and inflammatory profiles in PRL patients, aiming to identify potential prognostic markers. Methods: The study comprised data from 59 PRL patients gathered in a registry at the University Hospital of Zurich. Diagnostic criteria included MRI findings and elevated serum prolactin levels. We assessed baseline and follow-up clinical demographics, metabolic markers, serum inflammation-based scores, and endocrine parameters. Treatment outcomes were evaluated based on prolactin normalization, tumor shrinkage, and cabergoline dosage. Results: The PRL cohort exhibited a higher prevalence of overweight/obesity, prediabetes/diabetes mellitus, and dyslipidemia compared to the general population. Significant correlations were found between PRL characteristics and BMI, HbA1c, and fT4 levels. Follow-up data indicated decreases in tumor size, tumor volume, prolactin levels, and LDL-cholesterol, alongside increases in fT4 and sex hormones levels. No significant associations were observed between baseline parameters and tumor shrinkage at follow-up. A positive association was noted between PRL size/volume and the time to achieve prolactin normalization, and a negative association with baseline fT4 levels. Conclusion: This study underscores the metabolic significance of PRL, with notable correlations between PRL parameters and metabolic indices. However, inflammatory markers were not significantly correlated with patient stratification or outcome prediction. These findings highlight the necessity for standardized follow-up protocols and further research into the metabolic pathogenesis in PRL patients.

Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension.

Introduction: Endocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing's syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT. Methods: Retrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus. Results: After adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites - 18 in PPGL, 15 in CS, and 23 in PA - were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites. Discussions: Our study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL's metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.

Psychopathological characteristics in patients with arginine vasopressin deficiency (central diabetes insipidus) and primary polydipsia compared to healthy controls.

OBJECTIVE: Distinguishing arginine vasopressin deficiency (AVP-D; central diabetes insipidus) from primary polydipsia (PP), commonly referred to as psychogenic polydipsia, is challenging. Psychopathologic findings, commonly used for PP diagnosis in clinical practice, are rarely evaluated in AVP-D patients, and no comparative data between the two conditions currently exist. DESIGN: Data from two studies involving 82 participants [39 AVP-D, 28 PP, and 15 healthy controls (HC)]. METHODS: Psychological evaluations were conducted using standardized questionnaires measuring anxiety [State-Trait Anxiety Inventory (STAI)], alexithymia [Toronto Alexithymia Scale (TAS-20)], depressive symptoms (Beck's Depression Inventory-II (BDI-II), and overall mental health [Short Form-36 Health Survey (SF-36)]. Higher STAI, TAS-20, and BDI-II scores suggest elevated anxiety, alexithymia, and depression, while higher SF-36 scores signify better overall mental health. RESULTS: Compared to HC, patients with AVP-D and PP showed higher levels of anxiety (HC 28 points [24-31] vs AVP-D 36 points [31-45]; vs PP 38 points [33-46], P < .01), alexithymia (HC 30 points [29-37] vs AVP-D 43 points [35-54]; vs PP 46 points [37-55], P < .01), and depression (HC 1 point [0-2] vs AVP-D 7 points [4-14]; vs PP 7 points [3-13], P < .01). Levels of anxiety, alexithymia, and depression showed no difference between both patient groups (P = .58, P = .90, P = .50, respectively). Compared to HC, patients with AVP-D and PP reported similarly reduced self-reported overall mental health scores (HC 84 [68-88] vs AVP-D 60 [52-80], P = .05; vs PP 60 [47-74], P < .01). CONCLUSION: This study reveals heightened anxiety, alexithymia, depression, and diminished overall mental health in patients with AVP-D and PP. The results emphasize the need for careful interpretation of psychopathological characteristics to differentiate between AVP-D and PP.

Unawareness of Primary Aldosteronism as a Common Cause of Hypokalemia - Insights from the IPAHK+ Trial (Incidence of Primary Aldosteronism in Patients with Hypokalemia).

Hypokalemia plays an important role in the diagnosis and management of primary aldosteronism (PA). While the hypokalemic variant of the disease accounts for about one third of all cases, little is known about the incidence of PA in hypokalemic populations. The IPAHK+ study is an epidemiological, cross-sectional trial to provide evidence on the incidence of PA in hypokalemic patients from a university hospital outpatient population. Recruitment of outpatients with hypokalemia≤3 mmol/l is carried out on a continuous referral-basis through an automated data delivery system. Up to an interim data closure, 66 patients underwent the study protocol. The mean age of the participants was 52.9±1.5 years with an equal sex ratio of 1:1 women to men, a mean potassium value of 2.78±0.31 mmol/l [1.8;3.0] and a prevalence of arterial hypertension of 72.7%. PA was diagnosed in 46.6% of all participants, all of whom had a history of hypertension. Incidence of PA increased continuously with decreasing potassium levels with proportions of 26.7%, 50% and 57.1% in the subgroups of 3.0 mmol/l (n=15), 2.8-2.9 mmol/l (n=22) and≤2.7 mmol/l (n=21), respectively. Prior to testing, 59.1% of all patients presented at least with one plausible other cause of hypokalemia. The incidence of PA in the investigated outpatient population was more than 4 out of 10 and inversely correlated with baseline potassium levels. Moderate or severe hypokalemia, regardless of its cause, should therefore prompt evaluation for PA in hypertensive individuals. Normotensive hypokalemic PA was not observed in this cohort.

Arginine or Hypertonic Saline-Stimulated Copeptin to Diagnose AVP Deficiency.

BACKGROUND: Distinguishing between arginine vasopressin (AVP) deficiency and primary polydipsia is challenging. Hypertonic saline-stimulated copeptin has been used to diagnose AVP deficiency with high accuracy but requires close sodium monitoring. Arginine-stimulated copeptin has shown similar diagnostic accuracy but with a simpler test protocol. However, data are lacking from a head-to-head comparison between arginine-stimulated copeptin and hypertonic saline-stimulated copeptin in the diagnosis of AVP deficiency. METHODS: In this international, noninferiority trial, we assigned adult patients with polydipsia and hypotonic polyuria or a known diagnosis of AVP deficiency to undergo diagnostic evaluation with hypertonic-saline stimulation on one day and with arginine stimulation on another day. Two endocrinologists independently made the final diagnosis of AVP deficiency or primary polydipsia with use of clinical information, treatment response, and the hypertonic-saline test results. The primary outcome was the overall diagnostic accuracy according to prespecified copeptin cutoff values of 3.8 pmol per liter after 60 minutes for arginine and 4.9 pmol per liter once the sodium level was more than 149 mmol per liter for hypertonic saline. RESULTS: Of the 158 patients who underwent the two tests, 69 (44%) received the diagnosis of AVP deficiency and 89 (56%) received the diagnosis of primary polydipsia. The diagnostic accuracy was 74.4% (95% confidence interval [CI], 67.0 to 80.6) for arginine-stimulated copeptin and 95.6% (95% CI, 91.1 to 97.8) for hypertonic saline-stimulated copeptin (estimated difference, -21.2 percentage points; 95% CI, -28.7 to -14.3). Adverse events were generally mild with the two tests. A total of 72% of the patients preferred testing with arginine as compared with hypertonic saline. Arginine-stimulated copeptin at a value of 3.0 pmol per liter or less led to a diagnosis of AVP deficiency with a specificity of 90.9% (95% CI, 81.7 to 95.7), whereas levels of more than 5.2 pmol per liter led to a diagnosis of primary polydipsia with a specificity of 91.4% (95% CI, 83.7 to 95.6). CONCLUSIONS: Among adult patients with polyuria polydipsia syndrome, AVP deficiency was more accurately diagnosed with hypertonic saline-stimulated copeptin than with arginine-stimulated copeptin. (Funded by the Swiss National Science Foundation; CARGOx ClinicalTrials.gov number, NCT03572166.).

Development and external validation of clinical prediction models for pituitary surgery.

Introduction: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. Research question: This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. Material and methods: With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. Results: The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63-0.80) for GTR, 0.69 (0.52-0.83) for BR, as well as 0.82 (0.76-0.89) for IMP. Discussion and conclusion: All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient.

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study.

BACKGROUND: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. METHODS: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. FINDINGS: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. INTERPRETATION: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. FUNDING: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios.

Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma.

PURPOSE: Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. DESIGN: A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. METHODS: Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. RESULTS: Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. CONCLUSIONS: Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.

Machine learning-based clinical outcome prediction in surgery for acromegaly.

PURPOSE: Biochemical remission (BR), gross total resection (GTR), and intraoperative cerebrospinal fluid (CSF) leaks are important metrics in transsphenoidal surgery for acromegaly, and prediction of their likelihood using machine learning would be clinically advantageous. We aim to develop and externally validate clinical prediction models for outcomes after transsphenoidal surgery for acromegaly. METHODS: Using data from two registries, we develop and externally validate machine learning models for GTR, BR, and CSF leaks after endoscopic transsphenoidal surgery in acromegalic patients. For the model development a registry from Bologna, Italy was used. External validation was then performed using data from Zurich, Switzerland. Gender, age, prior surgery, as well as Hardy and Knosp classification were used as input features. Discrimination and calibration metrics were assessed. RESULTS: The derivation cohort consisted of 307 patients (43.3% male; mean [SD] age, 47.2 [12.7] years). GTR was achieved in 226 (73.6%) and BR in 245 (79.8%) patients. In the external validation cohort with 46 patients, 31 (75.6%) achieved GTR and 31 (77.5%) achieved BR. Area under the curve (AUC) at external validation was 0.75 (95% confidence interval: 0.59-0.88) for GTR, 0.63 (0.40-0.82) for BR, as well as 0.77 (0.62-0.91) for intraoperative CSF leaks. While prior surgery was the most important variable for prediction of GTR, age, and Hardy grading contributed most to the predictions of BR and CSF leaks, respectively. CONCLUSIONS: Gross total resection, biochemical remission, and CSF leaks remain hard to predict, but machine learning offers potential in helping to tailor surgical therapy. We demonstrate the feasibility of developing and externally validating clinical prediction models for these outcomes after surgery for acromegaly and lay the groundwork for development of a multicenter model with more robust generalization.

Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary Hypertension.

CONTEXT: Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. OBJECTIVE: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. METHODS: Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a "classical approach" (CA) (performing a series of univariate and multivariate analyses) and a "machine learning approach" (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively. RESULTS: From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). CONCLUSION: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

[CME: Pituitary Incidentaloma in Adults: Key Knowledge for the General Practice]. / CME: Zufallsraumforderung in der Hypophyse bei Erwachsenen: das Wichtigste für die Hausarztpraxis.

CME: Pituitary Incidentaloma in Adults: Key Knowledge for the General Practice Abstract. Incidentally detected pituitary masses, so-called pituitary incidentalomas, are increasing in frequency as the frequency of performing imaging increases. Evaluation of the imaging from a trained neuroradiologist as well as additional endocrinological and, if necessary, neuroophtalmological studies are part of the initial assessment that drives the treatment decision: in the case of benign small lesions with unremarkable assessment results, follow-up is indicated, whereas potentially malignant lesions or lesions with endocrinological or neuroophtalmological irregularities are usually treated. In borderline cases, interdisciplinary work is beneficial for the determination of the case-specific treatment procedure.

[CME/Answers: Pituitary Incidentaloma in Adults: Key Knowledge for the General Practice]. / CME/Antworten zu «Zufallsraumforderung in der Hypophyse bei Erwachsenen: das Wichtigste für die Hausarztpraxis¼.

CME/Answers: Pituitary Incidentaloma in Adults: Key Knowledge for the General Practice Abstract. Incidentally detected pituitary masses, so-called pituitary incidentalomas, are increasing in frequency as the frequency of performing imaging increases. Evaluation of the imaging from a trained neuroradiologist as well as additional endocrinological and, if necessary, neuroophtalmological studies are part of the initial assessment that drives the treatment decision: in the case of benign small lesions with unremarkable assessment results, follow-up is indicated, whereas potentially malignant lesions or lesions with endocrinological or neuroophtalmological irregularities are usually treated. In borderline cases, interdisciplinary work is beneficial for the determination of the case-specific treatment procedure.

Metabolic impact of pheochromocytoma/paraganglioma: targeted metabolomics in patients before and after tumor removal.

OBJECTIVE: Excess catecholamine release by pheochromocytomas and paragangliomas (PPGL) leads to characteristic clinical features and increased morbidity and mortality. The influence of PPGLs on metabolism is ill described but may impact diagnosis and management. The objective of this study was to systematically and quantitatively study PPGL-induced metabolic changes at a systems level. DESIGN: Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens in a clinically well-characterized prospective cohort study. METHODS: Analyses of metabolic profiles of plasma specimens from 56 prospectively enrolled and clinically well-characterized patients (23 males, 33 females) with catecholamine-producing PPGL before and after surgery, as well as measurement of 24-h urinary catecholamine using LC-MS/MS. RESULTS: From 127 analyzed metabolites, 15 were identified with significant changes before and after surgery: five amino acids/biogenic amines (creatinine, histidine, ornithine, sarcosine, tyrosine) and one glycerophospholipid (PCaeC34:2) with increased concentrations and six glycerophospholipids (PCaaC38:1, PCaaC42:0, PCaeC40:2, PCaeC42:5, PCaeC44:5, PCaeC44:6), two sphingomyelins (SMC24:1, SMC26:1) and hexose with decreased levels after surgery. Patients with a noradrenergic tumor phenotype had more pronounced alterations compared to those with an adrenergic tumor phenotype. Weak, but significant correlations for 8 of these 15 metabolites with total urine catecholamine levels were identified. CONCLUSIONS: This first large prospective metabolomics analysis of PPGL patients demonstrates broad metabolic consequences of catecholamine excess. Robust impact on lipid and amino acid metabolism may contribute to increased morbidity of PPGL patients.

Metabolic Alterations in Patients with Pheochromocytoma.

Metabolic alterations in patients with hormonally active pheochromocytoma/paraganglioma (PPGL) have been described early on in the literature. The initial findings were related to disturbed glucose homeostasis and lipolysis activation, as well as elevated metabolic rates in affected patients. Similarly, from early autopsy reports, the presence of brown adipose tissue had been noted in PPGL patients. In more recent years, changes in body weight, fat mass and distribution have been analyzed in more detail in addition to activity of brown adipose tissue based on functional imaging techniques. Over the last decades, several larger case series and cohort studies have contributed towards the elucidation of possible mechanism contributing to these clinical observations. Herein, we summarize the clinical and experimental data regarding metabolic alterations and related clinical manifestations in PPGL patients.

[CME: Pheochromocytoma in the General Practice]. / CME: Phäochromozytom in der Hausarztpraxis.

CME: Pheochromocytoma in the General Practice Abstract. Pheochromocytoma are rare tumors, usually symptomatic due to their hormonal activity with excessive catecholamine secretion. Because of their bright spectrum of clinical presentation, they are often undiagnosed. The first diagnostic step is biochemical screening with measurement of free metanephrines in plasma or fractionated metanephrines in 24-hours urine. Knowledge about measurement method used and potential preanalytical factors leading to false results, are necessary for the interpretation. Tumor localisation (imaging) is performed after biochemical evidence of the tumor is present. Laparoscopic surgery is for the majority of cases the primary therapy and curative. Lifelong biochemical follow-up is necessary, with additional tests in case of hereditary tumors.

Autosomal dominant polycystic kidney disease: prevalence of renal neoplasias in surgical kidney specimens.

BACKGROUND: The role of autosomal dominant polycystic kidney disease (ADPKD) as a risk factor for renal cell carcinoma (RCC) is still under discussion. Data on prevalence of RCC in ADPKD are limited, especially on a large population scale. The aim of this study was to analyze the prevalence of RCC in ADPKD kidneys and characterize the clinical features of this coincidence. METHODS: Based on our histopathological registry for ADPKD and the Else Kröner-Fresenius Registry, we retrospectively reviewed malignant and benign renal lesions in patients with ADPKD who had undergone renal surgery from 1988 to 2011. RESULTS: 240 ADPKD patients underwent 301 renal surgeries. Mean age at surgery was 54 years. Overall, 16 malignant and 11 benign lesions were analyzed in 301 kidneys (5.3%; 3.7%), meaning that 12/240 (5%; 1:20) patients presented with malignant renal lesions. 66.7% (8/12) of these patients had undergone dialysis prior to surgery. We found 10/16 (63%) papillary RCC, 5/16 (31%) clear cell RCC, and 1/16 (6%) papillary noninvasive urothelial cancer. Regarding all renal lesions, 6/17 (35.3%) patients had more than one histological finding in their kidneys. In 2 cases, metachronous metastases were removed. Mean follow-up was 66.7 months. CONCLUSION: Kidney-related prevalence of RCC in ADPKD kidneys was surprisingly high. Whether or not this is due to chronic dialysis or due to the underlying disease is still speculative. Like other cystic renal diseases with an increased risk for RCC, the attending physician should be aware of the malignant potential of ADPKD, especially with concomitant dialysis.