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

Approach to the Patient: Concept and Application of Targeted Radiotherapy in the Paraganglioma Patient

Paragangliomas can metastasize, posing potential challenges both in symptomatic management and disease control. Systemic targeted radiotherapies using 131I-MIBG and 177Lu-DOTATATE are a mainstay in the treatment of metastatic paragangliomas. This clinical scenario and discussion aim to enhance physicians' knowledge of the stepwise approach to treat these patients with paraganglioma-targeted radiotherapies. It comprehensively discusses current approaches to selecting paraganglioma patients for targeted radiotherapies and how to choose between the two radiotherapies based on specific patient and tumor characteristics, when either therapy is feasible, or one is superior to another. The safety, efficacy, toxicity profiles, and optimization of these radiotherapies are also discussed, along with other therapeutic options including radiotherapies, available for patients besides these two therapies. Perspectives in radiotherapies of paraganglioma patients are outlined since they hold promising approaches in the near future that can improve patient outcomes.

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.

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

Plasma metabolomics of oral squamous cell carcinomas based on NMR and MS approaches provides biomarker identification and survival prediction

Metabolomics has proven to be an important omics approach to understand the molecular pathways underlying the tumour phenotype and to identify new clinically useful markers. The literature on cancer has illustrated the potential of this approach as a diagnostic and prognostic tool. The present study aimed to analyse the plasma metabolic profile of patients with oral squamous cell carcinoma (OSCC) and controls and to compare patients with metastatic and primary tumours at different stages and subsites using nuclear magnetic resonance and mass spectrometry. To our knowledge, this is the only report that compared patients at different stages and subsites and replicates collected in diverse institutions at different times using these methodologies. Our results showed a plasma metabolic OSCC profile suggestive of abnormal ketogenesis, lipogenesis and energy metabolism, which is already present in early phases but is more evident in advanced stages of the disease. Reduced levels of several metabolites were also associated with an unfavorable prognosis. The observed metabolomic alterations may contribute to inflammation, immune response inhibition and tumour growth, and may be explained by four nonexclusive views-differential synthesis, uptake, release, and degradation of metabolites. The interpretation that assimilates these views is the cross talk between neoplastic and normal cells in the tumour microenvironment or in more distant anatomical sites, connected by biofluids, signalling molecules and vesicles. Additional population samples to evaluate the details of these molecular processes may lead to the discovery of new biomarkers and novel strategies for OSCC prevention and treatment.

Targeted metabolomics detects a putatively diagnostic signature in plasma and dried blood spots from head and neck paraganglioma patients

Head and neck paragangliomas (HNPGLs), rare chemoresistant tumors curable only with surgery, are strongly influenced by genetic predisposition, hence patients and relatives require lifetime follow-up with MRI and/or PET-CT because of de novo disease risk. This entails exposure to electromagnetic/ionizing radiation, costs, and organizational challenges, because patients and relatives are scattered far from reference centers. Simplified first-line screening strategies are needed. We employed flow injection analysis tandem mass spectrometry, as used in newborn metabolic screening, to compare the plasma metabolic profile of HNPGL patients (59 samples, 56 cases) and healthy controls (24 samples, 24 cases). Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) highlighted a distinctive HNPGL signature, likely reflecting the anaplerotic conversion of the TCA cycle to glutaminolysis and catabolism of branched amino acids, DNA damage and deoxyadenosine (dAdo) accumulation, impairment of fatty acid oxidation, switch towards the Warburg effect and proinflammatory lysophosphatidylcholines (LPCs) signaling. Statistical analysis of the metabolites that most impacted on PLS-DA was extended to 10 acoustic neuroma and 2 cholesteatoma patients, confirming significant differences relative to the HNPGL plasma metabolomic profile. The best confusion matrix from the ROC curve built on 2 metabolites, dAdo and C26:0-LPC, provided specificity of 94.29% and sensitivity of 89.29%, with positive and negative predictive values of 96.2% and 84.6%, respectively. Analysis of dAdo and C26:0-LPC levels in dried venous and capillary blood confirmed that dAdo, likely deriving from 2'-deoxy-ATP accumulated in HNPGL cells following endogenous genotoxic damage, efficiently discriminated HNPGL patients from healthy controls and acoustic neuroma/cholesteatoma patients on easily manageable dried blood spots.

Succinate: A Serum Biomarker of SDHB-Mutated Paragangliomas and Pheochromocytomas

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) are rare neuroendocrine tumors that are frequently associated with succinate dehydrogenase (SDH) germline mutations. When mutated, SDH losses its function, thus leading to succinate accumulation.

OBJECTIVE

In this study, we evaluated serum succinate levels as a new metabolic biomarker in SDHx-related carriers.

METHODS

Retrospective monocentric study of 88 PPGL patients (43 sporadic, 35 SDHB, 10 SDHA/C/D), 17 tumor-free familial asymptomatic carriers (13 SDHB, 4 SDHC/D), and 60 healthy controls. Clinical, biological, and imaging data were reviewed. Serum succinate levels (n = 280) were quantified by an ultra-performance liquid chromatography coupled to a tandem mass spectrometry method and correlated to SDHx mutational status, disease extension, and other biological biomarkers.

RESULTS

Serum succinate levels > 7 μM allowed identification of tumor-free asymptomatic SDHB-mutated cases compared to a healthy control group (100% specificity; 85% sensitivity). At PPGL diagnosis, SDHB-mutated patients had a significantly increased median succinate level (14 μM) compared to sporadic patients (8 μM) (P < 0.01). Metastatic disease extension was correlated to serum succinate levels (r = 0.81). In the SDHB group, patients displaying highest tumor burdens showed significant increased succinate levels compared to the sporadic group (P < 0.0001).

CONCLUSIONS

In this pilot study, we showed that serum succinate level is an oncometabolic biomarker that should be useful to identify SDHB-related carriers. Succinate levels are also a marker of metabolic tumor burden in patients with a metastatic PPGL and a potential marker of treatment response and follow-up.

The top 100 most-cited papers in pheochromocytomas and paragangliomas: A bibliometric study

Background

The purpose of this study was to define and analyze the characteristics of the top 100 most-cited articles and reviews on the topic of pheochromocytomas and paragangliomas (PPGLs) by using bibliometric methods.

Methods

We explored the Web of Science Core Collection database to gather the 100 top-cited original articles and reviews of PPGL from 1985 to 20 December 2020. We conducted a bibliometric study to identify the most influential journals, authors, countries, and institutions in the PPGL field.

Results

The 100 top-cited papers were cited a total number of 25,723 times, ranging from 131 to 1,144 (mean, 257.23 ± 173.64). All of these 100 top-cited papers were published between 1999 and 2017, and the number of top-cited papers published before 2008 (1999–2008) was significantly higher than that after 2008 (2009–2017) (p = 0.043). The journal with the highest number of published papers is the Journal of Clinical Endocrinology &amp; Metabolism (n = 23). The United States was the most productive country in this topic, which published about half of these publications (n = 51). The National Institutes of Health (NIH) had the largest number of publications (n = 17). Genes or genetics is still the hottest topic in the field of PPGLs.

Conclusions

We defined and analyzed the top 100 most-cited papers in the field of PPGLs by gathering detailed information. These data provided insights into the most influential studies related to PPGL. We hoped to inspire researchers and readers in this field to improve their understanding of PPGL research trends and provide ideas for future research from unique perspectives.

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.

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.

Correlation Between Plasma Catecholamines, Weight, and Diabetes in Pheochromocytoma and Paraganglioma

CONTEXT

Pheochromocytomas and paragangliomas (PCC/PGL) are neuroendocrine tumors with discrete catecholamine profiles that cause incompletely understood metabolic and physiologic changes.

OBJECTIVE

The objective was to evaluate relationships between plasma catecholamines, body weight, and hemoglobin A1c (HbA1c). We hypothesized that individual catecholamines would correlate negatively with weight and glucose control.

DESIGN

A retrospective cohort study was performed (1999-2020). Wilcoxon rank-sum tests compared nonparametric, continuous variables; mixed-effect linear modeling (MEM) evaluated relationships between catecholamines and weight or HbA1c. The median study duration was 54.2 months [interquartile range (IQR) 19.0-95.1].

SETTING

Tertiary academic hospital.

PATIENTS

360 patients were identified prospectively by referral to our center for management or surveillance of PCC/PGL. The median age was 59 years (IQR 45-67) and 56.4% (n = 203) were female.

MAIN OUTCOME MEASURES

The primary and secondary outcomes were weight and HbA1c, respectively.

RESULTS

On multivariable MEM, norepinephrine (P < 0.0005) negatively correlated with weight when all catecholamines and their derivatives were tried in the model, and normetanephrine (P < 0.0005) correlated when only metanephrines were included. In the surgical cohort (n = 272), normetanephrine decreased postoperatively and was inversely associated with weight (P < 0.0005). Elevated norepinephrine or normetanephrine at the study termination, indicative of metastatic and/or recurrent disease (MRD), correlated with weight loss. Norepinephrine and normetanephrine (P < 0.0005) directly correlated with HbA1c.

CONCLUSION

Plasma norepinephrine and its metabolite directly correlate with HbA1c and inversely correlate with weight in PCC/PGL. After resection, declining normetanephrine levels correlate with improving HbA1c despite an increase in patient body weight. Persistently elevated catecholamines and decreasing weight are observed in MRD.

High throughput proteomic and metabolic profiling identified target correction of metabolic abnormalities as a novel therapeutic approach in head and neck paraganglioma

Head and neck paragangliomas (HNPGLs) are rare neoplasms that represent difficult treatment paradigms in neurotology. Germline mutations in genes encoding succinate dehydrogenase (SDH) are the cause of nearly all familial HNPGLs. However, the molecular mechanisms underlying tumorigenesis remain unclear. Mutational analysis identified 6 out of 14 HNPGLs harboring clinicopathologic SDH gene mutations. The SDHB gene was most frequently mutated in these patients, and western blot showed loss of SDHB protein in tumors with SDHB mutations. The paraganglioma cell line (PGL-626) was established from a sample that harbored a missense SDHB mutation (c.649C > T). Spectrometric analysis using tandem mass tags identified 151 proteins significantly differentially expressed in HNPGLs compared with normal nerves. Bioinformatics analyses confirmed the high level of enrichment of oxidative phosphorylation and metabolism pathways in HNPGLs. The mitochondrial complex subunits NDUFA2, NDUFA10, and NDUFA4, showed the most significantly increased expression and were localized predominantly in the cytoplasm of PGL-626 cells. The mitochondrial complex I inhibitor metformin exerted dose-dependent inhibitory effects on PGL-626 cells via cooperative down-regulation of NDUFA2, 4, and 10, with a significant decrease in the levels of reactive oxygen species and mitochondrial membrane potential. Further metabolomic analysis of PGL-626 cells showed that metabolites involved in central carbon metabolism in cancer and sphingolipid signaling pathways, pantothenate and CoA biosynthesis, and tryptophan and carbon metabolism were significantly altered after metformin treatment. Thus, this study provides insights into the molecular mechanisms underlying HNPGL tumorigenesis and identifies target correction of metabolic abnormalities as a novel therapeutic approach for this disease.

Metabolic profiling analysis for clinical urine of colorectal cancer

AIM

To demonstrate the little-known metabolic changes and pathways in patients with colorectal cancer (CRC).

METHODS

We used gas chromatography time-of-flight mass spectrometry (GC-TOF/MS) to perform metabolic profiling of urine samples from 163 consecutive patients with CRC and 111 healthy controls without history of gastrointestinal tumors. The metabolic profiles were assayed using multivariate statistical analysis and one-way analysis of variance, and further analyzed to identify potential marker metabolites related to CRC. The GC-TOF/MS-derived models showed clear discriminations in metabolic profiles between the CRC group and healthy control group.

RESULTS

We demonstrated that 15 metabolites contributed to the differences. Among them, eleven metabolites were significantly upregulated, while other four metabolites were downregulated in the urine samples of CRC patients compared with healthy controls. Pathway analysis revealed changes in energy metabolism of patients with CRC, which are reflected in the upregulation of glycolysis and amino acid metabolism and the downregulation of lipid metabolism. Our study revealed the metabolic profile of urine from CRC patients and indicated that GC-TOF/MS-based methods can distinguish CRC from healthy controls.

CONCLUSION

GC-TOF/MS-based metabolomics has the potential to be developed into a novel, non-invasive, and painless clinical tool for CRC diagnosis, and may contribute to an improved understanding of disease mechanisms.

Accuracy and uniformity of the nomenclature of biogenic amines and polyamines in metabolomics studies: A preliminary study

Metabolomics is one of the newest areas in biochemistry dedicated to investigating small biomolecules in biofluids, tissues, and cells. Cutting edge instruments used in metabolomics studies make it possible to identify thousands of biomolecules and determine their interactions with biological networks. This tremendous area has increased the significance of accurate chemical nomenclature of compounds. Therefore, the classification of the organic molecules has become one of the most important issues in the field. Biogenic amines are nitrogenous compounds of low molecular weight formed by the decarboxylation of amino acids or by the amination and the transamination of aldehydes and ketones during normal metabolic processes. This letter covers the topic of nomenclature with respect to the current usage of biogenic amines in scientific literature. We use metabolomics as an example of field reporting data on trace levels of molecules that may be miscategorized in primary literature. We suggest that the incorrect classification of molecules will influence science education adversely because resources used for teaching are drawn from primary literature references that may contain errors.

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.

Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet.

OBJECTIVE

Evaluation of quantitative metabolomics as a diagnostic tool for PPGL.

DESIGN

Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study.

PATIENTS

Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded.

RESULTS

Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling.

CONCLUSIONS

The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.

Confirmatory testing of primary aldosteronism with saline infusion test and LC-MS/MS

OBJECTIVE

Saline infusion testing (SIT) for confirmation of primary aldosteronism (PA) is based on impaired aldosterone suppression in PA compared to essential hypertension (EH). In the past, aldosterone was quantified using immunoassays (IA). Liquid chromatography tandem mass spectrometry (LC-MS/MS) is increasingly used in clinical routine. We aimed at a method-specific aldosterone threshold for the diagnosis of PA during SIT and explored the diagnostic utility of steroid panel analysis.

DESIGN

Retrospective cohort study of 187 paired SIT samples (2009-2018). Diagnosis of PA (n = 103) and EH (n = 84) was established based on clinical routine workup without using LC-MS/MS values.

SETTING

Tertiary care center.

METHODS

LC-MS/MS using a commercial steroid panel. Receiver operator characteristics analysis was used to determine method-specific cut-offs using a positive predictive value (PPV) of 90% as criterion.

RESULTS

Aldosterone measured by IA was on average 31 ng/L higher than with LC-MS/MS. The cut-offs for PA confirmation were 54 ng/L for IA (sensitivity: 95%, 95% CI: 89.0-98.4; specificity: 87%, 95% CI: 77.8-93.3; area under the curve (AUC): 0.955, 95% CI: 0.924-0.986; PPV: 90%, 95% CI: 83.7-93.9) and 69 ng/L for LC-MS/MS (79%, 95% CI: 69.5-86.1; 89%, 95% CI: 80.6-95.0; 0.902, 95% CI: 0.857-0.947; 90%, 95% CI: 82.8-94.4). Other steroids did not improve SIT.

CONCLUSIONS

Aldosterone quantification with LC-MS/MS and IA yields comparable SIT-cut-offs. Lower AUC for LC-MS/MS is likely due to the spectrum of disease in PA and previous decision making based on IA results. Until data of a prospective trial with clinical endpoints are available, the suggested cut-off can be used in clinical routine.