The objective of the study was to evaluate the efficacy of second-line therapies in patients with acromegaly caused by a growth hormone (GH) and prolactin (PRL) co-secreting pituitary neuroendocrine tumor (GH&PRL-Pit-NET) compared to their efficacy in patients with acromegaly caused by a GH-secreting pituitary neuroendocrine tumor (GH-Pit-NET). This is a multicenter retrospective study of patients with acromegaly on treatment with pasireotide and/or pegvisomant. Patients were classified in two groups: GH&PRL-Pit-NETs when evidence of hyperprolactinemia and immunohistochemistry (IHC) for GH and PRL was positive or if PRL were >200 ng/dL regardless of the PRL-IHC and GH-Pit-NETs when the previously mentioned criteria were not met. A total of 28 cases with GH&PRL-Pit-NETs and 122 with GH-Pit-NETs met the inclusion criteria. GH&PRL-Pit-NETs presented at a younger age, caused hypopituitarism, and were invasive more frequently than GH-Pit-NETs. There were 124 patients treated with pegvisomant and 49 with pasireotide at any time. The efficacy of pegvisomant for IGF-1 normalization was of 81.5% and of pasireotide of 71.4%. No differences in IGF-1 control with pasireotide and with pegvisomant were observed between GH&PRL-Pit-NETs and GH-Pit-NETs. All GH&PRL-Pit-NET cases treated with pasireotide (n = 6) and 82.6% (n = 19/23) of the cases treated with pegvisomant normalized PRL levels. No differences in the rate of IGF-1 control between pegvisomant and pasireotide were detected in patients with GH&PRL-Pit-NETs (84.9% vs 66.7%, P = 0.178). We conclude that despite the more aggressive behavior of GH&PRL-Pit-NETs than GH-Pit-NETs, no differences in the rate of IGF-1 control with pegvisomant and pasireotide were observed between both groups, and both drugs have shown to be effective treatments to control IGF-1 and PRL hypersecretion in these tumors.

The objective of our study was to determine the prevalence of glycemic disorders (diabetes mellitus and prediabetes) in patients with pheochromocytomas and sympathetic paragangliomas (PPGLs) and identify risk factors for their development and the likelihood of their resolution after surgery. A multicentric retrospective study of patients with PPGLs submitted to surgery between 2000 and 2021 in 17 Spanish hospitals was performed. Diabetes-specific data were collected at diagnosis, in the immediate- and long-term postsurgical follow-up. A total of 229 patients with PPGLs were included (218 with pheochromocytomas and 11 with sympathetic paragangliomas). Before surgery, glycemic disorders were diagnosed in 35.4% of the patients (n = 81): 54 with diabetes and 27 with prediabetes. The variables independently associated with a higher risk of glycemic disorders were sporadic PPGL (odds ratio (OR) = 3.26 (1.14–9.36)) and hypertension (OR = 3.14 (1.09–9.01)). A significant decrease in fasting plasma glucose and HbA1c levels was observed after surgery, in the short-term and long-term follow-up (P < 0.001). After a median follow-up of 48.5 months (range 3.3–168.9), after surgery, 52% of diabetic and 68% of prediabetic patients experienced a complete resolution. Lower body mass index (BMI) (P = 0.001), lower glucose levels (P = 0.047) and shorter duration of diabetes prior to surgery (P = 0.021) were associated with a higher probability of diabetes resolution. In conclusion, glycemic disorders in patients with PPGLs are present in more than a third of them at diagnosis. Sporadic PPGLs and hypertension are risk factors for their development. More than 50% of cases experience a complete resolution of the glycemic disorder after resection of the PPGLs.

Introduction: It is estimated that 30–40% of patients with apparently sporadic pheochromocytomas (PHEOs) have an inherited predisposition syndrome. The aim of our study was to develop a predictive model of hereditary PHEO based on the clinical, hormonal, and radiological features present at the diagnosis of patients with PHEOs. Methods: A retrospective multicenter cohort study of patients with PHEOs with available genetic study from 18 tertiary hospitals. Clinical, biochemical, and radiological features were used to build a multivariate logistic regression model. The estimation of all possible equations was used to select the model with the best diagnostic accuracy (lower Akaike index). Results: A total of 245 patients were included: 169 (69.0%) patients with sporadic PHEOs and 76 (31%) with hereditary PHEOs. The parsimonious predictive model with the highest diagnostic accuracy for the prediction of hereditary PHEO combined the variables age, non-cardiovascular disease, urinary norepinephrine levels, and tumor size. The area under the ROC curve of this model was 0.800 (0.705–0.887). Based on the predictive model, the probability of hereditary PHEO in patients older than 60 years with cardiovascular disease, high levels of urinary norepinephrine and unilateral PHEOs >60 mm was <2%. And if the age was above 80 years, lower than 1%. The probability of sporadic PHEO linearly increased with age (MH Test for linear Trend: χ2 (1) = 30.05; p < 0.001). Conclusion: In certain populations such as old patients with cardiovascular disease, with high levels of urinary norepinephrine and large tumors in which the probability of hereditary PHEO is very low, genetic testing could be avoided in the absence of specific suspicion.

Purpose To evaluate whether the clinical, biochemical and radiological features of patients with primary aldosteronism (PA) can predict both main subtypes of PA. Methods A retrospective multicenter study of PA patients followed in 27 Spanish tertiary hospitals (SPAIN-ALDO Register). Only patients with confirmed unilateral or bilateral PA based on adrenal venous sampling (AVS) and/or postsurgical biochemical cure after adrenalectomy were included. Supervised regression techniques were used for model development. Results 328 patients [270 unilateral PA (UPA), 58 bilateral PA (BPA)] were included. The area under the curve (AUC) for aldosterone/potassium ratio and aldosterone responses following saline infusion test were 0.602 [95%CI 0.520 to 0.684] and 0.574 [95% CI 0.446–0.701], respectively, to differentiate UPA from BPA. The AUC was 0.825 [95% 0.764–0.886] when the prediction model with seven parameters – comorbidities (dyslipidemia, cerebrovascular disease, sleep apnea syndrome [SAS]), systolic blood pressure (SBP), plasma aldosterone levels (PAC), hypokalemia and unilateral adrenal nodule >1 cm and normal contralateral adrenal gland on CT/MRI – was used. In patients without comorbidities, hypokalemia, SBP > 160 mmHg, PAC > 40 ng/dL, and unilateral adrenal lesions were associated with a likelihood of having a UPA of 98.5%. The chance of BPA was higher in individuals with comorbidities, SBP < 140 mmHg, normokalemia, low PAC levels, and no adrenal tumors on the CT/MRI (91.5%). Conclusion A combination of high PAC, SBP > 160 mmHg, low serum potassium, a unilateral adrenal nodule>1 cm and no comorbidities could predict a UPA with a 98.5% accuracy.