Clinical Predictors of Pseudohypoxia-Type Pheochromocytomas

Main mechanisms and clinical implications of alterations in energy expenditure state among patients with pheochromocytoma and paraganglioma: A review

Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors with diverse clinical presentations. Alterations in energy expenditure state are commonly observed in patients with PPGL. However, the reported prevalence of hypermetabolism varies significantly and the underlying mechanisms and implications of this presentation have not been well elucidated. This review discusses and analyzes the factors that contribute to energy consumption. Elevated catecholamine levels in patients can significantly affect substance and energy metabolism. Additionally, changes in the activation of brown adipose tissue (BAT), inflammation, and the inherent energy demands of the tumor can contribute to increased resting energy expenditure (REE) and other energy metabolism indicators. The PPGL biomarker, chromogranin A (CgA), and its fragments also influence energy metabolism. Chronic hypermetabolic states may be detrimental to these patients, with surgical tumor removal remaining the primary therapeutic intervention. The high energy expenditure of PPGL has not received the attention it deserves, and an accurate assessment of energy metabolism is the cornerstone for an adequate understanding and treatment of the disease.

Development and internal validation of a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas

Aim

To develop and internally validate a novel predictive model for SDHB mutations in pheochromocytomas and retroperitoneal paragangliomas (PPGLs).

Methods

Clinical data of patients with PPGLs who presented to Peking Union Medical College Hospital from 2013 to 2022 and underwent genetic testing were retrospectively collected. Variables were screened by backward stepwise and clinical significance and were used to construct multivariable logistic models in 50 newly generated datasets after the multiple imputation. Bootstrapping was used for internal validation. A corresponding nomogram was generated based on the model. Sensitivity analyses were also performed.

Results

A total of 556 patients with PPGLs were included, of which 99 had a germline SDHB mutation. The prediction model revealed that younger age of onset [Odds ratio (OR): 0.93, 95% CI: 0.91-0.95], synchronous metastasis (OR: 6.43, 95% CI: 2.62-15.80), multiple lesion (OR: 0.22, 95% CI: 0.09-0.54), retroperitoneal origin (OR: 5.72, 95% CI: 3.13-10.47), negative 131I-meta-iodobenzylguanidine (MIBG) (OR: 0.34, 95% CI: 0.15-0.73), positive octreotide scintigraphy (OR: 3.24, 95% CI: 1.25-8.43), elevated 24h urinary dopamine (DA) (OR: 1.72, 95% CI: 0.93-3.17), NE secretory type (OR: 2.83, 95% CI: 1.22- 6.59), normal secretory function (OR: 3.04, 95% CI: 1.04-8.85) and larger tumor size (OR: 1.09, 95% CI: 0.99-1.20) were predictors of SDHB mutations in PPGLs, and showed good and stable predictive performance with a mean area under the ROC curve (AUC) of 0.865 and coefficient of variation of 2.2%.

Conclusions

This study provided a novel and useful tool for predicting SDHB mutations by integrating easily obtained clinical data. It may help clinicians select suitable genetic testing methods and make appropriate clinical decisions for these high-risk patients.

Development and validation of a novel nomogram predicting pseudohypoxia type pheochromocytomas and paragangliomas

PURPOSE

Pseudohypoxia type (PHT) pheochromocytomas and paragangliomas (PPGLs) are more likely to metastasize and have a poor prognosis. However, application of genetic tests has many restrictions. The study aims to establish a novel nomogram for predicting the risk of PHT PPGLs.

METHODS

This retrospective cross-sectional study included 242 patients with pathology confirmed PPGLs in one tertiary care center in China in 2010-2021. Clinical and biochemical characteristics were collected. Next-generation sequencing was performed in all PPGLs patients for detection of mutation. Univariate and multivariable logistic regression analyses were used to select risk factors for constructing the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination of the nomogram and the calibration curve was performed.

RESULTS

Four variables including age ≤ 35 years, hypertension, 24 h urinary output of urinary vanillylmandelic acid (VMA) ≥ 100 umol/24 h and urinary 17-ketosteroide (17 KS) ≤ 50 umol/24 h levels were independently associated with PHT PPGLs in the logistic regression analysis and were included in the nomogram. The nomogram showed a good discrimination performance with AUC of 0.829 [95% confidence interval (CI), 0.767-0.891] in the training set and 0.797 (95%CI, 0.659-0.935) in the validation set, respectively. The calibration curve showed a bias-corrected AUC of 0.809 vs. 0.795, and a Hosmer-Lemeshow (H-L) test yielded a p value of 0.801 vs. 0.885, indicating the nomogram's good ability to distinguish PHT PPGLs from non-PHT PPGLs.

CONCLUSION

Our study has proposed a novel nomogram for individualized prediction of the PHT PPGLs, which may make contributions to guide the patients' personalized management, follow-up, and treatment.