Prolactin is a polypeptide hormone that is well known for its role in reproductive physiology. Recent studies highlight its role in neurohormonal appetite regulation and metabolism. Elevated prolactin levels are widely associated with worsening metabolic disease, but it appears that low prolactin levels could also be metabolically unfavorable. This review discusses the pathophysiology of prolactin related metabolic changes, and the less commonly recognized effects of prolactin on adipose tissue, pancreas, liver, and small bowel. Furthermore, the effect of dopamine agonists on the metabolic profiles of patients with hyperprolactinemia are discussed as well.

Endogenous Cushing’s syndrome (CS) is a rare disease characterized by prolonged glucocorticoid excess. Timely diagnosis is critical to allow prompt treatment and limit long-term disease morbidity and risk for mortality. Traditional biochemical diagnostic modalities each have limitations and sensitivities and specificities that vary significantly with diagnostic cutoff values. Biochemical evaluation is particularly complex in patients whose hypercortisolemia fluctuates daily, often requiring repetition of tests to confirm or exclude disease, and when delineating CS from physiologic, nonneoplastic states of hypercortisolism. Lastly, traditional pituitary MRI may be negative in up to 60% of patients with adrenocorticotropic hormone (ACTH)-secreting pituitary adenomas (termed “Cushing’s disease” [CD]) whereas false positive pituitary MRI findings may exist in patients with ectopic ACTH secretion. Thus, differentiating CD from ectopic ACTH secretion may necessitate dynamic testing or even invasive procedures such as bilateral inferior petrosal sinus sampling. Newer methods may relieve some of the diagnostic uncertainty in CS, providing a more definitive diagnosis prior to subjecting patients to additional imaging or invasive procedures. For example, a novel method of cortisol measurement in patients with CS is scalp hair analysis, a non-invasive method yielding cortisol and cortisone values representing long-term glucocorticoid exposure of the past months. Hair cortisol and cortisone have both shown to differentiate between CS patients and controls with a high sensitivity and specificity. Moreover, advances in imaging techniques may enhance detection of ACTH-secreting pituitary adenomas. While conventional pituitary MRI may fail to identify microadenomas in patients with CD, high-resolution 3T-MRI with 3D-spoiled gradient-echo sequence has thinner sections and superior soft-tissue contrast that can detect adenomas as small as 2 mm. Similarly, functional imaging may improve the identification of ACTH-secreting adenomas noninvasively; Gallium-68-tagged corticotropin-releasing hormone (CRH) combined with PET-CT can be used to detect CRH receptors, which are upregulated on corticotroph adenomas. This technique can delineate functionality of adenomas in patients with CD from patients with ectopic ACTH secretion and false positive pituitary lesions on MRI. Here, we review emerging methods and imaging modalities for the diagnosis of CS, discussing their diagnostic accuracy, strengths and limitations, and applicability to clinical practice.

Purpose Both prolactinomas and nonfunctioning adenomas (NFAs) can present with hyperprolactinemia. Distinguishing them is critical because prolactinomas are effectively managed with dopamine agonists, whereas compressive NFAs are treated surgically. Current guidelines rely only on serum prolactin (PRL) levels, which are neither sensitive nor specific enough. Recent studies suggest that accounting for tumor volume may improve diagnosis. The objective of this study is to investigate the diagnostic utility of PRL, tumor volume, and imaging features in differentiating prolactinoma and NFA. Methods Adult patients with pathologically confirmed prolactinoma (n = 21) or NFA with hyperprolactinemia (n = 58) between 2013 and 2020 were retrospectively identified. Diagnostic performance of clinical and imaging variables was analyzed using receiver-operating characteristic curves to calculate area under the curve (AUC). Results Tumor volume and PRL positively correlated for prolactinoma (r = 0.4839, p = 0.0263) but not for NFA (r = 0.0421, p = 0.7536). PRL distinguished prolactinomas from NFAs with an AUC of 0.8892 (p < 0.0001) and optimal cut-off value of 62.45 ng/ml, yielding a sensitivity of 85.71% and specificity of 94.83%. The ratio of PRL to tumor volume had an AUC of 0.9647 (p < 0.0001) and optimal cut-off value of 21.62 (ng/ml)/cm³ with sensitivity of 100% and specificity of 82.76%. Binary logistic regression found that PRL was a significant positive predictor of prolactinoma diagnosis, whereas tumor volume, presence of CSI not previously defined, and T2 hyperintensity were significant negative predictors. The regression model had an AUC of 0.9915 (p < 0.0001). Conclusions Consideration of tumor volume improves differentiation between prolactinomas and NFAs, which in turn leads to effective management.

Background: Pituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs can be challenging to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories. Methods: In this study, we analyzed clinical, immunohistochemical, and DNA methylation data of 118 PitNETs to develop a clinicomolecular approach to classifying PitNETs and identifying epigenetic classes. Results: CNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the 3 lineages when the calibrated score is ≥ 0.3. Unsupervised DNA methylation analysis separated PitNETs into 2 major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation. Conclusion: Our findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.