Are prolactin levels efficient in predicting a pituitary lesion in patients with hyperprolactinemia?

Hyperprolactinemia Pituitary lesion Prolactin Sellar mass Serial sampling Stalk-effect

Journal

Endocrine
ISSN: 1559-0100
Titre abrégé: Endocrine
Pays: United States
ID NLM: 9434444

Informations de publication

Date de publication:
09 Jan 2024
Historique:
received: 16 11 2023
accepted: 25 12 2023
medline: 9 1 2024
pubmed: 9 1 2024
entrez: 9 1 2024
Statut: aheadofprint

Résumé

Data regarding the presence of a prolactin (PRL) threshold above which a pituitary magnetic resonance imaging (MRI) is mandatory in patients with hyperprolactinemia (hyperPRL) are controversial and derived primarily from studies focused on female populations. Aim of our study was to evaluate in a cohort of patients of both sexes with confirmed hyperPRL, the possible correlation between PRL values and the presence of pituitary abnormalities. We retrospectively analyzed data from patients who underwent serial PRL sampling at our Division between January 2015 and December 2022. Patients diagnosed with monomeric hyperPRL at serial sampling and with subsequent contrast-enhanced MRI results available for the pituitary region were included in the study. Exclusion criteria were prior pituitary disease, severe renal insufficiency, liver cirrhosis, uncompensated primary hypothyroidism and ongoing therapy with hyperprolactinemic drugs. Physiological causes of hyperPRL were also ruled out. Out of the 1253 patients who underwent serial PRL sampling, 139 patients (101 women and 38 men) met the inclusion criteria: 106 (76.3%) patients had some form of pituitary disease, with microlesions observed in 69.8%, macrolesions in 25.5% and other findings in 4.7% of subjects. PRL values showed a modest accuracy in predicting the presence of a pituitary abnormality and the best cut-offs identified were >25 µg/L (AUC 0.767, p = 0.003) and >44.2 µg/L (AUC 0.697, p < 0.001) in men and women, respectively; however, if only patients with PRL values > 500 µg/L were excluded from the analysis, as they were already supposed to harbor a macroprolactinoma, PRL levels were not able to predict the presence of a macrolesion neither in men nor women. Given the high prevalence of pituitary abnormalities in patients of both sexes with hyperPRL at serial sampling, performing a pituitary imaging in all cases of hyperPRL, even if mild, appears to be a cautious choice.

Identifiants

pubmed: 38194218
doi: 10.1007/s12020-023-03678-z
pii: 10.1007/s12020-023-03678-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s).

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Auteurs

Emanuele Varaldo (E)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy. emanuele.varaldo@unito.it.

Daniela Cuboni (D)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Nunzia Prencipe (N)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Luigi Simone Aversa (LS)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Michela Sibilla (M)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Fabio Bioletto (F)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Alessandro Maria Berton (AM)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Valentina Gasco (V)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Ezio Ghigo (E)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Silvia Grottoli (S)

Division of Endocrinology, Diabetology and Metabolism; Department of Medical Sciences, University of Turin, Turin, Italy.

Classifications MeSH