Journal article
Multiple endocrine neoplasia type 1 (MEN1): recommendations and guidelines for best practice
- Abstract:
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Multiple endocrine neoplasia type 1 (MEN1) is characterised by combined occurrence of parathyroid tumours, duodenopancreatic neuroendocrine tumours, and anterior pituitary adenomas. Some patients might also develop thymic and bronchopulmonary neuroendocrine tumours, and adrenal tumours. MEN1 is an autosomal dominant disorder caused by mutations in the tumour-suppressor gene MEN1, which encodes a scaffold protein, menin. Without treatment, patients with MEN1 have high morbidity and premature mortality, which can be mitigated by early tumour detection and intervention. Identification of individuals at high risk for MEN1 can be facilitated by genetic testing of patients and their first-degree relatives, and undertaking periodic clinical, biochemical, and radiological screening in patients and MEN1 mutation carriers. However, no consensus exists regarding the optimal assessment and management of MEN1. To provide such recommendations, a multidisciplinary group was convened to undertake systematic reviews and a meta-analysis of the literature, and to use a Delphi approach for the development of consensus statements. 55 clinical recommendations were developed to guide clinicians, patients, and stakeholders about approaches for MEN1 in adults and children.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 5.3MB, Terms of use)
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- Publisher copy:
- 10.1016/S2213-8587(25)00119-6
- Publisher:
- Elsevier
- Journal:
- Lancet Diabetes and Endocrinology More from this journal
- Volume:
- 13
- Issue:
- 8
- Pages:
- 699-721
- Publication date:
- 2025-06-13
- Acceptance date:
- 2025-04-15
- DOI:
- EISSN:
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2213-8595
- ISSN:
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2213-8587
- Language:
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English
- Pubs id:
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2119649
- Local pid:
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pubs:2119649
- Deposit date:
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2025-04-24
Terms of use
- Copyright holder:
- Elsevier Ltd.
- Copyright date:
- 2025
- Rights statement:
- Copyright: © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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