Genes 2013, 4(4), 522-535; doi:10.3390/genes4040522
Review

Monogenic Diabetes: A Diagnostic Algorithm for Clinicians

1 Endocrine, Diabetes and Research Centre, Wellington Regional Hospital, Private Bag 7902, Newtown, Wellington 6021, New Zealand 2 Department of Medicine, University of Otago, Newtown, Wellington 6021, New Zealand 3 Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
* Author to whom correspondence should be addressed.
Received: 15 July 2013; in revised form: 30 August 2013 / Accepted: 2 September 2013 / Published: 26 September 2013
(This article belongs to the Special Issue Genetics of Diabetes)
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Abstract: Monogenic forms of beta cell diabetes account for approximately 1%–2% of all cases of diabetes, yet remain underdiagnosed. Overlapping clinical features with common forms of diabetes, make diagnosis challenging. A genetic diagnosis of monogenic diabetes in many cases alters therapy, affects prognosis, enables genetic counseling, and has implications for cascade screening of extended family members. We describe those types of monogenic beta cell diabetes which are recognisable by distinct clinical features and have implications for altered management; the cost effectiveness of making a genetic diagnosis in this setting; the use of complementary diagnostic tests to increase the yield among the vast majority of patients who will have commoner types of diabetes which are summarised in a clinical algorithm; and the vital role of cascade genetic testing to enhance case finding.
Keywords: monogenic; diabetes; maturity onset diabetes of the young; neonatal diabetes; genetic testing

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MDPI and ACS Style

Carroll, R.W.; Murphy, R. Monogenic Diabetes: A Diagnostic Algorithm for Clinicians. Genes 2013, 4, 522-535.

AMA Style

Carroll RW, Murphy R. Monogenic Diabetes: A Diagnostic Algorithm for Clinicians. Genes. 2013; 4(4):522-535.

Chicago/Turabian Style

Carroll, Richard W.; Murphy, Rinki. 2013. "Monogenic Diabetes: A Diagnostic Algorithm for Clinicians." Genes 4, no. 4: 522-535.

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