Lipodystrophy: metabolic insights from a rare disorder

Isabel Huang-Doran, Alison Sleigh, Justin J Rochford, Stephen O'Rahilly, David B Savage

Research output: Contribution to journalLiterature reviewpeer-review

174 Citations (Scopus)

Abstract

Obesity, insulin resistance and their attendant complications are among the leading causes of morbidity and premature mortality today, yet we are only in the early stages of understanding the molecular pathogenesis of these aberrant phenotypes. A powerful approach has been the study of rare patients with monogenic syndromes that manifest as extreme phenotypes. For example, there are striking similarities between the biochemical and clinical profiles of individuals with excess fat (obesity) and those with an abnormal paucity of fat (lipodystrophy), including severe insulin resistance, dyslipidaemia, hepatic steatosis and features of hyperandrogenism. Rare lipodystrophy patients therefore provide a tractable genetically defined model for the study of a prevalent human disease phenotype. Indeed, as we review herein, detailed study of these syndromes is beginning to yield valuable insights into the molecular genetics underlying different forms of lipodystrophy, the essential components of normal adipose tissue development and the mechanisms by which disturbances in adipose tissue function can lead to almost all the features of the metabolic syndrome. Journal of Endocrinology (2010) 207, 245-255

Original languageEnglish
Pages (from-to)245-255
Number of pages11
JournalJournal of Endocrinology
Volume207
Issue number3
Early online date24 Sept 2010
DOIs
Publication statusPublished - 1 Dec 2010

Keywords

  • familial partial lipodystrophy
  • congenital generalized lipodystrophy
  • severe insulin-resistance
  • activated-receptor-gamma
  • fatty-acid-metabolism
  • of-the-literature
  • adipose-tissue
  • adipocyte differentiation
  • ppar-gamma
  • lamin A/C

Fingerprint

Dive into the research topics of 'Lipodystrophy: metabolic insights from a rare disorder'. Together they form a unique fingerprint.

Cite this