A review of genetic forms of severe insulin resistance and lipodystrophy suggests a revised classification based on underlying pathophysiology to improve diagnosis and clinical management.
This review provides an updated understanding and a revised classification of rare monogenic defects causing severe insulin resistance and lipodystrophy to aid in clinical diagnosis and management.
Insulin resistance is among the most prevalent endocrine derangements in the world, and it is closely associated with major diseases of global reach including diabetes mellitus, atherosclerosis, nonalcoholic fatty liver disease, and ovulatory dysfunction. It is most commonly found in those with obesity but may also occur in an unusually severe form in rare patients with monogenic defects. Such patients may loosely be grouped into those with primary disorders of insulin signaling and those with defects in adipose tissue development or function (lipodystrophy). The severe insulin resistance of both subgroups puts patients at risk of accelerated complications and poses severe challenges in clinical management. However, the clinical disorders produced by different genetic defects are often biochemically and clinically distinct and are associated with distinct risks of complications. This means that optimal management of affected patients should take into account the specific natural history of each condition. In clinical practice, they are often underdiagnosed, however, with low rates of identification of the underlying genetic defect, a problem compounded by confusing and overlapping nomenclature and classification. We now review recent developments in understanding of genetic forms of severe insulin resistance and/or lipodystrophy and suggest a revised classification based on growing knowledge of the underlying pathophysiology.
Semple et al. (Mon,) conducted a review in Severe Insulin Resistance and Lipodystrophy. A review of genetic forms of severe insulin resistance and lipodystrophy suggests a revised classification based on underlying pathophysiology to improve diagnosis and clinical management.