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Lipodystrophy and severe insulin resistance syndrome: Epidemiological data from a French national rare diseases
Marie-Christine Vantyghem1, Estelle Nobécourt2, Camille Vatier3
1University Hospital of Lille, CHU Lille, Department of Endocrinology, Diabetology, and Metabolism, University of Lille, Inserm U1190 Translational Research for Diabetes, Pasteur Institute of Lille, Lille, France.
Context:
Lipodystrophy and extreme insulin resistance syndrome are rare diseases with severe metabolic complications. Reported epidemiological data are conflicting.
Objective:
We aimed to evaluate nationwide and regional prevalences of lipodystrophy and insulin resistance syndrome in France, and to study diagnostic and care-pathways.
Methods:
We studied data from the French National Rare Disease Registry (BNDMR), which includes all patients attending rare disease reference centers in France. We analyzed demographic data and age at first signs and at diagnosis in patients with an ORPHA code of lipodystrophy or severe insulin resistance syndrome.
Results:
The number of patients registered with lipodystrophy/insulin resistance syndrome doubled from 2017 to 2023, with the deployment of nationwide epidemiological tools in specialized rare diseases centers. Currently, 567 of the 652 patients (58% female) had a diagnosis of genetically determined disease with generalized (GL) or partial lipodystrophy (PL), giving an estimated national prevalence of 1.6 and 6.4 per million, respectively. Wide regional differences in estimated prevalence may be partly due to founder pathogenic variants, or local spread of clinical skills and knowledge. Median age at first signs and diagnosis were respectively 1 [IQR: 1-3] and 5 years [0-20] for GL, and 22 [14-35] and 40 years [25-52] for PL, with earlier first signs of PL in women than in men.
Conclusion:
The estimated prevalence of genetic lipodystrophy/extreme insulin resistance syndrome has increases with the use of dedicated epidemiological tools, suggesting persistent underdiagnosis. Knowledge of these diseases needs to be improved to decrease diagnostic delay and reduce regional and gender-associated diagnostic disparities.
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