LipoDDx: a mobile application for identification of rare lipodystrophy syndromes

BACKGROUND: Lipodystrophy syndromes are a group of disorders characterized by a loss of adipose tissue once other situations of nutritional deprivation or exacerbated catabolism have been ruled out. With the exception of the HIV-associated lipodystrophy, they have a very low prevalence, which together with their large phenotypic heterogeneity makes their identification difficult, even for endocrinologists and pediatricians. This leads to significant delays in diagnosis or even to misdiagnosis. Our group has developed an algorithm that identifies the more than 40 rare lipodystrophy subtypes described to date. This algorithm has been implemented in a free mobile application, LipoDDx(R). Our aim was to establish the effectiveness of LipoDDx(R). Forty clinical records of patients with a diagnosis of certainty of most lipodystrophy subtypes were analyzed, including subjects without lipodystrophy. The medical records, blinded for diagnosis, were evaluated by 13 physicians, 1 biochemist and 1 dentist. Each evaluator first gave his/her results based on his/her own criteria. Then, a second diagnosis was given using LipoDDx(R). The results were analysed based on a score table according to the complexity of each case and the prevalence of the disease. RESULTS: LipoDDx(R) provides a user-friendly environment, based on usually dichotomous questions or choice of clinical signs from drop-down menus. The final result provided by this app for a particular case can be a low/high probability of suffering a particular lipodystrophy subtype. Without using LipoDDx(R) the success rate was 17 +/- 20%, while with LipoDDx(R) the success rate was 79 +/- 20% (p < 0.01). CONCLUSIONS: LipoDDx(R) is a free app that enables the identification of subtypes of rare lipodystrophies, which in this small cohort has around 80% effectiveness, which will be of help to doctors who are not experts in this field. However, it will be necessary to analyze more cases in order to obtain a more accurate efficiency value

Celia's encephalopathy and c.974dupG in BSCL2 gene: a hidden change in a known variant

Celia's encephalopathy (progressive encephalopathy with/without lipodystrophy (PELD)) is a childhood neurodegenerative disorder with a fatal prognosis before the age of 10, due to the variant c.985C>T in the BSCL2 gene that causes a cryptic splicing site leading to skipping of exon 7. For years, different authors have reported cases of congenital generalized lipodystrophy due to the variant c.974dupG in BSCL2 associated with neurological manifestations of variable severity, although some of them clearly superimposable to PELD. To identify the molecular mechanisms responsible for these neurological alterations in two patients with c.974dupG. Clinical characterization, biochemistry, and neuroimaging studies of two girls carrying this variant. In silico analysis, PCR amplification, and BSCL2 cDNA sequencing. BSCL2-201 transcript expression, which lacks exon 7, by qPCR in fibroblasts from the index case, from a healthy child as a control and from two patients with PELD, and in leukocytes from the index case and her parents. One with a severe encephalopathy including a picture of intellectual deficiency, severe language impairment, myoclonic epilepsy, and lipodystrophy as described in PELD, dying at 9 years and 9 months of age. The other 2-year-old patient showed incipient signs of neurological involvement. In silico and cDNA sequencing studies showed that variant c.974dupG gives rise to skipping of exon 7. The expression of BSCL2-201 in fibroblasts was significantly higher in the index case than in the healthy child, although less than in the case with homozygous PELD due to c.985C>T variant. The expression of this transcript was approximately half in the healthy carrier parents of this patient. The c.974dupG variant leads to the skipping of exon 7 of the BSCL2 gene and is responsible for a variant of Celia's encephalopathy, with variable phenotypic expression