The Clinical and Mutational Spectrum of Bardet-Biedl Syndrome in Saudi Arabia.
Doaa MilibariSawsan R NowilatyRola Ba-AbbadPublished in: Genes (2024)
The retinal features of Bardet-Biedl syndrome (BBS) are insufficiently characterized in Arab populations. This retrospective study investigated the retinal features and genotypes of BBS in Saudi patients managed at a single tertiary eye care center. Data analysis of the identified 46 individuals from 31 families included visual acuity (VA), systemic manifestations, multimodal retinal imaging, electroretinography (ERG), family pedigrees, and genotypes. Patients were classified to have cone-rod, rod-cone, or generalized photoreceptor dystrophy based on the pattern of macular involvement on the retinal imaging. Results showed that nyctalopia and subnormal VA were the most common symptoms with 76% having VA ≤ 20/200 at the last visit (age: 5-35). Systemic features included obesity 91%, polydactyly 56.5%, and severe cognitive impairment 33%. The predominant retinal phenotype was cone-rod dystrophy 75%, 10% had rod-cone dystrophy and 15% had generalized photoreceptor dystrophy. ERGs were undetectable in 95% of patients. Among the 31 probands, 61% had biallelic variants in BBSome complex genes, 32% in chaperonin complex genes, and 6% had biallelic variants in ARL6 ; including six previously unreported variants. Interfamilial and intrafamilial variabilities were noted, without a clear genotype-phenotype correlation. Most BBS patients had advanced retinopathy and were legally blind by early adulthood, indicating a narrow therapeutic window for rescue strategies.
Keyphrases
- end stage renal disease
- optical coherence tomography
- ejection fraction
- diabetic retinopathy
- chronic kidney disease
- healthcare
- cognitive impairment
- early onset
- high resolution
- type diabetes
- gene expression
- adipose tissue
- body mass index
- electronic health record
- transcription factor
- mass spectrometry
- insulin resistance
- depressive symptoms
- weight loss
- photodynamic therapy
- machine learning
- intellectual disability
- weight gain
- patient reported
- big data
- bioinformatics analysis