Abstract
BackgroundNeuronal ceroid lipofuscinoses (NCL) are heterogeneous neurodegenerative disorders. A better understanding of genotype–phenotype–histology correlation is expected to improve patient care and enhance understanding for phenotypic variability. This meta-analysis studies the correlation of NCL genotypes with clinical phenotypes, ages of onset, and pathologic findings. MethodsA structured MEDLINE search was performed using search strings incorporating relevant Medical Subject Headings (MeSH) terms. Studies of NCL patients with genetic, clinical, and histologic data were included. Individual patient data were extracted. Chi-square statistic was used to test the genotype differences in clinical phenotypes and histology. The distribution of age(s) of onset as a function of genotype was explored. Pairwise comparisons were performed with robust analysis of variance. ResultsSixty-eight studies including a total of 440 individuals with NCL were analyzed. Genetic testing was performed on 395 patients, and a pathologic mutation was identified in 372 of 395 of them. A significant clustering of genotypes into juvenile-onset (only CLN3) and infantile-onset (all others) phenotypes was observed (P < 0.0001). However, the CLN6 genotype showed a bimodal onset and included 14 of 17 subjects with the adult-onset phenotype. The estimated age of onset was respectively lower for subjects with CLN1 mutation (3.01 years, 95% confidence interval [CI] = 2.54 to 3.49) and higher for those with CLN6 mutation (16.33 years, 95% CI = 15.68 to 16.98), compared with other genotypes (P < 0.05 for pairwise comparisons). There was a significant (P < 0.0001) clustering of genotype observed according to the sampled tissue types and electron microscopic findings. ConclusionsNCL genotypes significantly differ in terms of ages of onset and clinical phenotypes. There is a distinct segregation of genotypes and electron microscopic findings and high-yield tissue types for pathologic study. This information can possibly facilitate testing and diagnosis in resource-limited settings.
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