ReviewInternational journal of molecular sciences2020
Metabolomic Studies of Lipid Storage Disorders, with Special Reference to Niemann-Pick Type C Disease: A Critical Review with Future Perspectives.
Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- A multi-omics-empowered framework for precision diagnosis and treatment of lysosomal diseases.Journal of pharmaceutical analysis · 2025Review
- Heterozygosity in NPC may be associated with neurologic and systemic phenotypes.Frontiers in neurology · 2025Review
- Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery.Metabolites · 2023Article
- Proteomic analysis of s-acylated proteins in human retinal pigment epithelial cells and the role of palmitoylation of Niemann-Pick type C1 protein in cholesterol transport.Frontiers in aging neuroscience · 2022Article
- Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an UnbiasedCells · 2021Article
- The rapidly evolving view of lysosomal storage diseases.EMBO molecular medicine · 2021Review
- Analysis of Biomarkers for Congenital Heart Disease Based on Maternal Amniotic Fluid Metabolomics.Frontiers in cardiovascular medicine · 2021Article
- Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment.International journal of environmental research and public health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Lysosomal storage disorders (LSDs) are predominantly very rare recessive autosomal neurodegenerative diseases.Sphingolipidoses, a sub-group of LSDs, result from defects in lysosomal enzymes involved in sphingolipid catabolism, and feature disrupted storage systems which trigger complex pathogenic cascades with other organelles collaterally affected. This process leads to cell dysfunction and death, particularly in the central nervous system. One valuable approach to gaining insights into the global impact of lysosomal dysfunction is through metabolomics, which represents a discovery tool for investigating disease-induced modifications in the patterns of large numbers of simultaneously-analysed metabolites, which also features the identification of biomarkers Here, the scope and applications of metabolomics strategies to the investigation of sphingolipidoses is explored in order to facilitate our understanding of the biomolecular basis of these conditions. This review therefore surveys the benefits of applying 'state-of-the-art' metabolomics strategies, both univariate and multivariate, to sphingolipidoses, particularly Niemann-Pick type C disease. Relevant limitations of these techniques are also discussed, along with the latest advances and developments. We conclude that metabolomics strategies are highly valuable, distinctive bioanalytical techniques for probing LSDs, most especially for the detection and validation of potential biomarkers. They also show much promise for monitoring disease progression and the evaluation of therapeutic strategies and targets.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.