Evidence map›Paper›PMID 29654385›Full record

ReviewJournal of inherited metabolic disease2018

Recognizable phenotypes in CDG.

Carlos R Ferreira, Ruqaia Altassan, Dorinda Marques-Da-Silva, Rita Francisco, Jaak Jaeken, Eva Morava

Open access · greenAbstract readReview
In one paragraph

Review in Journal of inherited metabolic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
5.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 97 citations in OpenAlex.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Strabismus in Genetic Syndromes: A Review.Clinical & experimental ophthalmology · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. A novelFrontiers in genetics · 2024
    Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. [Advances in the diagnosis and treatment of phosphomannomutase 2 deficiency].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 3 countries.

Carlos R FerreiraMedical Genetics Branch National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Ruqaia AltassanMetabolic Center, Department of Pediatrics, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium.
Dorinda Marques-Da-SilvaUCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisboa, Portugal.
Rita FranciscoUCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisboa, Portugal.
Jaak JaekenMetabolic Center, Department of Pediatrics, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium.
Eva MoravaMetabolic Center, Department of Pediatrics, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium. Morava-Kozicz.Eva@MAYO.edu.ORCID 0000-0001-7441-700X
KU Leuven · BEPortuguese League Against Epilepsy · PTMayo Clinic · USNational Institutes of Health · US

Funding

Tracking & Evaluation CoreU54GM104940 · NIGMS · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI JUDD E SHELLITO · 2012 to 2026
$69.1M
NIGMS NIH HHS U54 GM104940
6 · The paper itself

Abstract

Pattern recognition, using a group of characteristic, or discriminating features, is a powerful tool in metabolic diagnostic. A classic example of this approach is used in biochemical analysis of urine organic acid analysis, where the reporting depends more on the correlation of pertinent positive and negative findings, rather than on the absolute values of specific markers. Similar uses of pattern recognition in the field of biochemical genetics include the interpretation of data obtained by metabolomics, like glycomics, where a recognizable pattern or the presence of a specific glycan sub-fraction can lead to the direct diagnosis of certain types of congenital disorders of glycosylation. Another indispensable tool is the use of clinical pattern recognition-or syndromology-relying on careful phenotyping. While genomics might uncover variants not essential in the final clinical expression of disease, and metabolomics could point to a mixture of primary but also secondary changes in biochemical pathways, phenomics describes the clinically relevant manifestations and the full expression of the disease. In the current review we apply phenomics to the field of congenital disorders of glycosylation, focusing on recognizable differentiating findings in glycosylation disorders, characteristic dysmorphic features and malformations in PMM2-CDG, and overlapping patterns among the currently known glycosylation disorders based on their pathophysiological basis.

Indexed as

PhenotypeCongenital Disorders of GlycosylationDiagnosis, DifferentialGenomicsGlycomicsHumansMetabolomicsPattern Recognition, AutomatedPattern Recognition, Visual

Identifiers

PMID29654385
PMCPMC5960425
OpenAlexW2796918229

What OpenQuestion holds

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Registered trials

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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.