Evidence map›Paper›PMID 37308883›Full record

SynthesisOrphanet journal of rare diseases2023

Chinese genetic variation database of inborn errors of metabolism: a systematic review of published variants in 13 genes.

Yongchao Guo, Jianhui Jiang, Zhongyao Xu

Abstract readSystematic Review
In one paragraph

Synthesis in Orphanet journal of rare diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

3 authors.

Yongchao GuoShenzhen Uni-medica Technology Co., Ltd, Liuxian Culture Park, Nanshan District, 5180553, Shenzhen, China.
Jianhui JiangMaternal and Child Health Hospital of Guangdong Province, No.13 Guangyuan West Road, Yuexiu District, 510010, Guangzhou, Guangdong Province, China.
Zhongyao XuShenzhen Uni-medica Technology Co., Ltd, Liuxian Culture Park, Nanshan District, 5180553, Shenzhen, China. xuzhongyao@uni-medica.com.ORCID 0000-0001-9910-9257

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPopulation-specific variation database of inborn errors of metabolism (IEMs) is essential for precise genetic diagnosis and disease prevention. Here we presented a systematic review of clinically relevant variants of 13 IEMs genes reported among Chinese patients.

methodsA systematic search of the following electronic databases for 13 IEMs genes was conducted: PubMed-NCBI, China national knowledge infrastructure and Wanfang databases. Patient data was extracted from articles eligible for inclusion and recorded in Excel electronic form using a case-by-case approach.

resultsA total of 218 articles, 93 published in English and 125 in Chinese, were retrieved. After variant annotation and deduplication, 575 unique patients (241 from articles published in Chinese) were included in the population-specific variation database. Patients identified by newborn screening and symptomatic presentation were 231 (40.17%) and 344 (59.83%), respectively. Biallelic variants were observed in 525/575 (91.3%). Among the 581 unique variants identified, 83 (14.28%) were described ≥ 3 times and 97 (16.69%) were not recorded in Clinvar or HGMD. Four variants were reclassified as benign and dozens of confusing variants deserved further research.

conclusionThis review provides a unique resource of the well-characterized diseases and causative variants that have accumulated in Chinese population and is a preliminary attempt to build the Chinese genetic variation database of IEMs.

Indexed as

East Asian PeopleMetabolism, Inborn ErrorsChinaGenetic VariationHumansInfant, NewbornChineseInborn errors of metabolismPopulation-specific variant databaseVariant interpretation

Identifiers

PMID37308883
PMCPMC10262587

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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.