Evidence map›Paper›PMID 36916827›Full record

ArticleJournal of clinical laboratory analysis2023

A retrospective analysis of preemptive pharmacogenomic testing in 22,918 individuals from China.

Quanfei Huang, Yuwei Liao, Tao Yu, Wei Lei, Hongfeng Liang, Jianxin Wen, Qing Liu, Yu Chen, Kaisheng Huang, Lifang Jing and 7 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.2field-weighted citation impact, top 9% 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

11 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Challenges and prospects ofFrontiers in pharmacology · 2026
    Review
  3. Review
  4. Article
  5. Pharmacogenetics: Opportunities for theAnnual review of pharmacology and toxicology · 2025
    Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. 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

17 authors at 4 institutions in 2 countries.

Quanfei HuangInstitute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, China.ORCID https://orcid.org/0000-0001-5166-5756
Yuwei LiaoPrecision Medical Lab Center, People's Hospital of Yangjiang, Yangjiang, China.
Tao YuCapitalBio Genomics Co., Ltd., Dongguan, China.
Wei LeiCapitalBio Genomics Co., Ltd., Dongguan, China.
Hongfeng LiangPrecision Medical Lab Center, People's Hospital of Yangjiang, Yangjiang, China.
Jianxin WenCapitalBio Genomics Co., Ltd., Dongguan, China.
Qing LiuCapitalBio Genomics Co., Ltd., Dongguan, China.
Yu ChenCapitalBio Genomics Co., Ltd., Dongguan, China.
Kaisheng HuangCapitalBio Technology Co. Ltd., Beijing, China.
Lifang JingCapitalBio Genomics Co., Ltd., Dongguan, China.
Xiaoyan HuangCapitalBio Genomics Co., Ltd., Dongguan, China.
Yuanru LiuCapitalBio Technology Co. Ltd., Beijing, China.
Xiaokang YuCapitalBio Genomics Co., Ltd., Dongguan, China.
Kaichan SuCapitalBio Genomics Co., Ltd., Dongguan, China.
Tengfei LiuCapitalBio Genomics Co., Ltd., Dongguan, China.ORCID https://orcid.org/0000-0001-5505-7707
Liye YangPrecision Medical Lab Center, People's Hospital of Yangjiang, Yangjiang, China.ORCID https://orcid.org/0000-0003-1581-9089
Min HuangInstitute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, China.
Beijing VDJBio (China) · CNQujiang People's Hospital · CNGuangdong Medical College · CNSun Yat-sen University · CN

Funding

National Key Research and Development Program 2017YFC0909303
6 · The paper itself

Abstract

backgroundPharmacogenomics (PGx) examines the influence of genetic variation on drug responses. With more and more Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines published, PGx is gradually shifting from the reactive testing of single gene toward the preemptive testing of multiple genes. But the profile of PGx genes, especially for the intra-country diversity, is not well understood in China.

methodsWe retrospectively collected preemptive PGx testing data of 22,918 participants from 20 provinces of China, analyzed frequencies of alleles, genotypes and phenotypes of pharmacogenes, predicted drug responses for each participant, and performed comparisons between different provinces. RESULTS AND

conclusionAfter analyzing 15 pharmacogenes from CPIC guidelines of 31 drugs, we found that 99.97% of individuals may have an atypical response to at least one drug; the participants carry actionable genotypes leading to atypical dosage recommendation for a median of eight drugs. Over 99% of the participants were recommended a decreased warfarin dose based on genetic factors. There were 20 drugs with high-risk ratios from 0.18% to 58.25%, in which clopidogrel showed the highest high-risk ratio. In addition, the high-risk ratio of rasburicase in GUANGDONG (risk ratio (RR) = 13.17, 95%CI:4.06-33.22, p < 0.001) and GUANGXI (RR = 23.44, 95%CI:8.83-52.85, p < 0.001) were significantly higher than that in all provinces. Furthermore, the diversity we observed among 20 provinces suggests that preemptive PGx testing in different geographical regions in China may need to pay more attention to specific genes. These results emphasize the importance of preemptive PGx testing and provide essential evidence for promoting clinical implementation in China.

Indexed as

PharmacogeneticsPharmacogenomic TestingChinaGenotypeHumansRetrospective Studiesactionable genotypeatypical dosageclinical pharmacogenetics implementation consortiumgeographic distributionpharmacogenomics

Identifiers

PMID36916827
PMCPMC10098050
OpenAlexW4324129879

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.