Evidence map›Paper›PMID 26110887›Full record

ArticlePloS one2015

The Role of Genetic Risk Score in Predicting the Risk of Hypertension in the Korean population: Korean Genome and Epidemiology Study.

Nam-Kyoo Lim, Ji-Young Lee, Jong-Young Lee, Hyun-Young Park, Myeong-Chan Cho

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
2.0field-weighted citation impact, top 12% 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

20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Article
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  7. Review
  8. Developing and Validating Risk Algorithm for Hypertension in South Africa: Results from a Nationally Representative Cohort (2008-2017).High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2022
    Article
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  18. Genetic susceptibility to salt-sensitive hypertension in a Han Chinese population: a validation study of candidate genes.Hypertension research : official journal of the Japanese Society of Hypertension · 2017
    Article
  19. Article
  20. Observational
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

5 authors at 3 institutions in 1 country.

Nam-Kyoo LimDivision of Cardiovascular and Rare Diseases, Korea National Institute of Health, Cheongju, Republic of Korea.
Ji-Young LeeDivision of Structural and Functional Genomics, Korea National Institute of Health, Cheongju, Republic of Korea; Cardiovascular Research Institute and Cardiovascular Genome Center, Yonsei University Health System, Seoul, Republic of Korea.
Jong-Young LeeDivision of Structural and Functional Genomics, Korea National Institute of Health, Cheongju, Republic of Korea.
Hyun-Young ParkDivision of Cardiovascular and Rare Diseases, Korea National Institute of Health, Cheongju, Republic of Korea.
Myeong-Chan ChoDepartment of Cardiology, College of Medicine, Chungbuk National University, Cheongju, Republic of Korea.
Korea National Institute of Health · KRChungbuk National University · KRYonsei University Health System · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension is regarded as a multifactorial disease with a modest contribution of genetic factors and strongly affected by environmental factors. Recent genome-wide association studies have identified specific loci associated with high blood pressure (BP) and hypertension. This study aimed to examine the association between the genetic risk score (GRS), a linear function of multiple single nucleotide polymorphisms (SNPs) associated with hypertension, and high BP and prevalent hypertension at baseline examination and to evaluate the efficacy of the GRS for predicting incident hypertension with longitudinal data in Korean subjects. Data for 8,556 participants, aged 40 to 69, in a community-based cohort study were analyzed. Unweighted GRS (cGRS) and weighted GRS (wGRS) were constructed from 4 SNPs related to high BP or hypertension in previous genome-wide association and its replication studies for the Korean middle-aged population. Cross-sectional analysis (n=8,556) revealed that cGRS was significantly associated with prevalent hypertension (odds ratio=1.15 per risk allele; 95%CI, 1.09-1.20). Additionally, the odds ratios (ORs) of prevalent hypertension for those who in medium and the highest tertile compared with those who in the lowest tertile of wGRS were 1.31 (95% CI, 1.15-1.50) and 1.59 (95%CI, 1.38-1.82), respectively. In a longitudinal analysis (n=5,632), participants in the highest tertile of wGRS had a 1.22-fold (OR=1.22, 95%CI, 1.02‒1.46) greater risk of incident hypertension relative to those in the lowest tertile, after adjusting for a number of confounding factors. However, wGRS topped with traditional risk factors had no significant effect on discrimination ability (c-statistics with and without wGRS were 0.811 and 0.810, P=0.1057). But, reclassification analysis showed that the addition of GRS to the model with conventional risk factors led to about 9% significant increment in category-free net reclassification improvement. GRSs based on 4 SNPs were independently associated with hypertension and may provide a statistically significant improvement over the existing model for prediction of incident hypertension.

Indexed as

AdultAgedBlood PressureCohort StudiesCross-Sectional StudiesFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansHypertensionMaleMiddle AgedPolymorphism, Single NucleotidePrevalenceRepublic of Korea

Identifiers

PMID26110887
PMCPMC4482533
OpenAlexW2232028183

What OpenQuestion holds

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LicenceCC BY
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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.