Evidence map›Paper›PMID 34674714›Full record

ArticleBMC medicine2021

Modification effect of ideal cardiovascular health metrics on genetic association with incident heart failure in the China Kadoorie Biobank and the UK Biobank.

Ruotong Yang, Jun Lv, Canqing Yu, Yu Guo, Pei Pei, Ninghao Huang, Ling Yang, Iona Y Millwood, Robin G Walters, Yiping Chen and 8 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2021. 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
1.7field-weighted citation impact, top 14% 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, 14 citations in OpenAlex.

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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

18 authors at 5 institutions in 4 countries.

Ruotong YangDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Jun LvDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Canqing YuDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Yu GuoChinese Academy of Medical Sciences, Beijing, China.
Pei PeiChinese Academy of Medical Sciences, Beijing, China.
Ninghao HuangDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Ling YangMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
Iona Y MillwoodMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
Robin G WaltersMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
Yiping ChenMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
Huaidong DuMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
Ran TaoInstitute of Chronic Disease, Jiangsu Provincial Center for Disease Control and Prevention, Jiangsu, China.
Junshi ChenChina National Center for Food Safety Risk Assessment, Beijing, China.
Zhengming ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Robert ClarkeClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Tao HuangDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China. huangtaotao@pku.edu.cn.
Liming LiDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China. lmleeph@vip.163.com.
China Kadoorie Biobank Collaborative Group
University of Oxford · GBPeking University · CNChina National Center for Food Safety Risk Assessment · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNJiangsu Provincial Center for Disease Control and Prevention · CN

Funding

British Heart Foundation CH/1996001/9454Cancer Research UK 16896Cancer Research UK 29186Medical Research Council MC_PC_13049Medical Research Council MC_PC_14135Medical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MC_U137686851Medical Research Council MC_UU_00017/1Medical Research Council MC_UU_12026/2Wellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

backgroundBoth genetic and cardiovascular factors contribute to the risk of developing heart failure (HF), but whether idea cardiovascular health metrics (ICVHMs) offset the genetic association with incident HF remains unclear.

objectivesTo investigate the genetic association with incident HF as well as the modification effect of ICVHMs on such genetic association in Chinese and British populations.

methodsAn ICVHMs based on smoking, drinking, physical activity, diets, body mass index, waist circumference, blood pressure, blood glucose, and blood lipids, and a polygenic risk score (PRS) for HF were constructed in the China Kadoorie Biobank (CKB) of 96,014 participants and UK Biobank (UKB) of 335,782 participants which were free from HF and severe chronic diseases at baseline.

resultsDuring the median follow-up of 11.38 and 8.73 years, 1451 and 3169 incident HF events were documented in CKB and UKB, respectively. HF risk increased monotonically with the increase of PRS per standard deviation (CKB: hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.07, 1.32; UKB: 1.07; 1.03, 1.11; P for trend < 0.001). Each point increase in ICVHMs was associated with 15% and 20% lower risk of incident HF in CKB (0.85; 0.81, 0.90) and UKB (0.80; 0.77, 0.82), respectively. Compared with unfavorable ICVHMs, favorable ICVHMs was associated with a lower HF risk, with 0.71 (0.44, 1.15), 0.41 (0.22, 0.77), and 0.48 (0.30, 0.77) in the low, intermediate, and high genetic risk in CKB and 0.34 (0.26, 0.44), 0.32 (0.25, 0.41), and 0.37 (0.28, 0.47) in UKB (P for multiplicative interaction > 0.05). Participants with low genetic risk and favorable ICVHMs, as compared with high genetic risk and unfavorable ICVHMs, had 56~72% lower risk of HF (CKB 0.44; 0.28, 0.70; UKB 0.28; 0.22, 0.37). No additive interaction between PRS and ICVHMs was observed (relative excess risk due to interaction was 0.05 [-0.22, 0.33] in CKB and 0.04 [-0.14, 0.22] in UKB).

conclusionsIn CKB and UKB, genetic risk and ICVHMs were independently associated with the risk of incident HF, which suggested that adherence to favorable cardiovascular health status was associated with a lower HF risk among participants with all gradients of genetic risk.

Indexed as

Heart FailureQuality Indicators, Health CareBiological Specimen BanksChinaHumansIncidenceRisk FactorsUnited KingdomGenetic riskHeart failureIdeal cardiovascular health metrics

Identifiers

PMID34674714
PMCPMC8532287
OpenAlexW3206559254

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.