Evidence map›Paper›PMID 39385593›Full record

ArticleInternational journal of epidemiology2024

Conventional and genetic associations of BMI with major vascular and non-vascular disease incidence and mortality in a relatively lean Chinese population: U-shaped relationship revisited.

Andri Iona, Fiona Bragg, Zammy Fairhurst-Hunter, Iona Y Millwood, Neil Wright, Kuang Lin, Ling Yang, Huaidong Du, Yiping Chen, Pei Pei and 11 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. 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

21 authors.

Andri IonaClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Fiona BraggClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID 0000-0002-9181-6886
Zammy Fairhurst-HunterClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Iona Y MillwoodClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Neil WrightClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Kuang LinClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Ling YangClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Huaidong DuClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID 0000-0002-9814-0049
Yiping ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Pei PeiPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
Liang ChengQingdao Shinan District Centre for Disease Control and Prevention, Shinan District, Qingdao, China.
Dan SchmidtClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Daniel AveryClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Canqing YuPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.ORCID 0000-0002-0019-0014
Jun LvPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.ORCID 0000-0001-7916-3870
Robert ClarkeClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID 0000-0002-9802-8241
Robin WaltersClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Liming LiPeking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
Sarah ParishClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.ORCID 0000-0003-3532-0832
Zhengming ChenClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
China Kadoorie Biobank Collaborative Group

Funding

Kadoorie Charitable Foundation in Hong Kong 088158/Z/09/Z
6 · The paper itself

Abstract

backgroundHigher body mass index (BMI) is associated with higher incidence of cardiovascular and some non-cardiovascular diseases (CVDs/non-CVDs). However, uncertainty remains about its associations with mortality, particularly at lower BMI levels.

methodsThe prospective China Kadoorie Biobank recruited >512 000 adults aged 30-79 years in 2004-08 and genotyped a random subset of 76 000 participants. In conventional and Mendelian randomization (MR) analyses, Cox regression yielded adjusted hazard ratios (HRs) associating measured and genetically predicted BMI levels with incident risks of major vascular events (MVEs; conventional/MR 68 431/23 621), ischaemic heart disease (IHD; 50 698/12 177), ischaemic stroke (IS; 42 427/11 897) and intracerebral haemorrhage (ICH; 7644/4712), and with mortality risks of CVD (15 427/6781), non-CVD (26 915/4355) and all causes (42 342/6784), recorded during ∼12 years of follow-up.

resultsOverall, the mean BMI was 23.8 (standard deviation: 3.2) kg/m2 and 13% had BMIs of <20 kg/m2. Measured and genetically predicted BMI showed positive log-linear associations with MVE, IHD and IS, but a shallower positive association with ICH in conventional analyses. Adjusted HRs per 5 kg/m2 higher genetically predicted BMI were 1.50 (95% CI 1.41-1.58), 1.49 (1.38-1.61), 1.42 (1.31-1.54) and 1.64 (1.58-1.69) for MVE, IHD, IS and ICH, respectively. These were stronger than associations in conventional analyses [1.21 (1.20-1.23), 1.28 (1.26-1.29), 1.31 (1.29-1.33) and 1.14 (1.10-1.18), respectively]. At BMIs of ≥20 kg/m2, there were stronger positive log-linear associations of BMI with CVD, non-CVD and all-cause mortality in MR than in conventional analyses.

conclusionsAmong relatively lean Chinese adults, higher genetically predicted BMI was associated with higher risks of incident CVDs. Excess mortality risks at lower BMI in conventional analyses are likely not causal and may reflect residual reverse causality.

Indexed as

Body Mass IndexMendelian Randomization AnalysisAdultAgedCardiovascular DiseasesChinaEast Asian PeopleFemaleHumansIncidenceMaleMiddle AgedProportional Hazards ModelsProspective StudiesRisk FactorsThinnessBody mass indexcardiovascular diseaseMendelian randomizationstroke

Identifiers

PMID39385593
PMCPMC11464668

What Socratic holds

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LicenceCC BY
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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 Socratic graph.