Evidence map›Paper›PMID 34706724›Full record

ArticleBMC medicine2021

Association between cardiometabolic disease multimorbidity and all-cause mortality in 2 million women and men registered in UK general practices.

Dexter Canoy, Jenny Tran, Mariagrazia Zottoli, Rema Ramakrishnan, Abdelaali Hassaine, Shishir Rao, Yikuan Li, Gholamreza Salimi-Khorshidi, Robyn Norton, Kazem Rahimi

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 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
7.8field-weighted citation impact, top 2% 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

57 citing papers in PubMed, 83 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

10 authors at 3 institutions in 2 countries.

Dexter CanoyNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK. dexter.canoy@wrh.ox.ac.uk.
Jenny TranNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
Mariagrazia ZottoliDepartment of Statistics, University of Oxford, Oxford, UK.
Rema RamakrishnanNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
Abdelaali HassaineNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
Shishir RaoNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
Yikuan LiNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
Gholamreza Salimi-KhorshidiNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
Robyn NortonThe George Institute for Global Health, University of New South Wales, Sydney, Australia.
Kazem RahimiNuffield Department of Women's and Reproductive Health, University of Oxford, Hayes House, 75 George St., OX1 2BQ, Oxford, UK.
University of Oxford · GBOxford BioMedica (United Kingdom) · GBThe George Institute for Global Health · AU

Funding

British Heart Foundation FS/19/36/34346British Heart Foundation PG/18/65/33872
6 · The paper itself

Abstract

backgroundMyocardial infarction (MI), stroke and diabetes share underlying risk factors and commonalities in clinical management. We examined if their combined impact on mortality is proportional, amplified or less than the expected risk separately of each disease and whether the excess risk is explained by their associated comorbidities.

methodsUsing large-scale electronic health records, we identified 2,007,731 eligible patients (51% women) and registered with general practices in the UK and extracted clinical information including diagnosis of myocardial infarction (MI), stroke, diabetes and 53 other long-term conditions before 2005 (study baseline). We used Cox regression to determine the risk of all-cause mortality with age as the underlying time variable and tested for excess risk due to interaction between cardiometabolic conditions.

resultsAt baseline, the mean age was 51 years, and 7% (N = 145,910) have had a cardiometabolic condition. After a 7-year mean follow-up, 146,994 died. The sex-adjusted hazard ratios (HR) (95% confidence interval [CI]) of all-cause mortality by baseline disease status, compared to those without cardiometabolic disease, were MI = 1.51 (1.49-1.52), diabetes = 1.52 (1.51-1.53), stroke = 1.84 (1.82-1.86), MI and diabetes = 2.14 (2.11-2.17), MI and stroke = 2.35 (2.30-2.39), diabetes and stroke = 2.53 (2.50-2.57) and all three = 3.22 (3.15-3.30). Adjusting for other concurrent comorbidities attenuated these estimates, including the risk associated with having all three conditions (HR = 1.81 [95% CI 1.74-1.89]). Excess risks due to interaction between cardiometabolic conditions, particularly when all three conditions were present, were not significantly greater than expected from the individual disease effects.

conclusionMyocardial infarction, stroke and diabetes were associated with excess mortality, without evidence of any amplification of risk in people with all three diseases. The presence of other comorbidities substantially contributed to the excess mortality risks associated with cardiometabolic disease multimorbidity.

Indexed as

Diabetes MellitusMyocardial InfarctionStrokeFemaleHumansMaleMiddle AgedMultimorbidityRisk FactorsUnited KingdomDiabetesElectronic health recordsMortalityMultimorbidityMyocardial infarctionStroke

Identifiers

PMID34706724
PMCPMC8555122
OpenAlexW3210559745

What OpenQuestion holds

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LicenceCC BY
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.