Evidence map›Paper›PMID 35619136›Full record

ArticleBMC medicine2022

Temporal trajectories of important diseases in the life course and premature mortality in the UK Biobank.

Xianwen Shang, Xueli Zhang, Yu Huang, Zhuoting Zhu, Xiayin Zhang, Shunming Liu, Jiahao Liu, Shulin Tang, Wei Wang, Honghua Yu and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 11 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

12 authors at 5 institutions in 2 countries.

Xianwen Shang *Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China. andy243@126.com.ORCID 0000-0002-2362-3222
Xueli Zhang *Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Yu HuangDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Zhuoting ZhuDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Xiayin ZhangDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Shunming LiuDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Jiahao LiuMelbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3010, Australia.
Shulin TangDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Wei WangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
Honghua YuDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China.
Zongyuan GeMonash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC, 3800, Australia.
Mingguang HeDepartment of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Eye Institute, Guangzhou, 510080, China. mingguang_he@yahoo.com.
Guangdong Academy of Medical Sciences · CNCentre for Eye Research Australia · AUSun Yat-sen University · CNMonash University · AUThe University of Melbourne · AU

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

backgroundLittle is known regarding life-course trajectories of important diseases. We aimed to identify diseases that were strongly associated with mortality and test temporal trajectories of these diseases before mortality.

methodsOur analysis was based on UK Biobank. Diseases were identified using questionnaires, nurses' interviews, or inpatient data. Mortality register data were used to identify mortality up to January 2021. The association between 60 individual diseases at baseline and in the life course and incident mortality was examined using Cox proportional regression models. Those diseases with great contribution to mortality were identified and disease trajectories in life course were then derived.

resultsDuring a median follow-up of 11.8 years, 31,373 individuals (median age at death (interquartile range): 70.7 (65.3-74.8) years, 59.4% male) died of all-cause mortality (with complete data on diagnosis date of disease), with 16,237 dying with cancer and 6702 with cardiovascular disease (CVD). We identified 37 diseases including cancers and heart diseases that were associated with an increased risk of mortality independent of other diseases (hazard ratio ranged from 1.09 to 7.77). Among those who died during follow-up, 2.2% did not have a diagnosis of any disease of interest and 90.1% were diagnosed with two or more diseases in their life course. Individuals who were diagnosed with more diseases in their life course were more likely to have longer longevity. Cancer was more likely to be diagnosed following hypertension, hypercholesterolemia, CVD, or digestive disorders and more likely to be diagnosed ahead of CVD, chronic kidney disease (CKD), or digestive disorders. CVD was more likely to be diagnosed following hypertension, hypercholesterolemia, or digestive disorders and more likely to be diagnosed ahead of cancer or CKD. Hypertension was more likely to precede other diseases, and CKD was more likely to be diagnosed as the last disease before more mortality.

conclusionsThere are significant interplays between cancer and CVD for mortality. Cancer and CVD were frequently clustered with hypertension, CKD, and digestive disorders with CKD highly being diagnosed as the last disease in the life course. Our findings underline the importance of health checks among middle-aged adults for the prevention of premature mortality.

Indexed as

Cardiovascular DiseasesHypercholesterolemiaHypertensionRenal Insufficiency, ChronicAdultBiological Specimen BanksFemaleHumansLife Change EventsMaleMiddle AgedMortality, PrematureRisk FactorsUnited KingdomCancerCardiovascular diseaseChronic kidney diseaseDisease trajectoryHypertensionMultimorbidityMultiple chronic diseasesPremature mortality

Identifiers

PMID35619136
PMCPMC9137080
OpenAlexW4281637918

What OpenQuestion holds

Textmetadata
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.