Evidence map›Paper›PMID 36187016›Full record

ArticleFrontiers in cardiovascular medicine2022

Age-related changes in the risk of high blood pressure.

Weibin Cheng, Yumeng Du, Qingpeng Zhang, Xin Wang, Chaocheng He, Jingjun He, Fengshi Jing, Hao Ren, Mengzhuo Guo, Junzhang Tian and 1 more

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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  17. Hypertension guideline implementation and blood pressure control in Matlosana, South Africa.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2024
    Article
  18. Article
  19. Antihypertensive medications and dementia in older adults with hypertension.medRxiv : the preprint server for health sciences · 2024
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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

11 authors.

Weibin ChengDepartment of Health Management, Guangdong Second Provincial General Hospital, Guangzhou, China.
Yumeng DuInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.
Qingpeng ZhangSchool of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Xin WangSchool of Artificial Intelligence, Tianjin University, Tianjin, China.
Chaocheng HeSchool of Information Management, Wuhan University, Wuhan, China.
Jingjun HeDepartment of Health Management, Guangdong Second Provincial General Hospital, Guangzhou, China.
Fengshi JingInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.
Hao RenInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.
Mengzhuo GuoSchool of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Junzhang TianDepartment of Health Management, Guangdong Second Provincial General Hospital, Guangzhou, China.
Zhongzhi XuInstitute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Understanding the age-related trend of risk in high blood pressure (BP) is important for preventing heart failure and cardiovascular diseases. But such a trend is still underexplored. This study aims to (a) depict the relationship of BP patterns with age, and (b) understand the trend of high BP prevalence over time in different age groups. Materials and methods: Health check-up data with an observational period of 8 years (January 1, 2011, to December 31, 2018) was used as the data source. A total of 71,468 participants aged over 18 years old with complete information on weight, height, age, gender, glucose, triglyceride, total cholesterol, systolic (SBP), and diastolic blood pressure (DBP) were included for analysis. Generalized additive models were adopted to explore the relationship between the risk of high BP and age. Variance analysis was conducted by testing the trend of high BP prevalence in age groups over time. Results: Risk of high SBP showed a continuous rise from age 35 to 79 years and a concurrent early increase in the risk of high DBP; after age 50-65 years, high DBP risk declined. The risk of SBP rises linearly with age for men, whereas increases non-linearly for women. In addition, a significant increasing trend of high SBP risk among middle-aged people was found during the past decade, men experienced a later but longer period of increase in high SBP than women. Conclusion: The high SBP risk progresses more rapidly in the early lifetime in women, compared to the lifetime thereafter. Thresholds of increasing trend of SBP suggest a possible need for hypertension screening in China after the age of 40.

Indexed as

age-related trendgeneralized additive modelsheart failurehigh blood pressureSouth China

Identifiers

PMID36187016
PMCPMC9521719

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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.