Evidence map›Paper›PMID 35211447›Full record

ArticleFrontiers in public health2022

Associations Between High-Sensitivity C-Reactive Protein and All-Cause Mortality Among Oldest-Old in Chinese Longevity Areas: A Community-Based Cohort Study.

Pei-Liang Chen, Zhi-Hao Li, Hai-Lian Yang, Zhao-Jin Cao, Xin Cheng, Feng Zhao, Xi-Ru Zhang, Yue-Bin Lv, Fu-Rong Li, Yuan-Feng Zhou and 7 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 15 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

17 authors at 2 institutions in 1 country.

Pei-Liang ChenDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Zhi-Hao LiDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Hai-Lian YangDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Zhao-Jin CaoChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Xin ChengDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Feng ZhaoChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Xi-Ru ZhangDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Yue-Bin LvChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Fu-Rong LiDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Yuan-Feng ZhouDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Hao-Nan LiDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Ying-Li QuChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Zhao-Xue YinChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Ling LiuChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Xian-Bo WuDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Xiao-Ming ShiChinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China.
Chen MaoDepartment of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Southern Medical University · CNChinese Center For Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe association between high-sensitivity C-reactive protein (hsCRP) levels and all-cause mortality for the oldest-old (aged 80 years or older) remains unclear. We aimed to investigate the associations between hsCRP concentrations and the risks of all-cause mortality, and further identify the potential modifying factors affecting these associations among the oldest-old.

methodsThis prospective, community-based cohort study included 2,206 participants aged 80 years or older (median age 93.0 years) from the Healthy Aging and Biomarkers Cohort Study. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) with 95% confidential intervals (95% CIs) for all-cause mortality according to hsCRP quartiles and recommendation for relative risk categories of hsCRP levels (< 1.0, 1.0-3.0, and > 3.0 mg/L), with adjustment for sociodemographic information, lifestyle, physical examination, medical history, and other potential confounders.

resultsDuring a median follow-up period of 3.1 years (IQR: 1.6-3.9 years), 1,106 deaths were verified. After full adjustment for potential confounders, a higher hsCRP concentration was positively associated with an increased risk of all-cause mortality (

conclusionsOur study indicated that elevated hsCRP concentrations were associated with a higher risk of all-cause mortality among Chinese oldest-old. Future studies investigating additional factors of disease and aging processes are needed to obtain a better understanding of the mechanisms.

Indexed as

C-Reactive ProteinAged, 80 and overChinaCohort StudiesHumansProportional Hazards ModelsProspective StudiesC-Reactive Proteinagingall-cause mortalitycohort studyhigh-sensitivity C-reactive proteininflammationoldest-old

Identifiers

PMID35211447
PMCPMC8861080
OpenAlexW4210832633

What OpenQuestion holds

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LicenceCC BY
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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.