Evidence map›Paper›PMID 39695473›Full record

ArticleBMC public health2024

Linear and non-linear relationships between red blood cell indices and cardiovascular risk factors: findings from the China National Health Survey.

Huijing He, Li Pan, Feng Liu, Xiaolan Ren, Ze Cui, Lize Pa, Dingming Wang, Jingbo Zhao, Hailing Wang, Xianghua Wang and 3 more

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Huijing HeDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Li PanDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Feng LiuDepartment of Chronic and Noncommunicable Disease Prevention and Control, Shaanxi Provincial Center for Disease Control and Prevention, Xi'an, China.
Xiaolan RenDepartment of Chronic and Noncommunicable Disease Prevention and Control, Gansu Provincial Center for Disease Control and Prevention, Lanzhou, China.
Ze CuiDepartment of Chronic and Noncommunicable Disease Prevention and Control, Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, China.
Lize PaDepartment of Chronic and Noncommunicable Disease Prevention and Control, Xinjiang Uyghur Autonomous Region Center for Disease Control and Prevention, Urumqi, China.
Dingming WangDepartment of Chronic and Noncommunicable Disease Prevention and Control, Guizhou Provincial Center for Disease Control and Prevention, Guiyang, China.
Jingbo ZhaoDepartment of Epidemiology and Statistics, School of Public Health, Harbin Medical University, Harbin, China.
Hailing WangDepartment of Chronic and Noncommunicable Disease Prevention and Control, Inner Mongolia Autonomous Region Center for Disease Control and Prevention, Hohhot, China.
Xianghua WangIntegrated Office, Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College, Tianjin, China.
Jianwei DuDepartment of Chronic and Noncommunicable Disease Prevention and Control, Hainan Provincial Center for Disease Control and Prevention, Haikou, China.
Xia PengDepartment of Chronic and Noncommunicable Disease Prevention and Control, Yunnan Provincial Center for Disease Control and Prevention, Kunming, China.
Guangliang ShanDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. guangliang_shan@163.com.

Funding

Key Basic Research Program of the Ministry of Science and Technology of China 2013FY114100National Key R&D Program of China 2016YFC0900600 / 2016YFC0900601National Natural Science Foundation of China 82373669State Key Laboratory Special Fund 2060204
6 · The paper itself

Abstract

introductionCardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Red blood cell indices (RBIs) are associated with CVD risk factors (CRFs) and easy to test, making them useful as a screening tool for early identification of individuals at high risk for CVDs.

methodsData from 31,781 participants in the China National Health Survey conducted from 2012 to 2017 were analyzed. Linear and non-linear relationships between RBIs and CRFs (hyperuricemia, diabetes, dyslipidemia) were assessed using restricted cubic splines. Propensity score weighting was used to balance confounders between RBI groups in the multivariable logistic regression models.

resultsHemoglobin concentration, red blood cell count and hematocrit all showed a significant linear dose-response association with all CRFs (p values < 0.001). Higher RBIs levels were associated with increased risk of hyperuricemia, diabetes, high LDL, high triglycerides, and high total cholesterol, but decreased HDL. For example, compared to the lowest quantile of HGB, the highest quantile had a 26% (13-40%) higher risk for hyperuricemia, a 43% (25-63%) higher risk of diabetes, 87% (61%-1.18 fold) higher risk of high LDL, and 68% (52-85%) higher risk of high triglycerides. Non-linear relationships were revealed between RBIs and most CRFs except uric acid and glucose. Sex differences were observed, with stronger associations between RBIs and hyperuricemia in women but stronger links with high LDL in men.

conclusionsElevated RBIs indicated higher risk of multiple CRFs. These findings suggest incorporating RBIs into CVD screening strategies to facilitate early prevention efforts, with consideration of sex differences.

Indexed as

Cardiovascular DiseasesErythrocyte IndicesHealth SurveysHeart Disease Risk FactorsAdultAgedChinaFemaleHumansMaleMiddle AgedRisk FactorsCardiovascular risk factorsDiabetesDyslipidemiaHyperuricemiaRed blood cell

Identifiers

PMID39695473
PMCPMC11657473

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.