Evidence map›Paper›PMID 38185630›Full record

ArticleLipids in health and disease2024

Lipid parameters, adipose tissue distribution and prognosis prediction in chronic kidney Disease patients.

Hui-Fen Chen, Bing-Jie Xiao, Lin-Yi Chen, Wen-Wei OuYang, Xian-Long Zhang, Zhi-Ren He, Li-Zhe Fu, Fang Tang, Xiao-Na Tang, Xu-Sheng Liu and 1 more

Abstract read
In one paragraph

Article in Lipids in health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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.

Hui-Fen ChenThe Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Bing-Jie XiaoThe Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Lin-Yi ChenThe Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Wen-Wei OuYangKey Unit of Methodology in Clinical Research, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China.
Xian-Long ZhangRenal Division, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, Guangdong, China.
Zhi-Ren HeRenal Division, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, Guangdong, China.
Li-Zhe FuChronic Disease Management Outpatient Clinic, The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China.
Fang TangChronic Disease Management Outpatient Clinic, The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China.
Xiao-Na TangBao'an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen, China.
Xu-Sheng LiuThe Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China. liuxu801@126.com.ORCID http://orcid.org/0000-0002-0931-4925
Yi-Fan WuThe Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China. wuyifan007@gzucm.edu.cn.ORCID http://orcid.org/0000-0002-8498-8436

Funding

National Key Research and Development Program of China 2019YFE0196300Science and Technology Planning Project of Guangdong Province 2022A1414020015Traditional Chinese Medicine Science and Technology Research YN2020ZWB05
6 · The paper itself

Abstract

backgroundLipid management in clinic is critical to the prevention and treatment of Chronic kidney disease (CKD), while the manifestations of lipid indicators vary in types and have flexible association with CKD prognosis. PURPOSE: Explore the associations between the widely used indicators of lipid metabolism and their distribution in clinic and CKD prognosis; provide a reference for lipid management and inform treatment decisions for patients with non-dialysis CKD stage 3-5.

methodsThis is a retrospective cohort study utilizing the Self-Management Program for Patients with Chronic Kidney Disease Cohort (SMP-CKD) database of 794 individuals with CKD stages 3-5. It covers demographic data, clinical diagnosis and medical history collection, laboratory results, circulating lipid profiles and lipid distribution assessments. Primary endpoint was defined as a composite outcome(the initiation of chronic dialysis or renal transplantation, sustained decline of 40% or more in estimated glomerular filtration rate (eGFR), doubled of serum creatinine (SCr) from the baseline, eGFR less than 5 mL/min/1.73m

results255 individuals reached the composite outcome. Median follow-up duration was 2.03 [1.06, 3.19] years. Median age was 58.8 [48.7, 67.2] years with a median eGFR of 33.7 [17.6, 47.8] ml/min/1.73 m

conclusionComprehensive consideration of lipid manifestations is optimal in predicting the prognosis of individuals with non-dialysis CKD stages 3-5.

Indexed as

Renal Insufficiency, ChronicHumansLipidsMiddle AgedPrognosisRetrospective StudiesTissue DistributionLipidsChronic kidney diseaseLipid distributionLipid profiles

Identifiers

PMID38185630
PMCPMC10773091

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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.