Evidence map›Paper›PMID 41001432›Full record

ArticlePhenomics (Cham, Switzerland)2025

Lipoprotein Subfractions Predict All-cause and Cardiovascular Mortality in CKD Patients Undergoing Hemodialysis: A Prospective Cohort Study Based on NMR Metabolomics.

Qingxia Huang, Han Zhang, Ziyan Shen, Jing Chen, Xuesen Cao, Linghan Xu, Cheng Zhu, Shiqi Lv, Xixi Yu, Xiaoqiang Ding and 2 more

Abstract read
In one paragraph

Article in Phenomics (Cham, Switzerland), 2025. 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. Review
  2. Article
  3. Article
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.

Qingxia Huang *State Key Laboratory of Genetic Engineering, School of Life Sciences, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200032 China.ORCID 0009-0003-4987-7057
Han Zhang *Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Ziyan ShenDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Jing ChenDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Xuesen CaoDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Linghan XuDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Cheng ZhuDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Shiqi LvDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Xixi YuDepartment of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China.
Xiaoqiang DingDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.
Huiru TangState Key Laboratory of Genetic Engineering, School of Life Sciences, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200032 China.ORCID 0000-0002-7139-2756
Xiaoyan ZhangDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Medical Center of Kidney Disease, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Institute of Kidney and Dialysis, Shanghai, 200032 China.ORCID 0000-0001-6892-1082

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lipoprotein metabolism is markedly altered in chronic kidney disease (CKD). Studies on the association between nuclear magnetic resonance (NMR) derived lipoprotein subfraction signature and mortality in CKD patients undergoing hemodialysis are limited. NMR based metabolomics was performed on the baseline plasma samples from 368 maintenance hemodialysis patients. Survival analyses were used to investigate the effect of lipoproteins on all-cause mortality and cardiovascular disease (CVD) mortality. Prediction models were further developed using stepwise regression combined cox proportional-hazards model. During the average follow-up of 45.1 months, we observed 144 all-cause deaths and 67 CVD deaths. After adjustment for 14 important covariates, we identified 18 and 35 lipoprotein parameters associated with all-cause mortality and CVD mortality, separately. Cholesterol in total low-density lipoprotein (LDL-C) and total high-density lipoprotein (HDL-C) were correlated with neither all-cause death nor CVD death. For lipoprotein subfractions, triglyceride levels in large very-low density lipoprotein (VLDL) were positively correlated only with all-cause mortality. Lipids (triglyceride, cholesterol and phospholipid) in medium VLDL, cholesterol/total lipids in intermediate-density lipoprotein (IDL) and free cholesterol/total lipids in small high-density lipoprotein (HDL) were positively associated with both all-cause and CVD mortality. The addition of lipoprotein parameters to traditional risk factors significantly improved the mortality risk prediction: the area under the receiver operating characteristic curve (AUC) was improved from 0.811 to 0.842 ( Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-024-00179-5.

Indexed as

Cardiovascular diseaseChronic kidney diseaseLipidLipoproteinMetabolomicsNuclear magnetic resonance

Identifiers

PMID41001432
PMCPMC12457243

What Socratic holds

Textmetadata
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 Socratic graph.