Evidence map›Paper›PMID 39091361›Full record

ArticleFrontiers in cardiovascular medicine2024

Causal factors of cardiovascular disease in end-stage renal disease with maintenance hemodialysis: a longitudinal and Mendelian randomization study.

Dandan Tian, You Xu, Ying Wang, Xirui Zhu, Chun Huang, Min Liu, Panlong Li, Xiangyong Li

Abstract read
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Article in Frontiers in cardiovascular medicine, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Dandan Tian *Department of Hypertension, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.
You Xu *Department of Clinical Laboratory, The Third Affifiliated Hospital, Southern Medical University, Guangzhou, China.
Ying WangDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Xirui ZhuDepartment of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.
Chun HuangDepartment of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.
Min LiuDepartment of Hypertension, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.
Panlong LiDepartment of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China.
Xiangyong LiDepartment of Infectious Disease, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD ( Results: The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant ( Conclusions: The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD.

Indexed as

cardiovascular diseasecausal factorsdiabetesend-stage renal diseasehemodialysis

Identifiers

PMID39091361
PMCPMC11291196

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