ArticleMedicine2026
Machine learning-based evaluation of lipid biomarkers for cardiovascular risk prediction in chronic kidney disease: A cross-sectional study.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic kidney disease (CKD) is an important public health issue globally, greatly increasing the prevalence and mortality of cardiovascular disease (CVD). Dyslipidemia is a prevalent metabolic disease in people with CKD that is linked to atherosclerosis and cardiovascular events. Nevertheless, the association between various lipid markers and CVD is still uncertain. This study aimed to analyze the associations between various lipid markers and CVD in patients with CKD using machine learning methods and to identify the optimal lipid biomarkers for risk prediction. This study analyzed 2696 CKD participants from the National Health and Nutrition Examination Survey from 2005 to 2018 through multivariate logistic regression to examine the relationship between various lipid markers and CVD, used restricted cubic splines to evaluate linear and nonlinear relationships between variables and outcomes, and evaluated the predictive value of various lipid markers for cardiovascular risk using machine learning models. Total cholesterol (odds ratio [OR]: 0.679 [0.614-0.751], P < .001), low-density lipoprotein cholesterol (LDL-C; OR: 0.629 [0.560-0.704], P < .001), and apolipoprotein B (OR: 0.986 [0.982-0.991], P < .001) were independently associated with CVD after multivariable adjustment in patients with CKD, among which total cholesterol, LDL-C, and apolipoprotein B showed significant L-shaped associations with CVD, while remnant cholesterol and triglycerides exhibited a U-shaped relationship. High-density lipoprotein cholesterol showed an almost linear relationship. Among the lipid markers, LDL-C demonstrated the strongest discriminative performance for CVD. In patients with CKD, lipid markers were significantly associated with CVD. Incorporating these variables into predictive models improved model discrimination and may enhance cardiovascular risk stratification in this population. Further validation in external CKD cohorts is warranted.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.