Evidence map›Paper›PMID 41196834›Full record

ArticleJournal of vascular research2026

Development of an Explainable Machine Learning Model for Cardiovascular-Kidney-Metabolic Syndrome Prediction Based on Dietary Antioxidants in a National Population.

Mei Xue, Hannah Chang, Bo-Chun Wang, Ning Ma, Xiao-Qian Zhang, Xiao-Qian Wang, Wen-Quan Niu, Xiao-Qun Dong, Chung-Chou H Chang

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In one paragraph

Article in Journal of vascular research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Mei XueGraduate School, Beijing University of Chinese Medicine, Beijing, China.
Hannah ChangPrecision, University of Michigan, Ann Arbor, Michigan, USA.
Bo-Chun WangNortheast Forestry University, Harbin, China.
Ning MaGraduate School, Beijing University of Chinese Medicine, Beijing, China.
Xiao-Qian ZhangGraduate School, Beijing University of Chinese Medicine, Beijing, China.
Xiao-Qian WangCapital Institute of Pediatrics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wen-Quan NiuCenter for Evidence-Based Medicine, Capital Center for Children's Health, Capital Medical University, Capital Institute of Pediatrics, Beijing, China, niuwenquan@pumc.edu.cn.
Xiao-Qun DongPrecision Health Program, Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA, dongxi19@msu.edu.
Chung-Chou H ChangDepartment of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA, changj@pitt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

<p>Introduction: The role of dietary antioxidants in preventing or delaying the progression of cardiovascular-kidney-metabolic (CKM) syndrome remains underexplored. We aimed to develop and interpret a machine learning (ML) model to predict advanced CKM stages based on dietary antioxidant profiles.

methodsData were analyzed from 10,257 adults aged >30 years in the NHANES 2007-2010 and 2017-2018 cycles. Dietary antioxidant intake was estimated using two 24-h dietary recalls. Five ML algorithms were trained with rigorous hyperparameter optimization and evaluated comprehensively. SHapley Additive exPlanations (SHAP) was applied to elucidate feature importance and individual-level contributions. An online prediction tool was deployed to enhance clinical utility.

resultsThe eXtreme Gradient Boosting (XGBoost) model achieved the highest predictive performance, yielding an area under the curve of 0.901. SHAP analysis identified seven key predictors: age, sex, smoking status, magnesium, zinc, myricetin, and catechin. Older age, male sex, and smoking were associated with increased CKM risk, whereas higher intakes of magnesium, myricetin, zinc, and catechin were protective.

conclusionsXGBoost effectively predicted advanced CKM stages using a concise set of seven features. Explainable AI approaches such as SHAP enhance model transparency and clinical translation, supporting personalized CKM risk stratification based on dietary antioxidant patterns. </p>.

Indexed as

AntioxidantsCardio-Renal SyndromeDecision Support TechniquesDietMachine LearningMetabolic SyndromeAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedNutrition SurveysPredictive Value of TestsRisk AssessmentAntioxidantsCardiovascular-kidney-metabolic syndromeDietary antioxidantseXtreme Gradient BoostingMachine learningSHapley Additive exPlanations

Identifiers

PMID41196834
PMCPMC12707898

What Socratic holds

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LicenceCC BY-NC
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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.