Evidence map›Paper›PMID 40199949›Full record

ArticleScientific reports2025

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

Zhiyuan Ning, Xuanfei Jiang, Huan Huang, Honggang Ma, Ji Luo, Xiangyan Yang, Bing Zhang, Ying Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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. 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

8 authors.

Zhiyuan Ning *Department of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.
Xuanfei Jiang *Department of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.
Huan Huang *Department of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.
Honggang MaDepartment of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.
Ji LuoDepartment of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.
Xiangyan YangDepartment of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.
Bing ZhangDepartment of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China. sjnkhz@163.com.
Ying LiuDepartment of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China. liuy2365@mail.sysu.edu.cn.

Funding

Huzhou Science and Technology Plan Project 2024GYB04Zhejiang Provincial Medical and Healthcare Science and Technology Plan 2024KY1641Zhejiang Provincial Natural Science Foundation of China LQN25H090017
6 · The paper itself

Abstract

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardiovascular health. We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). Machine learning was employed to assess 66 factors that associated with NT-proBNP levels, including demographic, anthropometric, lifestyle, biochemical, blood, metabolic, and disease characteristics. The predictive power of the model was assessed using five-fold cross-validation. The optimal features predicting NT-proBNP levels were identified using univariate and step-forward multivariate models. Weighted least squares regression (WLS) was applied for supplementary analysis. Finally, the relationship between the corresponding features and NT-proBNP was validated using weighted and adjusted generalized additive models (GAM). We included 12, 526 participants without cardiovascular diseases. In the univariate model, age exhibited the highest association with NT-proBNP levels (the coefficient of determination (R

Indexed as

Cardiovascular DiseasesMachine LearningNatriuretic Peptide, BrainPeptide FragmentsAdultAgedBiomarkersFemaleHumansMaleMiddle AgedNutrition SurveysBiomarkersNatriuretic Peptide, BrainPeptide Fragmentspro-brain natriuretic peptide (1-76)Circulating NT-proBNPMachine learningNational health and nutrition examination survey (NHANES)People without cardiovascular diseasesPersonalized medicinePublic health

Identifiers

PMID40199949
PMCPMC11978906

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.