Evidence map›Paper›PMID 41660422›Full record

ArticleFrontiers in nutrition2026

Machine learning-based estimation of trunk fat percentage and its association with cardiometabolic risk leveraging two large national cohorts.

Liangming Zeng, Xuemin Guo, Hesen Wu, Changjing Huang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in nutrition, 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

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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

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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

4 authors.

Liangming ZengClinical Laboratory Center, Meizhou People's Hospital, Meizhou Academy of Medical Sciences, Meizhou, Guangdong, China.
Xuemin GuoClinical Laboratory Center, Meizhou People's Hospital, Meizhou Academy of Medical Sciences, Meizhou, Guangdong, China.
Hesen WuClinical Laboratory Center, Meizhou People's Hospital, Meizhou Academy of Medical Sciences, Meizhou, Guangdong, China.
Changjing HuangDepartment of Cardiology, Meizhou People's Hospital, Meizhou Academy of Medical Sciences, Meizhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to develop and validate a machine learning model for accurate estimation of trunk fat percentage using readily available anthropometric measures, and to evaluate its discriminative performance for cardiometabolic diseases compared with conventional whole-body fat percentage. Methods: We utilized data from the National Health and Nutrition Examination Survey (NHANES; 1999-2006 and 2011-2018) as the development cohort ( Results: The XGBoost model demonstrated superior performance in the development cohort, achieving an Conclusion: This study presents a highly accurate and clinically practical machine learning model for trunk fat percentage estimation using five basic anthropometric measurements. External validation confirms that trunk fat percentage is a superior biomarker for identifying cardiometabolic risks compared to whole-body fat percentage. The model provides a reliable tool for non-invasive central adiposity assessment in large-scale epidemiological studies and clinical practice.

Indexed as

central adipositydual-energy X-ray absorptiometrymetabolic risk predictionpercent fattrunk fat

Identifiers

PMID41660422
PMCPMC12872505

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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 OpenQuestion graph.