ArticleFrontiers in nutrition2025
A body composition-based clustering study and its association with metabolic phenotypes among the general population in China.
Article in Frontiers in nutrition, 2025. 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Metabolic phenotypes are linked to various metabolic diseases, but current classification methods have limitations. This study aims to directly cluster obese populations based on body composition and machine learning, enhancing understanding of lipid metabolism and disease associations. Methods: A retrospective analysis included participants who underwent InBody examinations at Taikang Xianlin Drum Tower Hospital in 2023. Subjects were categorized into four phenotypes: MHNW, MHO, MUNW, and MUO, based on BMI and metabolic syndrome criteria. Correlations between InBody indexes and clinical data were analyzed. Machine learning cluster analysis identified subgroupings, and associations with metabolic diseases were examined. Results: InBody indexes correlated strongly with medical history and lab results. Clustering classified males into two groups and females into three, with significant differences in age, weight, height, BMI, and InBody scores (all Conclusion: The InBody-based clustering analysis showed males could be categorized into 2 subgroups while females could be classified into 3 subgroups, indicating that the population with a specific InBody clustering profile could be at higher risk of metabolic diseases.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.