Evidence map›Paper›PMID 41353430›Full record

ArticleLipids in health and disease2025

Association of the body roundness index and sun sensitivity: insights from the NHANES data with machine learning analysis.

Zhaofu Tan, Yanjie Chen, Yi Ou, Xinyi Shao, Qian Liu, Yidian Fu, Aijun Chen, Genlong Bai, Jingbo Zhang

Abstract read
In one paragraph

Article in Lipids in health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Zhaofu Tan *Department of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yanjie Chen *Department of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yi OuDepartment of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xinyi ShaoDepartment of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Qian LiuDepartment of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yidian FuGraduate School of Hebei Medical University, Shijiazhuang, 050017, Hebei, China.
Aijun ChenDepartment of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. chenaijun@hospital.cqmu.edu.cn.
Genlong BaiDepartment of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. 934147726@qq.com.
Jingbo ZhangDepartment of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. 49554556samael@gmail.com.

Funding

China Postdoctoral Science Foundation 2024M763893Natural Science Foundation Project of Chongqing 2024NSCQ-LZX0086Outstanding Youth Foundation of Chongqing Province CSTB2024YCJH-KYXM0101The First Clinical College Clinical medicine first-class discipline construction project to department of Dermatology and Venereology to Zhaofu Tan CYYY-BSYJSCXXM-202333
6 · The paper itself

Abstract

backgroundSun sensitivity, an abnormal skin reaction to ultraviolet radiation, increases the risk of melanoma and impairs the quality of life. In recent times, the body roundness index (BRI) has been suggested to be associated with various health conditions. Nevertheless, the association between the BRI and sun sensitivity remains not well understood. The purpose of this research was to investigate the association between the BRI and sun sensitivity.

methodsThis research utilized information acquired from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States (2001-2006, 2009-2018; n = 9,999, including 1,085 cases of sun sensitivity). The association between the BRI and sun sensitivity was analyzed, and the Boruta algorithm was employed for feature selection. Subsequently, seven machine learning (ML) models were used to predict the risk of sun sensitivity, and the independent effect of the BRI was analyzed using Shapley additive explanations (SHAP).

resultsAfter full adjustment, each one-unit increase in the BRI significantly increased the risk of sun sensitivity (OR = 1.08, 95% CI: 1.04-1.11, P < 0.001). Quartile analysis showed that participants in the highest BRI quartile (≥ 6.32) had a 47% higher risk of sun sensitivity (OR = 1.47, 95% CI: 1.17-1.83, P < 0.001) than those in the lowest quartile. RCS confirmed a linear dose-response relationship (P

conclusionsThis is the first study to systematically evaluate the relationship between the BRI and risk of sun sensitivity using ML methods. The study identified a significant association between the BRI and risk of sun sensitivity. These findings provide new evidence regarding the etiology of sun sensitivity and offer a scientific basis for personalized disease management and public health interventions.

Indexed as

Machine LearningSunlightAdultAgedFemaleHumansMaleMelanomaMiddle AgedNutrition SurveysRisk FactorsUnited StatesBRIMachine learningNHANESSHAPSun sensitivity

Identifiers

PMID41353430
PMCPMC12683886

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LicenceCC BY-NC-ND
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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.