Evidence map›Paper›PMID 40301848›Full record

ArticleLipids in health and disease2025

Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes.

Jiaying Ge, Siqi Sun, Jiangping Zeng, Yujie Jing, Huihui Ma, Chunhua Qian, Ran Cui, Shen Qu, Hui Sheng

Abstract readValidation Study
PubMed Full text at PMC Publisher
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. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

9 authors.

Jiaying GeDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Siqi SunDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Jiangping ZengDepartment of Endocrinology and Metabolism, Suzhou Dushu Lake Hospital, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, 215123, Jiangsu, China.
Yujie JingDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Huihui MaDepartment of Endocrinology, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, 250000, China.
Chunhua QianDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Ran CuiDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Shen QuDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Hui ShengDepartment of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China. shenghui@tongji.edu.cn.

Funding

National Natural Science Foundation of China No. 82170894the Research Physician Project of Shanghai Tenth People's Hospital 2023YJXYSA014
6 · The paper itself

Abstract

BACKGROUND AND

aimsLow muscle mass (LMM) is a critical complication in patients with obesity and diabetes, exacerbating metabolic and cardiovascular risks. Novel obesity indices, such as the body roundness index (BRI), conicity index, and relative fat mass, have shown promise for assessing body composition. This study aimed to investigate the associations of these indices with LMM and to develop machine learning models for accurate and accessible LMM prediction.

methodData from NHANES 2011-2018 (n = 2,176) were analyzed. Obesity was defined by body fat percentage, and LMM was determined using skeletal muscle mass index thresholds adjusted for BMI. Predictive models were developed using logistic regression, random forest, and other algorithms, with feature selection via LASSO regression. Validation included NHANES 2005-2006 data (n = 310). Model performance was evaluated using AUROC, Brier scores, and SHapley Additive exPlanations (SHAP) for feature importance.

resultsBRI was independently associated with LMM (odds ratio 1.39, 95% confidence interval 1.22-1.58; P < 0.001). Eight features were included in the random forest model, which achieved excellent discrimination (AUROC = 0.721 in the validation set) and calibration (Brier score = 0.184). Feature importance analysis highlighted BRI, creatinine, race, age, and HbA1c as key contributors to the model's predictive performance. SHAP analysis emphasized BRI's role in predicting LMM. An online prediction tool was developed.

conclusionsBRI is a significant predictor of LMM in patients with obesity and diabetes. The random forest model demonstrated strong performance and offers a practical tool for early LMM detection, supporting clinical decision-making and personalized interventions.

Indexed as

Diabetes MellitusMachine LearningMuscle, SkeletalObesitySarcopeniaAdultAgedBody CompositionBody Mass IndexFemaleHumansMaleMiddle AgedNutrition SurveysDiabetesLow muscle massMachine learningNHANESObesity

Identifiers

PMID40301848
PMCPMC12039300

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 OpenQuestion graph.