ArticleObesity (Silver Spring, Md.)2022
Three-dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults.
Article in Obesity (Silver Spring, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 23 citations in OpenAlex.
- Abdominal posture biases 3D optical imaging measurements while bioelectrical impedance analysis remains unaffected.Clinical nutrition ESPEN · 2026Article
- 3D body shape-based phenotyping for precision risk stratification of metabolic syndrome.Lipids in health and disease · 2026Article
- Development of a metabolic syndrome prediction model using smartphone-derived digital anthropometry.The British journal of nutrition · 2026Article
- Feasibility and clinical utility of digital anthropometry for precise assessment of outcomes after post-bariatric reconstructive plastic surgery.Frontiers in surgery · 2026Article
- Genetic ancestry influences body shape and obesity risk in Latin American populations.Scientific reports · 2025Article
- The Critical Role of Body Composition Assessment in Advancing Research and Clinical Health Risk Assessment across the Lifespan.Journal of obesity & metabolic syndrome · 2025Review
- Updates on Methods for Body Composition Analysis: Implications for Clinical Practice.Current obesity reports · 2025Review
- Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.International journal of obesity (2005) · 2024Article
- Prediction of total and regional body composition from 3D body shape.NPJ digital medicine · 2024Article
- Trunk-to-leg volume and appendicular lean mass from a commercial 3-dimensional optical body scanner for disease risk identification.Clinical nutrition (Edinburgh, Scotland) · 2024Article
- Prediction of presence and severity of metabolic syndrome using regional body volumes measured by a multisensor white-light 3D scanner and validation using a mobile technology.European heart journal. Digital health · 2024Article
- Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.Research square · 2024Article
- Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners.European journal of clinical nutrition · 2024Article
- Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans.Communications medicine · 2024Article
- Smartphone three-dimensional imaging for body composition assessment using non-rigid avatar reconstruction.Frontiers in medicine · 2024Article
- Relevance of body composition in phenotyping the obesities.Reviews in endocrine & metabolic disorders · 2023Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThis study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics.
methodsA diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model.
resultsA total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001).
conclusionsBody shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.
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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.