ArticleScientific reports2022
Normalized sensitivity of multi-dimensional body composition biomarkers for risk change prediction.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 11 citations in OpenAlex.
- Understanding the Impact of Weight on Quality of Life and Willingness to Pay for Obesity Management in the APAC Region.Clinical obesity · 2026Article
- Is a Large Neck Circumference a Predictive Factor for Poor Semen Quality in the Turkish Population?Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling.International journal of obesity (2005) · 2024Article
- Beyond Body Mass Index: Accurate Metabolic Disease-Risk Phenotyping With 3D Smartphone Application.Obesity science & practice · 2024Article
- Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.Research square · 2024Article
- Moderating effects of body composition biomarkers on the relationship between thyroid hormones and cognitive performance in euthyroid older adults: insights from NHANES data.Frontiers in endocrinology · 2024Article
- Modeling health risks using neural network ensembles.PloS one · 2024Article
- Body Composition Evaluation and Clinical Markers of Cardiometabolic Risk in Patients with Phenylketonuria.Nutrients · 2023Review
- Development and validation of an accurate smartphone application for measuring waist-to-hip circumference ratio.NPJ digital medicine · 2023Article
- Article
- Beyond BMI.Nutrients · 2023Review
- The Importance of HDL-Cholesterol and Fat-Free Percentage as Protective Markers in Risk Factor Hierarchy for Patients with Metabolic Syndrome.Metabolites · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The limitations of BMI as a measure of adiposity and health risks have prompted the introduction of many alternative biomarkers. However, ranking diverse biomarkers from best to worse remains challenging. This study aimed to address this issue by introducing three new approaches: (1) a calculus-derived, normalized sensitivity score (NORSE) is used to compare the predictive power of diverse adiposity biomarkers; (2) multiple biomarkers are combined into multi-dimensional models, for increased sensitivity and risk discrimination; and (3) new visualizations are introduced that convey complex statistical trends in a compact and intuitive manner. Our approach was evaluated on 23 popular biomarkers and 6 common medical conditions using a large database (National Health and Nutrition Survey, NHANES, N ~ 100,000). Our analysis established novel findings: (1) regional composition biomarkers were more predictive of risk than global ones; (2) fat-derived biomarkers had stronger predictive power than weight-related ones; (3) waist and hip are always elements of the strongest risk predictors; (4) our new, multi-dimensional biomarker models yield higher sensitivity, personalization, and separation of the negative effects of fat from the positive effects of lean mass. Our approach provides a new way to evaluate adiposity biomarkers, brings forth new important clinical insights and sets a path for future biomarker research.
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