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. 2022 Jul 20;12(1):12375.
doi: 10.1038/s41598-022-16142-1.

Normalized sensitivity of multi-dimensional body composition biomarkers for risk change prediction

A Criminisi  1   2 , N Sorek  3 , S B Heymsfield  4   5
Affiliations

Normalized sensitivity of multi-dimensional body composition biomarkers for risk change prediction

A Criminisi et al. Sci Rep. .

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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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
1D and 2D biomarker models. (A) A 1D d-map for a given population. (B) The corresponding 1D p-map for a condition C of interest. (C) A 2D d-map for a given population. (D) The corresponding 2D p-map. NORSE scores are indicated at the end of each row and column.
Figure 2
Figure 2
Age stratification of NORSE for C = diabetes in adult men (A) and women (B). (C) NORSE curve as a function of age.
Figure 3
Figure 3
2D biometric models for X = weight, Y = waist circumference, C = diabetes for adult men (A, B) and women (C, D).
Figure 4
Figure 4
Prevalence maps and average NORSE scores for X = Weight, Y = Waist, C = cancer, for adult men. (A) including smokers. (B) excluding smokers. (C, D) Same as above but for hypertension.
See this image and copyright information in PMC

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