Visualization and interpretation of multivariate associations with disease risk markers and disease risk—The triplot
Journal article, 2019

Licensee MDPI, Basel, Switzerland. Metabolomics has emerged as a promising technique to understand relationships between environmental factors and health status. Through comprehensive profiling of small molecules in biological samples, metabolomics generates high-dimensional data objectively, reflecting exposures, endogenous responses, and health effects, thereby providing further insights into exposure-disease associations. However, the multivariate nature of metabolomics data contributes to high complexity in analysis and interpretation. Efficient visualization techniques of multivariate data that allow direct interpretation of combined exposures, metabolome, and disease risk, are currently lacking. We have therefore developed the ‘triplot’ tool, a novel algorithm that simultaneously integrates and displays metabolites through latent variable modeling (e.g., principal component analysis, partial least squares regression, or factor analysis), their correlations with exposures, and their associations with disease risk estimates or intermediate risk factors. This paper illustrates the framework of the ‘triplot’ using two synthetic datasets that explore associations between dietary intake, plasma metabolome, and incident type 2 diabetes or BMI, an intermediate risk factor for lifestyle-related diseases. Our results demonstrate advantages of triplot over conventional visualization methods in facilitating interpretation in multivariate risk modeling with high-dimensional data. Algorithms, synthetic data, and tutorials are open source and available in the R package ‘triplot’.

Environmental factors

Multivariate risk modeling



Disease risk


Tessa Schillemans

Karolinska Institutet

Lin Shi

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Xin Liu

Xi'an Jiaotong University

Agneta Åkesson

Karolinska Institutet

Rikard Landberg

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Umeå University

Carl Brunius

Chalmers, Biology and Biological Engineering, Food and Nutrition Science


2218-1989 (ISSN) 22181989 (eISSN)

Vol. 9 7 133

Subject Categories

Biomedical Laboratory Science/Technology

Environmental Health and Occupational Health

Public Health, Global Health, Social Medicine and Epidemiology



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