• Weihong Zhou, Yingjie Wang, Xiaoping Gu, Zhong-Ping Feng, Kang Lee, Yuzhu Peng, and Andrew Barszczyk.
• Health Management Centre, Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing, China.
• Int. J. Clin. Pract. 2021 Jan 1; 75 (1): e13664.

IntroductionBlood biomarkers are measured for their ability to characterise physiological and disease states. Much is known about linear relations between blood biomarker concentrations and individual vital signs or adiposity indexes (eg, BMI). Comparatively little is known about non-linear relations with these easily accessible features, particularly when they are modelled in combination and can potentially interact with one another.MethodsIn this study, we used advanced machine learning algorithms to create non-linear computational models for predicting blood biomarkers (cells, lipids, metabolic factors) from age, general adiposity (BMI), visceral adiposity (Waist-to-Height Ratio, a Body Shape Index) and vital signs (systolic blood pressure, diastolic blood pressure, pulse). We determined the predictive power of the overall feature set. We further calculated feature importance in our models to identify the features with the strongest relations with each blood biomarker. Data were collected in 2018 and 2019 and analysed in 2020.ResultsOur findings characterise previously unknown relations between these predictors and blood biomarkers; in many instances the importance of certain features or feature classes (general adiposity, visceral adiposity or vital signs) differed from their expected contribution based on simplistic linear modelling techniques.ConclusionsThis work could lead to the formation of new hypotheses for explaining complex biological systems and informs the creation of predictive models for potential clinical applications.© 2020 John Wiley & Sons Ltd.

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