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. 2019 Aug 16;3(2):92-102.
doi: 10.1159/000502000. eCollection 2019 May-Aug.

Traditional and Digital Biomarkers: Two Worlds Apart?

Lmar M Babrak  1 , Joseph Menetski  2 , Michael Rebhan  3   4 , Giovanni Nisato  5 , Marc Zinggeler  6 , Noé Brasier  7 , Katja Baerenfaller  8 , Thomas Brenzikofer  4 , Laurenz Baltzer  9 , Christian Vogler  10 , Leo Gschwind  10 , Cornelia Schneider  11   12 , Fabian Streiff  4 , Peter M A Groenen  4   13 , Enkelejda Miho  1   4   14
Affiliations

Traditional and Digital Biomarkers: Two Worlds Apart?

Lmar M Babrak et al. Digit Biomark. .

Abstract

The identification and application of biomarkers in the clinical and medical fields has an enormous impact on society. The increase of digital devices and the rise in popularity of health-related mobile apps has produced a new trove of biomarkers in large, diverse, and complex data. However, the unclear definition of digital biomarkers, population groups, and their intersection with traditional biomarkers hinders their discovery and validation. We have identified current issues in the field of digital biomarkers and put forth suggestions to address them during the DayOne Workshop with participants from academia and industry. We have found similarities and differences between traditional and digital biomarkers in order to synchronize semantics, define unique features, review current regulatory procedures, and describe novel applications that enable precision medicine.

Keywords: Biomarker classification; Digital biomarkers; Digital health; Semantics; Traditional biomarkers.

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Conflict of interest statement

J.M. declares holding shares in Merck Pharmaceuticals (MSD outside the USA). P.M.A.G. declares holding shares in Idorsia Pharmaceuticals. E.M. declares holding shares in aiNET GmbH. L.B. is a publishing manager at Karger AG. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Digital technologies have enabled the measurement of digital biomarkers. Traditional biomarkers are divided into seven categories depending on the clinical goal for the biomarker (i.e., safety, predictive, diagnostic, etc.). We propose that digital technologies enable the measurement of digital biomarkers, which contribute new and unique features (e.g., longitudinal, continuous measurements) while sharing the same traditional biomarker clinical goals.
Fig. 2
Fig. 2
User populations. Different populations of data producers of potential digital biomarkers and device users are grouped based on application, size of population, consistency of measure, and data security risk, and are categorized based on the best potential use of the data for discovery of novel digital biomarkers or validation. The y-axis describes the number of individuals measured and the x-axis describes the consistency of the measure. Consistency is defined as accuracy, precision, reproducibility, calibration, and traceability.
Fig. 3
Fig. 3
Biomarker classification. Both traditional and digital biomarkers can be classified based on the status of a particular measurement to a particular clinical status or outcome. A digital biomarker either replaces a non-digital biomarker (Approved), opens a new field (Novel) or is a hybrid that on the one hand replaces and on the other hand opens a new field (Original).
See this image and copyright information in PMC

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