Objectives: There is a need for precision medicine and an unspoken promise of an optimal approach for identification of the right patients for value-based medicine based on big data. However, there may be a misconception that measurement of proteins is more valuable than measurement of fewer selected biomarkers. In population-based research, variation may be somewhat eliminated by quantity. However, this fascination of numbers may limit the attention to and understanding of the single. This review highlights that protein measurements (with collagens as examples) may mean different things depending on the targeted epitope - formation or degradation of tissues, and even signaling potential of proteins.

Design and methods: PubMed was searched for collagen, neo-epitope, biomarkers.

Results: Ample examples of assays with specific epitopes, either pathological such as HbA1c, or domain specific such as pro-peptides, which total protein arrays would not have identified were evident.

Conclusions: We suggest that big data may be considered as the funnel of data points, in which most important parameters will be selected. If the technical precision is low or the biological accuracy is limited, and we include suboptimal quality of biomarkers, disguised as big data, we may not be able to fulfill the promise of helping patients searching for the optimal treatment. Alternatively, if the technical precision of the total protein quantification is high, but we miss the functional domains with the most considerable biological meaning, we miss the most important and valuable information of a given protein. This review highlights that measurements of the same protein in different ways may provide completely different meanings. We need to understand the pathological importance of each epitope quantified to maximize protein measurements.

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