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Biomarkers in its broadest sense refer to (bio)medical signs, i.e., an objective indication of a medical state, that can be accurately and reproducibly measured. Biomarkers play a huge role in clinical practice and research in adults, but for practical and ethical reasons the number of specific pediatric biomarkers has traditionally been fewer. However, neonatal and pediatric biomarkers absolutely can be used to predict, to prevent, or to diagnose disorders, and to find the right treatment, as well as to monitor treatment effects. Biomarkers can be prognostic, by predicting the recurrence of a disorder, or they can be predictive, by identifying which medicine is the best treatment for each patient as also described in The BEST (Biomarkers, EndpointS, and other Tools) guidelines (
Broadly speaking, markers for infection and/or inflammation is still very much a theme in biomarker research. This is exemplified in this Research Topic by papers exploring correlation between inflammatory markers and clinical conditions as variates. About half the included papers describe biomarkers reflecting the immune response and different conditions: Two papers,
Inflammatory markers were also analyzed in
Genetics and outcome are presented in one paper by
Our research topic contains one review paper by
As encouraging as these papers are, generally in the area of biomarker research, there is often far between the manuscripts concluding with clinically relevant biomarkers. Several biomarkers may be statistically significant, but cannot be used as either screening or diagnostic markers. The perfect diagnostic biomarker, that is, a biomarker with 100% sensitivity and specificity, does not exist, but biomarkers with very low specificity are of poor value for diagnostic purposes. All statistically significant biomarkers may though help in the understanding of the disorders' etiology. A growing trend is quantity over quality, that is, the more markers the better, employing modern high-throughput laboratory techniques called omics, e.g., genomics, proteomics, transcriptomics, metabolomics, and microbiomics methods. The impressive progress in the field has enabled the fast discovery of candidate biomarkers and consequently large numbers of preclinical reports have been published. The relative complexity of these technologies put extra stress on requirement for well-designed biomarker discovery processes to develop clinically relevant biomarkers. The challenge then is to sort out the useful markers, and to make a mathematic formula that is more than just statistically significant, but also actually clinically useful.
When a biomarker has the potential to become a predictive or diagnostic biomarker, biobank resources such as those found in Denmark and other Scandinavian countries with a wealth of accessible health registers become highly relevant to prove the biomarkers potential (
Study design is very important during exploration for biomarkers. Controls should be selected carefully, not only regarding gender, age and BMI, but also regarding treatments, populations etc., and the statistical calculations should be made by people who actually know what they are doing. The more biomarkers available, the more complex the calculations get. Using the wrong statistical methods, almost all studies will find statistically significant biomarkers.
Particularly genomics has clinical appeal: Why use biomarkers in the form of proteins or smaller molecules, when the whole human genome is available? Is it possible to solely use genetic variation either as biomarker for disorders or for personal medicine in the future? Can a few drops of blood and a whole genome sequencing test be enough in the future to both set the diagnosis and to choose the best medicine for the patient? For a few disorders it might be sufficient, but in the majority of cases probably not, as most disorders cannot be explained by genetic variation alone. The lack of effect of medicines can be caused by other molecules in the blood, e.g., environmental factors, or by a combination of both external factors and genetics. In addition, there is the layer of posttranslational modification that is highly tissue-dependent and not directly predictable through a genomic analysis alone (
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