To achieve the goal of precision medicine, not only do different molecular profiles need to be understood in disease populations, but they must also be understood in the context of healthy populations. This especially applies to the stability of molecular profiles among healthy individuals over time, as this will clarify what qualifies as a ‘normal range’ of clinical parameters in health and disease research. The following study by Tebani et al. (2020) conducted a longitudinal analysis of the blood profiles from 100 healthy individuals to understand how they varied both between different individuals, and within an individual over time.Data collection included proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies, and immune cell profiling, complemented by gut microbiota and clinical chemistry analysis. Longitudinal data was collected over 2 years to obtain a detailed, high-resolution personal omics profile for each participant. Proteomics data was obtained using Olink’s PEA technology, which investigated 794 blood plasma proteins.
Figure: Edited figure from Tebani et al. (2020) that shows UMPA values for different omics profiles in 91 subjects. Lines between dots represent different visits (dots) for one individual. Apart from autoantibodies, the proteome is the most stable dataset, varying only slightly between each of the 4 data collection points.
Overall, the study revealed that the omics profiles for each individual were highly unique, but stable over time. However, some types of omics were more stable and predictable than others. Autoantibody profiles were the most stable for each participant, hardly changing over the 2-year period, followed by proteomics profiles. Transcriptomics data was the least stable over time, but highly linked to the plasma proteome. Other general patterns between different groups could also be observed, such as sex-based differences across the whole omics profile. Linear mixed modeling of each omic feature and clinical data also revealed that the plasma proteome has the greatest influence on clinical data parameters like BMI, body composition, heart and kidney function, and blood pressure.
While the findings in this study need to be validated in a larger cohort, they do support the idea that health should be viewed at the level of the individual, rather than being more generalized. The path forward lies in developing a comprehensive longitudinal molecular patient profile, which is no small task. In the meantime, however, it also emphasizes the need to understand health and disease in the context of both sexes, and to avoid sex-biased interpretations. Finally, the stability of the proteomics data emphasizes its potential to empower routine lab tests by providing more biologically relevant insight when interpreting data in both translational and clinical settings. These are just a sample of how Olink and you are accelerating proteomics together.
Read more in the paper linked below:
Tebani et al., 2020, Integration of molecular profiles in a longitudinal wellness profiling cohort, Nature Communications.