Integration of molecular profiles in a longitudinal wellness profiling cohort

Abdellah Tebani(Science for Life Laboratory), Anders Gummesson(Sahlgrenska University Hospital), Wen Zhong(Science for Life Laboratory), Ina Schuppe‐Koistinen(Science for Life Laboratory), Tadepally Lakshmikanth(Science for Life Laboratory), Lisa Olsson(University of Gothenburg), Fredrik Boulund(Karolinska Institutet), Maja Neiman(Science for Life Laboratory), Hans Stenlund(Umeå University), Cecilia Hellström(Science for Life Laboratory), Max Karlsson(Science for Life Laboratory), Muhammad Arif(Science for Life Laboratory), Tea Dodig‐Crnković(Science for Life Laboratory), Adil Mardinoğlu(King's College London), Sunjae Lee(Science for Life Laboratory), Cheng Zhang(Science for Life Laboratory), Yang Chen(Science for Life Laboratory), Axel Olin(Science for Life Laboratory), Jaromír Mikeš(Science for Life Laboratory), Hanna Danielsson(Karolinska Institutet), Kalle von Feilitzen(Science for Life Laboratory), Per-Anders Jansson(Sahlgrenska University Hospital), Oskar Angerås(Sahlgrenska University Hospital), Mikael Huss(Karolinska Institutet), Sanela Kjellqvist(Karolinska Institutet), Jacob Odeberg(Science for Life Laboratory), Fredrik Edfors(Science for Life Laboratory), Valentina Tremaroli(University of Gothenburg), Björn Forsström(Science for Life Laboratory), Jochen M. Schwenk(Science for Life Laboratory), Peter Nilsson(Science for Life Laboratory), Thomas Möritz(Swedish University of Agricultural Sciences), Fredrik Bäckhed(University of Copenhagen), Lars Engstrand(Karolinska Institutet), Petter Brodin(Science for Life Laboratory), Göran Bergström(Sahlgrenska University Hospital), Mathias Uhlén(Science for Life Laboratory), Linn Fagerberg(Science for Life Laboratory)
Nature Communications
September 8, 2020
Cited by 151Open Access
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Abstract

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.


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