SUPR
Wellness profiling study
Dnr:

sens2018115

Type:

SNIC SENS

Principal Investigator:

Fredrik Edfors

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2018-07-02

End Date:

2024-09-01

Primary Classification:

10610: Bioinformatics and Systems Biology (methods development to be 10203)

Allocation

  • Castor /proj at UPPMAX: 18000 GiB
  • Cygnus /proj at UPPMAX: 18000 GiB
  • Castor /proj/nobackup at UPPMAX: 3500 GiB
  • Cygnus /proj/nobackup at UPPMAX: 3500 GiB
  • Bianca at UPPMAX: 10 x 1000 core-h/month

Abstract

The Swedish SCAPIS SciLifeLab (S3) Wellness Profiling program is based on the Swedish CArdioPulmonary bioImage Study (SCAPIS), a large prospective clinical study involving 30,000 individuals with extensive clinical phenotyping, as well as on the Human Protein Atlas project (www.proteinatlas.org), where a combination of genomics, transcriptomics, proteomics and antibody-based profiling is used to study the global protein expression patterns in human cells, tissues and organs. In the ongoing pilot study, 100 participants are followed longitudinally every three months after their baseline examination with repeated analyses of molecular markers in blood, urine and stool samples in combination with physical measurements and continuous monitoring of biological signals like sleep and activity. The samples are analyzed in a large number of platforms including several complementary proteomics methods (Olink PEA, antibody bead arrays, autoimmunity profiling and targeted proteomics), immunology (CyTOF), genomics (WGS), transcriptomics (RNA sequencing), microbiome analyses (16s RNA and metagenomics) and metabolomics (plasma/urine/lipids). In addition, the data collection also consists of extensive lifestyle and imaging data from SCAPIS including MRI, CT-scans and ultrasound analysis. We have studied the longitudinal effects of lifestyle variation in a healthy population based on personalized expression profiles from different platforms including transcriptomics and proteomics. Using multivariate methods, we are also integrating clinical diagnostics, activity and immunology data with omics data to model early signs of atherosclerosis and other diseases. A large collection of data from multiple platforms is used for integrative studies to understand the normal variation of molecular profiles in healthy individuals over time with the goal to facilitate a molecular definition of health.