Integrated Personal Omics Profiling
What is the iPOP Study?
The iPOP (Integrated Personal Omics Profiling) study is a longitudinal study of approximately 100 individuals meant to help lay a foundation for precision personalized medicine through the unprecedented deep biochemical profiling of generally healthy individuals. It is designed to understand what “healthy’ biochemical and physiological profiles look like at a personal level and what happens when people become ill. The study was designed and is performed at Stanford University.
Over the course of several years samples are collected from participants at regular intervals, both while they are in good health and in times of illness or significant stress. Whole genome sequencing is performed on all participants, and other omics data collected includes information on how the genome is expressed (transcriptome, proteome, methylome), bacteria and other microorganisms in the gut and on the skin (microbiome), and the intermediate products of metabolism (metabolome). Data is also collected on participants’ diets, stress levels, activity levels, and personal and family medical history. Wearable devices enable tracking of participants physiology and activity. Altogether we make billions of measurements every time we sample someone.
A significant portion of the iPOP cohort is pre-diabetic, and better understanding the way that omics are influenced by a pre-diabetic state and the progression from pre-diabetes to either a normal healthy state or diabetes is another important focus of this study.
Many participants have also participated in one of several sub-studies of iPOP meant to better characterize dynamic changes in omics resulting from controlled periods of weight gain and weight loss, exercise, and fiber supplementation.
Compiling this invaluable data is allowing us to better characterize a normal state of health on the molecular level, as well as identify early signs of disease that may someday lead to the ability to better predict and treat disease in early stages, and perhaps even prevent disease altogether.
1) Personal omics profiling reveals dynamic molecular and medical phenotypes.
Chen R, Mias GI, Li-Pook-Than J, Jiang L, â€¦ Snyder M. Cell. 2012 Mar 16;148(6):1293-307. doi: 10.1016/j.cell.2012.02.009. PMID: 22424236
2) Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information.
Li X, Dunn J, Salins D, Zhou G, Zhou W, SchÃ¼ssler-Fiorenza Rose SM, Perelman D, Colbert E, Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP.
PLoS Biol. 2017 Jan 12;15(1):e2001402. doi: 10.1371/journal.pbio.2001402. PMID: 28081144
3) Longitudinal multi-omics of host–microbe dynamics in prediabetes.
Zhou, W., Sailani, M. R., Contrepois, K., Zhou, Y., Ahadi, S., Leopold, S. R., ... & Snyder, M. (2019). Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature, 569(7758), 663-671.
4) Personal aging markers and ageotypes revealed by deep longitudinal profiling.
Ahadi, S., Zhou, W., Rose, S. M. S. F., Sailani, M. R., Contrepois, K., Avina, M., ... & Snyder, M. (2020). Nature medicine, 26(1), 83-90.
5) A longitudinal big data approach for precision health.
Rose, S. M. S. F., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., ... & Snyder, M. P. (2019). Nature medicine, 25(5), 792-804.
6) Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California.
Sailani, M. R., Metwally, A. A., Zhou, W., Rose, S. M. S. F., Ahadi, S., Contrepois, K., ... & Snyder, M. P. (2020). Nature communications, 11(1), 1-12.
7) Glucotypes reveal new patterns of glucose dysregulation.
Hall, H., Perelman, D., Breschi, A., Limcaoco, P., Kellogg, R., McLaughlin, T., & Snyder, M. (2018). Glucotypes reveal new patterns of glucose dysregulation. PLoS biology, 16(7), e2005143.