As part of the Precision Medicine and Pharmacometabolomics Task Group, Dr. Michael Schmidt presented a lecture at the 2018 Metabolomics Society International Congress in Seattle, WA (June 25-28).  The lecture was entitled, “Precision Medicine & Metabolic Phenotyping in Small Cohorts:  The NASA Twins Study and Beyond.”

Applying integrated omics analysis (genomics, transcriptomics, proteomics, metabolomics, etc.) presents unique challenges when applied to any sized cohort.   This is because of the inherent problems of high dimensional data sets comprised of thousands of variables (analytes).  When small cohorts are examined (subject number from N=1, N=10, etc.), these small subject (sample) numbers are dwarfed by the large variable numbers, which can lead to statistical overfitting and, ultimately, false discoveries.  Moreover, there are many stages in the experimental process where experimental variance can be introduced, which creates noise in trying to identify actual biological variance.  The introduction of experimental variance is seen at the 1) patient (subject) level, 2) physician level, and 3) laboratory level.

Lack of attention to the introduction of experimental variance can lead to the generation of omics data that does not reflect the actual biological condition.  Dr. Schmidt’s presentation was focused on the experimental methods that can be used to optimize the ability to detect true biological variance and, thus, arrive at meaningful conclusions in small cohorts.  This can also include N=1 conditions encountered in the clinic, elite athletics, small military units, small astronaut units, and others.  Dr. Schmidt reviewed design elements of the NASA Twins Study, the Everest Twins Study, and ongoing elite military unit Studies, as representative small cohorts and how refined experimental design elements can be applied to such efforts.