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Global Translational Medicine Personalized, multi-omics disease detection

medicine, offering a paradigm shift toward more effective both healthy and ill states. The study encompasses whole
and efficient healthcare. genomics, transcriptomics, proteomics, methylomics and
metabolomics as well as microbiome information. It also
6. Envisioned future in healthcare: considers lifestyle factors, such as diet, stress and activity
a convergence of multi-omics levels, along with wearable device (including CGM) data
characterization and wearable technology for tracking physiology and activity. The research aimed
to characterize normal health at a molecular level, detect
Here, in the last part of this review, I would like to preface early disease indicators and potentially enable early disease
with a summary of some existing pioneering work prediction and prevention. The study discovered more
merging personalized multi-omics and wearable health than 67 clinically actionable health deviations, developed
technologies. Afterward, I will conclude by envisioning prediction models for insulin resistance and identified
the inclusion of these technologies into clinical trials for multiple molecular pathways associated with metabolic,
improved outcomes. cardiovascular and even oncologic pathophysiology.
Personalized medicine is at the forefront of the current Interestingly, the predictive models to assess insulin
medical revolution. Rather than relying on population- resistance were built on omics measurements and
based reference ranges, diagnostics has the potential demonstrate the possibility of replacing some clinical tests
to now consider an individual’s baseline profile. This that today are rather laborious. However, since current
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enables the detection of deviations from one’s unique healthcare practices can be limited in collecting clinical
molecular profile, flagging potential health issues long material in a longitudinal fashion, the team adopted a
before clinical symptoms manifest. This is all exemplified Mitra device, a micro-sampler that conveniently collected
by the integrated Personal Omics Profiling (iPOP) studies capillary fingerpick blood. This enabled frequent and dense
where researchers at Stanford University conducted a multi-omics micro-sampling in 10 μL of blood alongside
comprehensive and longitudinal analysis on individuals. physiological information from wearable sensors. From
Their proof-of-principle study involved a single, relatively these restrictive biosamples, the team analyzed shallow
healthy individual who was analyzed with whole-genome proteomes, lipidomes and metabolomes. In this study, they
sequencing, transcriptomics, proteomics, metabolomics, investigated the effect of a complex nutritional shake on
and autoantibody profiles over time. The analyses revealed metabolic profiles, and performed a dense 24/7 profiling
the individuals’ medical risks, such as type 2 diabetes and (98 microsamples) over 7 days. In the first part, after data
dynamic molecular changes across health and disease states cleaning and annotation, 769 analytes were detected from
(during two viral infections), emphasizing the importance microsamples, including 560 metabolites, 155 lipids and
of combining genomic information with continuous 54 cytokines/hormones. The metabolic response to the
physiological monitoring for personalized medicine, and shake was seen across all classes of molecules and each
the significance of personal baseline characterization. In a participant had a unique molecular profile, indicating high
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follow-up study, the researchers explored the use of portable inter-individual variability in the metabolism of nutrients.
biosensors to monitor human physiology during different In the second part of the study, a single participant collected
activities. The biosensors measured three physiological blood microsamples every 1 – 2 h over 7 days, along with
parameters (heart rate, skin temperature, and peripheral wearable data from a smartwatch, a CGM, and food
capillary oxygen saturation), six activity-related parameters logging using an app. Ninety-eight microsamples were
(sleep, steps, walking, biking, running, and calories), weight, collected from the individual and used for multi-omics
and total gamma and X-ray radiation exposure. By collecting profiling, which generated outcomes for 2,213 analytes and
over 250,000 daily measurements from the biosensors of 214,661 biochemical measurements, along with wearable
multiple individuals, it uncovered personalized circadian physiological data, providing comprehensive data on human
variations and significant physiological changes in specific physiome and lifestyle. In this part of the study, they found
environments, such as airline flights. These biosensors also that high-frequency internal multi-omics data can monitor
helped in identifying early signs of diseases like Lyme disease and reflect the participant’s health status and that wearable
and distinguishing between insulin-sensitive and -resistant data can predict internal molecular changes on an hourly
individuals, indicating their potential for managing scale at an individual level, including building predictive
health and improving healthcare access. In yet another models. The study also identified circadian rhythms of
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follow-up iPOP study, they monitored approximately internal molecules in human blood and revealed rhythmic
100 individuals at risk for diabetes mellitus and aimed to molecules and demonstrated that lipids related to energy
establish a foundation for precision personalized medicine metabolism have distinct circadian patterns. Figure 1
by deeply profiling biochemical and physiological data in provides an overview of the iPOP study. 84

Volume 3 Issue 1 (2024) 6 https://doi.org/10.36922/gtm.2357