학술논문

A longitudinal big data approach for precision health.
Document Type
article
Source
Nature medicine. 25(5)
Subject
Humans
Cardiovascular Diseases
Diabetes Mellitus
Type 2
Insulin Resistance
Risk Factors
Cohort Studies
Longitudinal Studies
Models
Biological
Adult
Aged
Middle Aged
Female
Male
Metabolome
Transcriptome
Exome
Gastrointestinal Microbiome
Precision Medicine
Big Data
Diabetes Mellitus
Type 2
Models
Biological
Immunology
Medical and Health Sciences
Language
Abstract
Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.