A longitudinal big data approach for precision health.
Level 4 - case-series / case-control
Single-arm prospective observational cohort without a comparison group.
PubMed 31068711 · doi:10.1038/s41591-019-0414-6
What was done
Investigators conducted a prospective longitudinal study of 109 individuals enriched for risk of type 2 diabetes mellitus followed quarterly for up to 8 years (median 2.8 years). Participants underwent integrative personalized omics profiling (genome, immunome, transcriptome, proteome, metabolome, microbiome), standard clinical measures, and wearable monitoring to identify subclinical disease risks, molecular pathways, and behavioral changes.
What was found
The authors reported more than 67 clinically actionable health discoveries and identified molecular pathways tied to metabolic, cardiovascular, and oncologic pathophysiology. Omics-based prediction models for insulin resistance were developed. Study participation led the majority of participants to adopt diet and exercise modifications. The abstract reported no specific numerical performance metrics, effect sizes, or exact participant response rates.
Why it matters
The study shows the potential of deep longitudinal multi-omic and physiological profiling to uncover early preclinical disease processes and build non-invasive biomarker models.
Limits
The cohort is small (n = 109) and enriched specifically for type 2 diabetes risk, limiting generalizability. There was no control group receiving standard care to determine if deep profiling translates to improved long-term clinical outcomes.
Cited by
- supports In a longitudinal deep-profiling study of 109 individuals over three years, 49 participants were diagnosed with significant early-stage health conditions including lymphoma, pre-cancers (MGUS and smoldering myeloma), and heart conditions before becoming symptomatic.