A longitudinal big data approach for precision health.

Schüssler-Fiorenza Rose SM1,2,3, Contrepois K1, Moneghetti KJ4,5,6, Zhou W1, Mishra T1, Mataraso S7,8, Dagan-Rosenfeld O1, Ganz AB1, Dunn J1,9, Hornburg D1, Rego S1, Perelman D1, Ahadi S1, Sailani MR1, Zhou Y10,11, Leopold SR10, Chen J12, Ashland M1, Christle JW4,5, Avina M1, Limcaoco P1, Ruiz C13, Tan M14, Butte AJ12, Weinstock GM10, Slavich GM15, Sodergren E10, McLaughlin TL14, Haddad F16,17, Snyder MP18,19.

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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.