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A longitudinal big data approach for precision health.

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.

SUBMITTER: Schussler-Fiorenza Rose SM

PROVIDER: S-EPMC6713274 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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A longitudinal big data approach for precision health.

Schüssler-Fiorenza Rose Sophia Miryam SM Contrepois Kévin K Moneghetti Kegan J KJ Zhou Wenyu W Mishra Tejaswini T Mataraso Samson S Dagan-Rosenfeld Orit O Ganz Ariel B AB Dunn Jessilyn J Hornburg Daniel D Rego Shannon S Perelman Dalia D Ahadi Sara S Sailani M Reza MR Zhou Yanjiao Y Leopold Shana R SR Chen Jieming J Ashland Melanie M Christle Jeffrey W JW Avina Monika M Limcaoco Patricia P Ruiz Camilo C Tan Marilyn M Butte Atul J AJ Weinstock George M GM Slavich George M GM Sodergren Erica E McLaughlin Tracey L TL Haddad Francois F Snyder Michael P MP

Nature medicine 20190508 5

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 ...[more]

PMID: 31068711

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Project description:Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

Dataset Information

A longitudinal big data approach for precision health.

Publications

A longitudinal big data approach for precision health.

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