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A longitudinal big data approach for precision health.
Schüssler-Fiorenza Rose, Sophia Miryam; Contrepois, Kévin; Moneghetti, Kegan J; Zhou, Wenyu; Mishra, Tejaswini; Mataraso, Samson; Dagan-Rosenfeld, Orit; Ganz, Ariel B; Dunn, Jessilyn; Hornburg, Daniel; Rego, Shannon; Perelman, Dalia; Ahadi, Sara; Sailani, M Reza; Zhou, Yanjiao; Leopold, Shana R; Chen, Jieming; Ashland, Melanie; Christle, Jeffrey W; Avina, Monika; Limcaoco, Patricia; Ruiz, Camilo; Tan, Marilyn; Butte, Atul J; Weinstock, George M; Slavich, George M; Sodergren, Erica; McLaughlin, Tracey L; Haddad, Francois; Snyder, Michael P.
Afiliação
  • Schüssler-Fiorenza Rose SM; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Contrepois K; Spinal Cord Injury Service, Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
  • Moneghetti KJ; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Zhou W; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Mishra T; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
  • Mataraso S; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Dagan-Rosenfeld O; Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Australia.
  • Ganz AB; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Dunn J; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Hornburg D; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
  • Rego S; Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA.
  • Perelman D; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Ahadi S; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Sailani MR; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Zhou Y; Mobilize Center, Stanford University, Stanford, CA, USA.
  • Leopold SR; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Chen J; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Ashland M; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Christle JW; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Avina M; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Limcaoco P; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Ruiz C; Department of Medicine, University of Connecticut Health, Farmington, CT, USA.
  • Tan M; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Butte AJ; Bakar Computational Health Sciences Institute and Department of Pediatrics, University of California, San Francisco, CA, USA.
  • Weinstock GM; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Slavich GM; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
  • Sodergren E; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • McLaughlin TL; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Haddad F; Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Snyder MP; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Nat Med ; 25(5): 792-804, 2019 05.
Article em En | MEDLINE | ID: mdl-31068711
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 2 / Medicina de Precisão / Big Data Idioma: En Ano de publicação: 2019 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 2 / Medicina de Precisão / Big Data Idioma: En Ano de publicação: 2019 Tipo de documento: Article