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O'Brien CG, Ashland M, Chang DD, Morin DP, Kapil S, Barnett CF, Khanijo S, Stavi D, Goffi A, Fiza B, Patterson A, Carrier FM, Daubert T, Castellucci C, Thompson S, Bughrara N, Zekhtser M, Courchesne K, Mayette M, Parikh R, Mohabir P. MYOCARDIAL INJURY IS NOT ASSOCIATED WITH MORTALITY IN SEVERE COVID-19. J Am Coll Cardiol 2023. [PMCID: PMC9982920 DOI: 10.1016/s0735-1097(23)01085-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Lancaster SM, Lee-McMullen B, Abbott CW, Quijada JV, Hornburg D, Park H, Perelman D, Peterson DJ, Tang M, Robinson A, Ahadi S, Contrepois K, Hung CJ, Ashland M, McLaughlin T, Boonyanit A, Horning A, Sonnenburg JL, Snyder MP. Global, distinctive, and personal changes in molecular and microbial profiles by specific fibers in humans. Cell Host Microbe 2022; 30:848-862.e7. [PMID: 35483363 PMCID: PMC9187607 DOI: 10.1016/j.chom.2022.03.036] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/19/2022] [Accepted: 03/25/2022] [Indexed: 12/11/2022]
Abstract
Dietary fibers act through the microbiome to improve cardiovascular health and prevent metabolic disorders and cancer. To understand the health benefits of dietary fiber supplementation, we investigated two popular purified fibers, arabinoxylan (AX) and long-chain inulin (LCI), and a mixture of five fibers. We present multiomic signatures of metabolomics, lipidomics, proteomics, metagenomics, a cytokine panel, and clinical measurements on healthy and insulin-resistant participants. Each fiber is associated with fiber-dependent biochemical and microbial responses. AX consumption associates with a significant reduction in LDL and an increase in bile acids, contributing to its observed cholesterol reduction. LCI is associated with an increase in Bifidobacterium. However, at the highest LCI dose, there is increased inflammation and elevation in the liver enzyme alanine aminotransferase. This study yields insights into the effects of fiber supplementation and the mechanisms behind fiber-induced cholesterol reduction, and it shows effects of individual, purified fibers on the microbiome.
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Affiliation(s)
- Samuel M Lancaster
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Brittany Lee-McMullen
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Charles Wilbur Abbott
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Jeniffer V Quijada
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Heyjun Park
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Dalia Perelman
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Dylan J Peterson
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Michael Tang
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Aaron Robinson
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Sara Ahadi
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Chia-Jui Hung
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Melanie Ashland
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Tracey McLaughlin
- Division of Endocrinology, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Anna Boonyanit
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Aaron Horning
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Justin L Sonnenburg
- Department of Microbiology & Immunology, Stanford School of Medicine, Stanford, CA 94305, USA; Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA.
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Contrepois K, Wu S, Moneghetti KJ, Hornburg D, Ahadi S, Tsai MS, Metwally AA, Wei E, Lee-McMullen B, Quijada JV, Chen S, Christle JW, Ellenberger M, Balliu B, Taylor S, Durrant MG, Knowles DA, Choudhry H, Ashland M, Bahmani A, Enslen B, Amsallem M, Kobayashi Y, Avina M, Perelman D, Schüssler-Fiorenza Rose SM, Zhou W, Ashley EA, Montgomery SB, Chaib H, Haddad F, Snyder MP. Molecular Choreography of Acute Exercise. Cell 2020; 181:1112-1130.e16. [PMID: 32470399 PMCID: PMC7299174 DOI: 10.1016/j.cell.2020.04.043] [Citation(s) in RCA: 219] [Impact Index Per Article: 54.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 12/10/2019] [Accepted: 04/21/2020] [Indexed: 02/07/2023]
Abstract
Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.
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Affiliation(s)
- Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kegan J Moneghetti
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, VIC, Australia; Stanford Sports Cardiology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sara Ahadi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ming-Shian Tsai
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ahmed A Metwally
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Eric Wei
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Jeniffer V Quijada
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Songjie Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey W Christle
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Stanford Sports Cardiology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Mathew Ellenberger
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brunilda Balliu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Shalina Taylor
- Pediatrics Department, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew G Durrant
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - David A Knowles
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University, Stanford, CA, USA
| | - Hani Choudhry
- Department of Biochemistry, Faculty of Science, Cancer and Mutagenesis Unit, King Fahd Center for Medical Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Melanie Ashland
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Amir Bahmani
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brooke Enslen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Myriam Amsallem
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Yukari Kobayashi
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Monika Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Dalia Perelman
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Euan A Ashley
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Department of Pathology, Stanford University, Stanford, CA, USA
| | - Hassan Chaib
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Francois Haddad
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.
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Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, Dagan-Rosenfeld O, Ganz AB, Dunn J, Hornburg D, Rego S, Perelman D, Ahadi S, Sailani MR, Zhou Y, Leopold SR, Chen J, Ashland M, Christle JW, Avina M, Limcaoco P, Ruiz C, Tan M, Butte AJ, Weinstock GM, Slavich GM, Sodergren E, McLaughlin TL, Haddad F, Snyder MP. A longitudinal big data approach for precision health. Nat Med 2019; 25:792-804. [PMID: 31068711 PMCID: PMC6713274 DOI: 10.1038/s41591-019-0414-6] [Citation(s) in RCA: 234] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 03/06/2019] [Indexed: 12/31/2022]
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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Affiliation(s)
- Sophia Miryam Schüssler-Fiorenza Rose
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Spinal Cord Injury Service, Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kegan J Moneghetti
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Australia
| | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Tejaswini Mishra
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Samson Mataraso
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Orit Dagan-Rosenfeld
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ariel B Ganz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jessilyn Dunn
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Mobilize Center, Stanford University, Stanford, CA, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Shannon Rego
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Dalia Perelman
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sara Ahadi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - M Reza Sailani
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Yanjiao Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Medicine, University of Connecticut Health, Farmington, CT, USA
| | - Shana R Leopold
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jieming Chen
- Bakar Computational Health Sciences Institute and Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Melanie Ashland
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey W Christle
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Monika Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Patricia Limcaoco
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Camilo Ruiz
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Marilyn Tan
- Division of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute and Department of Pediatrics, University of California, San Francisco, CA, USA
| | | | - George M Slavich
- Cousins Center for Psychoneuroimmunology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Erica Sodergren
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Tracey L McLaughlin
- Division of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
| | - Francois Haddad
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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Marchman VA, Loi EC, Adams KA, Ashland M, Fernald A, Feldman HM. Speed of Language Comprehension at 18 Months Old Predicts School-Relevant Outcomes at 54 Months Old in Children Born Preterm. J Dev Behav Pediatr 2018; 39:246-253. [PMID: 29309294 PMCID: PMC5866178 DOI: 10.1097/dbp.0000000000000541] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Identifying which preterm (PT) children are at increased risk of language and learning differences increases opportunities for participation in interventions that improve outcomes. Speed in spoken language comprehension at early stages of language development requires information processing skills that may form the foundation for later language and school-relevant skills. In children born full-term, speed of comprehending words in an eye-tracking task at 2 years old predicted language and nonverbal cognition at 8 years old. Here, we explore the extent to which speed of language comprehension at 1.5 years old predicts both verbal and nonverbal outcomes at 4.5 years old in children born PT. METHOD Participants were children born PT (n = 47; ≤32 weeks gestation). Children were tested in the "looking-while-listening" task at 18 months old, adjusted for prematurity, to generate a measure of speed of language comprehension. Parent report and direct assessments of language were also administered. Children were later retested on a test battery of school-relevant skills at 4.5 years old. RESULTS Speed of language comprehension at 18 months old predicted significant unique variance (12%-31%) in receptive vocabulary, global language abilities, and nonverbal intelligence quotient (IQ) at 4.5 years, controlling for socioeconomic status, gestational age, and medical complications of PT birth. Speed of language comprehension remained uniquely predictive (5%-12%) when also controlling for children's language skills at 18 months old. CONCLUSION Individual differences in speed of spoken language comprehension may serve as a marker for neuropsychological processes that are critical for the development of school-relevant linguistic skills and nonverbal IQ in children born PT.
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Affiliation(s)
| | - Elizabeth C. Loi
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University, Stanford, CA, 94305
| | - Katherine A. Adams
- Department of Applied Psychology, Steinhardt School of Culture, Education, and Human Development, New York University, New York NY 10003
| | - Melanie Ashland
- Department of Psychology, Stanford University, Stanford, CA, 94305
| | - Anne Fernald
- Department of Psychology, Stanford University, Stanford, CA, 94305
| | - Heidi M. Feldman
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University, Stanford, CA, 94305
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