1
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Buthmann JL, Miller JG, Aghaeepour N, King LS, Stevenson DK, Shaw GM, Wong RJ, Gotlib IH. Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study. J Child Psychol Psychiatry 2024; 65:1098-1107. [PMID: 38287782 PMCID: PMC11265978 DOI: 10.1111/jcpp.13948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/23/2023] [Indexed: 01/31/2024]
Abstract
BACKGROUND Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties. METHODS Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist). RESULTS We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes. CONCLUSIONS Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.
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Affiliation(s)
- Jessica L. Buthmann
- Department of Psychology, Stanford University, 450 Serra Mall, Stanford CA 94305
| | - Jonas G. Miller
- Department of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Unit 1020, Storrs, CT 06269-1020
| | - Nima Aghaeepour
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305
- Department of Biomedical Data Science, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305
| | - Lucy S. King
- Department of Psychology, Stanford University, 450 Serra Mall, Stanford CA 94305
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305
| | - Ronald J. Wong
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Stanford, CA 94305
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, 450 Serra Mall, Stanford CA 94305
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2
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Alam SF, Gonzalez Suarez ML. Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension. Clin Pract 2024; 14:1357-1374. [PMID: 39051303 PMCID: PMC11270379 DOI: 10.3390/clinpract14040109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/20/2024] [Accepted: 07/03/2024] [Indexed: 07/27/2024] Open
Abstract
This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at an individual and global level. We focus on five key areas: risk prediction, diagnosis, education, monitoring, and management of hypertension, supplemented with a brief look into the works on hypertensive disease of pregnancy. For each area, we discuss the advantages and disadvantages of integrating AI. While AI, in its current rudimentary form, cannot replace sound clinical judgment, it can still enhance faster diagnosis, education, prevention, and management. The integration of AI in healthcare is poised to revolutionize hypertension care, although careful implementation and ongoing research are essential to mitigate risks.
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Affiliation(s)
- Sreyoshi F. Alam
- Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA
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3
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Domingues R, Batista P, Pintado M, Oliveira-Silva P, Rodrigues PM. Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysis. Heliyon 2024; 10:e31721. [PMID: 38867964 PMCID: PMC11167299 DOI: 10.1016/j.heliyon.2024.e31721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 05/06/2024] [Accepted: 05/21/2024] [Indexed: 06/14/2024] Open
Abstract
This study aimed to explore more efficient ways of administering caffeine to the body by investigating the impact of caffeine on the modulation of the nervous system's activity through the analysis of electrocardiographic signals (ECG). An ECG non-linear multi-band analysis using Discrete Wavelet Transform (DWT) was employed to extract various features from healthy individuals exposed to different caffeine consumption methods: expresso coffee (EC), decaffeinated coffee (ED), Caffeine Oral Films (OF_caffeine), and placebo OF (OF_placebo). Non-linear feature distributions representing every ECG minute time series have been selected by PCA with different variance percentages to serve as inputs for 23 machine learning models in a leave-one-out cross-validation process for analyzing the behavior differences between ED/EC and OF_placebo/OF_caffeine groups, respectively, over time. The study generated 50-point accuracy curves per model, representing the discrimination power between groups throughout the 50 min. The best model accuracies for ED/EC varied between 30 and 70 %, (using the decision tree classifier) and OF_placebo/OF_caffeine ranged from 62 to 84 % (using Fine Gaussian). Notably, caffeine delivery through OFs demonstrated effective capacity compared to its placebo counterpart, as evidenced by significant differences in accuracy curves between OF_placebo/OF_caffeine. Caffeine delivery via OFs also exhibited rapid dissolution efficiency and controlled release rate over time, distinguishing it from EC. The study supports the potential of caffeine delivery through Caffeine OFs as a superior technology compared to traditional methods by means of ECG analysis. It highlights the efficiency of OFs in controlling the release of caffeine and underscores their promise for future caffeine delivery systems.
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Affiliation(s)
- Rita Domingues
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Patrícia Batista
- Universidade Católica Portuguesa, Faculty of Education and Psychology, Research Centre for Human Development, Human Neurobehavioral Laboratory, Rua de Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Manuela Pintado
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Patrícia Oliveira-Silva
- Universidade Católica Portuguesa, Faculty of Education and Psychology, Research Centre for Human Development, Human Neurobehavioral Laboratory, Rua de Diogo Botelho 1327, 4169-005, Porto, Portugal
| | - Pedro Miguel Rodrigues
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, 4169-005, Porto, Portugal
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4
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Stevenson DK, Winn VD, Shaw GM, England SK, Wong RJ. Solving the Puzzle of Preterm Birth. Clin Perinatol 2024; 51:291-300. [PMID: 38705641 DOI: 10.1016/j.clp.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Solving the puzzle of preterm birth has been challenging and will require novel integrative solutions as preterm birth likely arises from many etiologies. It has been demonstrated that many sociodemographic and psychological determinants of preterm birth relate to its complex biology. It is this understanding that has enabled the development of a novel preventative strategy, which integrates the omics profile (genome, epigenome, transcriptome, proteome, metabolome, microbiome) with sociodemographic, environmental, and psychological determinants of individual pregnant people to solve the puzzle of preterm birth.
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Affiliation(s)
- David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Room 2652, Stanford, CA 94305, USA.
| | - Virginia D Winn
- Department of Obstetrics and Gynecology, Division of Reproductive, Stem Cell and Perinatal Biology, Stanford University of School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Module 2700, Stanford, CA 94305, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Room 2652, Stanford, CA 94305, USA
| | - Sarah K England
- Department of Obstetrics and Gynecology, Center for Reproductive Health Sciences, Washington University School of Medicine, 425 S. Euclid Avenue, CB 8064, St. Louis, MO 63110, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Room 2652, Stanford, CA 94305, USA
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5
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Feyaerts D, Marić I, Arck PC, Prins JR, Gomez-Lopez N, Gaudillière B, Stelzer IA. Predicting Spontaneous Preterm Birth Using the Immunome. Clin Perinatol 2024; 51:441-459. [PMID: 38705651 DOI: 10.1016/j.clp.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Throughout pregnancy, the maternal peripheral circulation contains valuable information reflecting pregnancy progression, detectable as tightly regulated immune dynamics. Local immune processes at the maternal-fetal interface and other reproductive and non-reproductive tissues are likely to be the pacemakers for this peripheral immune "clock." This cellular immune status of pregnancy can be leveraged for the early risk assessment and prediction of spontaneous preterm birth (sPTB). Systems immunology approaches to sPTB subtypes and cross-tissue (local and peripheral) interactions, as well as integration of multiple biological data modalities promise to improve our understanding of preterm birth pathobiology and identify potential clinically actionable biomarkers.
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Affiliation(s)
- Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305, USA
| | - Ivana Marić
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Petra C Arck
- Department of Obstetrics and Fetal Medicine and Hamburg Center for Translational Immunology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20251 Hamburg, Germany
| | - Jelmer R Prins
- Department of Obstetrics and Gynecology, University of Groningen, University Medical Center Groningen, Postbus 30.001, 9700RB, Groningen, The Netherlands
| | - Nardhy Gomez-Lopez
- Department of Obstetrics and Gynecology, Washington University School of Medicine, 425 S. Euclid Avenue, St. Louis, MO 63110, USA; Department of Pathology and Immunology, Washington University School of Medicine, 425 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA 94305, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Palo Alto, CA 94304, USA
| | - Ina A Stelzer
- Department of Pathology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
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6
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Seong D, Espinosa C, Aghaeepour N. Computational Approaches for Predicting Preterm Birth and Newborn Outcomes. Clin Perinatol 2024; 51:461-473. [PMID: 38705652 PMCID: PMC11070639 DOI: 10.1016/j.clp.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.
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Affiliation(s)
- David Seong
- Immunology Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Medical Scientist Training Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
| | - Camilo Espinosa
- Immunology Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Biomedical Data Science, Stanford University, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Biomedical Data Science, Stanford University, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA.
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7
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Marić I, Stevenson DK, Aghaeepour N, Gaudillière B, Wong RJ, Angst MS. Predicting Preterm Birth Using Proteomics. Clin Perinatol 2024; 51:391-409. [PMID: 38705648 PMCID: PMC11186213 DOI: 10.1016/j.clp.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.
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Affiliation(s)
- Ivana Marić
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA.
| | - David K Stevenson
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305, USA
| | - Ronald J Wong
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA
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8
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Khorami-Sarvestani S, Vanaki N, Shojaeian S, Zarnani K, Stensballe A, Jeddi-Tehrani M, Zarnani AH. Placenta: an old organ with new functions. Front Immunol 2024; 15:1385762. [PMID: 38707901 PMCID: PMC11066266 DOI: 10.3389/fimmu.2024.1385762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024] Open
Abstract
The transition from oviparity to viviparity and the establishment of feto-maternal communications introduced the placenta as the major anatomical site to provide nutrients, gases, and hormones to the developing fetus. The placenta has endocrine functions, orchestrates maternal adaptations to pregnancy at different periods of pregnancy, and acts as a selective barrier to minimize exposure of developing fetus to xenobiotics, pathogens, and parasites. Despite the fact that this ancient organ is central for establishment of a normal pregnancy in eutherians, the placenta remains one of the least studied organs. The first step of pregnancy, embryo implantation, is finely regulated by the trophoectoderm, the precursor of all trophoblast cells. There is a bidirectional communication between placenta and endometrium leading to decidualization, a critical step for maintenance of pregnancy. There are three-direction interactions between the placenta, maternal immune cells, and the endometrium for adaptation of endometrial immune system to the allogeneic fetus. While 65% of all systemically expressed human proteins have been found in the placenta tissues, it expresses numerous placenta-specific proteins, whose expression are dramatically changed in gestational diseases and could serve as biomarkers for early detection of gestational diseases. Surprisingly, placentation and carcinogenesis exhibit numerous shared features in metabolism and cell behavior, proteins and molecular signatures, signaling pathways, and tissue microenvironment, which proposes the concept of "cancer as ectopic trophoblastic cells". By extensive researches in this novel field, a handful of cancer biomarkers has been discovered. This review paper, which has been inspired in part by our extensive experiences during the past couple of years, highlights new aspects of placental functions with emphasis on its immunomodulatory role in establishment of a successful pregnancy and on a potential link between placentation and carcinogenesis.
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Affiliation(s)
- Sara Khorami-Sarvestani
- Reproductive Immunology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
- Monoclonal Antibody Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Negar Vanaki
- Department of Immunology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Sorour Shojaeian
- Department of Biochemistry, School of Medical Sciences, Alborz University of Medical Sciences, Karaj, Iran
| | - Kayhan Zarnani
- Department of Immunology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Allan Stensballe
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
| | - Mahmood Jeddi-Tehrani
- Monoclonal Antibody Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Amir-Hassan Zarnani
- Reproductive Immunology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
- Department of Immunology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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9
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Becker M, Fehr K, Goguen S, Miliku K, Field C, Robertson B, Yonemitsu C, Bode L, Simons E, Marshall J, Dawod B, Mandhane P, Turvey SE, Moraes TJ, Subbarao P, Rodriguez N, Aghaeepour N, Azad MB. Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment. Sci Rep 2024; 14:2977. [PMID: 38316895 PMCID: PMC10844250 DOI: 10.1038/s41598-024-52323-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
Abstract
Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e-05) and maternal intake of fish (p = 4.1e-03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.
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Affiliation(s)
- Martin Becker
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada
- Stanford University, Stanford, 94305, USA
| | - Kelsey Fehr
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada
- Manitoba Interdisciplinary Lactation Centre (MILC), Winnipeg, Canada
- Children's Hospital Research Institute of Manitoba, Winnipeg, Canada
- University of Manitoba, Winnipeg, R3E3P4, Canada
| | - Stephanie Goguen
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada
- Manitoba Interdisciplinary Lactation Centre (MILC), Winnipeg, Canada
- Children's Hospital Research Institute of Manitoba, Winnipeg, Canada
- University of Manitoba, Winnipeg, R3E3P4, Canada
| | - Kozeta Miliku
- University of Toronto, Toronto, M5S 1A8, Canada
- McMaster University, Hamilton, M5S 1A8, Canada
| | | | | | - Chloe Yonemitsu
- University of California, San Diego, La Jolla, CA, 92093, USA
| | - Lars Bode
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada
- University of California, San Diego, La Jolla, CA, 92093, USA
| | | | | | | | | | - Stuart E Turvey
- University of British Columbia and British Columbia Children's Hospital, Vancouver, V5Z4H4, Canada
| | | | - Padmaja Subbarao
- University of Toronto, Toronto, M5S 1A8, Canada
- McMaster University, Hamilton, M5S 1A8, Canada
- SickKids, Toronto, M5G 0A4, Canada
| | - Natalie Rodriguez
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada
- Manitoba Interdisciplinary Lactation Centre (MILC), Winnipeg, Canada
- Children's Hospital Research Institute of Manitoba, Winnipeg, Canada
- University of Manitoba, Winnipeg, R3E3P4, Canada
| | - Nima Aghaeepour
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada.
- Stanford University, Stanford, 94305, USA.
| | - Meghan B Azad
- International Milk Composition (IMiC) Consortium, Winnipeg, Canada.
- Manitoba Interdisciplinary Lactation Centre (MILC), Winnipeg, Canada.
- Children's Hospital Research Institute of Manitoba, Winnipeg, Canada.
- University of Manitoba, Winnipeg, R3E3P4, Canada.
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10
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Youssef L, Testa L, Crovetto F, Crispi F. 10. Role of high dimensional technology in preeclampsia (omics in preeclampsia). Best Pract Res Clin Obstet Gynaecol 2024; 92:102427. [PMID: 37995432 DOI: 10.1016/j.bpobgyn.2023.102427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/05/2023] [Accepted: 08/06/2023] [Indexed: 11/25/2023]
Abstract
Preeclampsia is a pregnancy-specific disease that has no known precise cause. Integrative biology approach based on multi-omics has been applied to identify upstream pathways and better understand the pathophysiology of preeclampsia. At DNA level, genomics and epigenomics studies have revealed numerous genetic variants associated with preeclampsia, including those involved in regulating blood pressure and immune response. Transcriptomics analyses have revealed altered expression of genes in preeclampsia, particularly those related to inflammation and angiogenesis. At protein level, proteomics studies have identified potential biomarkers for preeclampsia diagnosis and prediction in addition to revealing the main pathophysiological pathways involved in this disease. At metabolite level, metabolomics has highlighted altered lipid and amino acid metabolisms in preeclampsia. Finally, microbiomics studies have identified dysbiosis in the gut and vaginal microbiota in pregnant women with preeclampsia. Overall, omics technologies have improved our understanding of the complex molecular mechanisms underlying preeclampsia. However, further research is warranted to fully integrate and translate these omics findings into clinical practice.
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Affiliation(s)
- Lina Youssef
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain; Institut de Recerca August Pi Sunyer (IDIBAPS), Barcelona, Spain; Josep Carreras Leukaemia Research Institute, Hospital Clinic/University of Barcelona Campus, Barcelona, Spain.
| | - Lea Testa
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Francesca Crovetto
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain; Institut de Recerca Sant Joan de Deu (IRSJD), Barcelona, Spain
| | - Fatima Crispi
- BCNatal | Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Déu, IDIBAPS, University of Barcelona, Barcelona, Spain; Institut de Recerca August Pi Sunyer (IDIBAPS), Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
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11
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Golob JL, Oskotsky TT, Tang AS, Roldan A, Chung V, Ha CWY, Wong RJ, Flynn KJ, Parraga-Leo A, Wibrand C, Minot SS, Oskotsky B, Andreoletti G, Kosti I, Bletz J, Nelson A, Gao J, Wei Z, Chen G, Tang ZZ, Novielli P, Romano D, Pantaleo E, Amoroso N, Monaco A, Vacca M, De Angelis M, Bellotti R, Tangaro S, Kuntzleman A, Bigcraft I, Techtmann S, Bae D, Kim E, Jeon J, Joe S, Theis KR, Ng S, Lee YS, Diaz-Gimeno P, Bennett PR, MacIntyre DA, Stolovitzky G, Lynch SV, Albrecht J, Gomez-Lopez N, Romero R, Stevenson DK, Aghaeepour N, Tarca AL, Costello JC, Sirota M. Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research. Cell Rep Med 2024; 5:101350. [PMID: 38134931 PMCID: PMC10829755 DOI: 10.1016/j.xcrm.2023.101350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/15/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023]
Abstract
Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.
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Affiliation(s)
- Jonathan L Golob
- Division of Infectious Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA.
| | - Tomiko T Oskotsky
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
| | - Alice S Tang
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Alennie Roldan
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | | | - Connie W Y Ha
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; March of Dimes Prematurity Research Center at Stanford University, Stanford, CA, USA
| | | | - Antonio Parraga-Leo
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, Obstetrics and Gynaecology, Universidad de Valencia, Valencia, Spain; IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Camilla Wibrand
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Samuel S Minot
- Data Core, Shared Resources, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Boris Oskotsky
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
| | - Gaia Andreoletti
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Idit Kosti
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | | | | | - Jifan Gao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Zhoujingpeng Wei
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Guanhua Chen
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Zheng-Zheng Tang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Pierfrancesco Novielli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Donato Romano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy; Dipartimento Interateneo di Fisica "M, Merlin", Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy; Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy; Dipartimento Interateneo di Fisica "M, Merlin", Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Mirco Vacca
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Maria De Angelis
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy; Dipartimento Interateneo di Fisica "M, Merlin", Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Abigail Kuntzleman
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, USA
| | - Isaac Bigcraft
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, USA
| | - Stephen Techtmann
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, USA
| | - Daehun Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Jongbum Jeon
- Korea Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
| | - Soobok Joe
- Korea Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea
| | - Kevin R Theis
- Department of Biochemistry, Microbiology and Immunology, Wayne State University, Detroit, MI, USA
| | - Sherrianne Ng
- Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Imperial College London, London, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Yun S Lee
- Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Imperial College London, London, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Patricia Diaz-Gimeno
- IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Phillip R Bennett
- Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Imperial College London, London, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - David A MacIntyre
- Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Imperial College London, London, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Gustavo Stolovitzky
- Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA; Thomas J. Watson Research Center, IBM, Yorktown Heights, NY, USA; Sema4, Stamford, CT, USA
| | - Susan V Lynch
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Nardhy Gomez-Lopez
- Department of Biochemistry, Microbiology and Immunology, Wayne State University, Detroit, MI, USA; Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI, USA
| | - Roberto Romero
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI, USA; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA; Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA; Detroit Medical Center, Detroit, MI, USA; Department of Obstetrics and Gynecology, Florida International University, Miami, FL, USA
| | - David K Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Center for Academic Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Adi L Tarca
- Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI, USA; Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA; Department of Computer Science, Wayne State University College of Engineering, Detroit, MI, USA
| | - James C Costello
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Marina Sirota
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
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12
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Hédou J, Marić I, Bellan G, Einhaus J, Gaudillière DK, Ladant FX, Verdonk F, Stelzer IA, Feyaerts D, Tsai AS, Ganio EA, Sabayev M, Gillard J, Amar J, Cambriel A, Oskotsky TT, Roldan A, Golob JL, Sirota M, Bonham TA, Sato M, Diop M, Durand X, Angst MS, Stevenson DK, Aghaeepour N, Montanari A, Gaudillière B. Discovery of sparse, reliable omic biomarkers with Stabl. Nat Biotechnol 2024:10.1038/s41587-023-02033-x. [PMID: 38168992 PMCID: PMC11217152 DOI: 10.1038/s41587-023-02033-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/16/2023] [Indexed: 01/05/2024]
Abstract
Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .
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Affiliation(s)
- Julien Hédou
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Grégoire Bellan
- Télécom Paris, Institut Polytechnique de Paris, Paris, France
| | - Jakob Einhaus
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Dyani K Gaudillière
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | | | - Franck Verdonk
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Intensive Care, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Edward A Ganio
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Maximilian Sabayev
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Joshua Gillard
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jonas Amar
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Amelie Cambriel
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Tomiko T Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Alennie Roldan
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan L Golob
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas A Bonham
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Masaki Sato
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Maïgane Diop
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | - Xavier Durand
- École Polytechnique, Institut Polytechnique de Paris, Paris, France
| | - Martin S Angst
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
| | | | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Andrea Montanari
- Department of Statistics, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
- Department of Pediatrics, Stanford University, Stanford, CA, USA.
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13
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Stevenson DK, Murray JC, Muglia LJ, Wong RJ, Katz M. Challenges to the study of gender dysphoria. Acta Paediatr 2023; 112:2273-2275. [PMID: 37724910 DOI: 10.1111/apa.16975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023]
Affiliation(s)
- David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Jeffrey C Murray
- Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA
| | - Louis J Muglia
- Burroughs Wellcome Fund, Research Triangle Park, Durham, North Carolina, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Michael Katz
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, California, USA
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14
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Jasim ZA, Al-Hakeim HK, Zolghadri S, Stanek A. Maternal Tryptophan Catabolites and Insulin Resistance Parameters in Preeclampsia. Biomolecules 2023; 13:1447. [PMID: 37892130 PMCID: PMC10604911 DOI: 10.3390/biom13101447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/29/2023] Open
Abstract
Preeclampsia (PE) is a pregnancy-related disorder characterized by high blood pressure and proteinuria in the third trimester. The disease is associated with many metabolic and biochemical changes. There is a need for new biomarkers for diagnosis and follow-up. The present study examined the diagnostic ability of tryptophan catabolites (TRYCATs) and insulin resistance (IR) parameters in women with PE. This case-control study recruited sixty women with preeclampsia and 60 healthy pregnant women as a control group. Serum levels of TRYCATs (tryptophan, kynurenic acid, kynurenine, and 3-hydroxykynurenine) and IR parameters (insulin and glucose) were measured by ELISA and spectrophotometric methods. The results showed that PE women have a significantly lower tryptophan level than healthy pregnant women. However, there was a significant increase in kynurenic acid, kynurenic acid/kynurenine, kynurenine/tryptophan, and 3-hydroxykynurenine levels. PE women also have a state of IR. The correlation study indicated various correlations of IR and TRYCATs with clinical data and between each other, reflecting the role of these parameters in the pathophysiology of PE. The ROC study showed that the presence of IR state, reduced tryptophan, and increased 3-HK predicted PE disease in a suspected woman with moderate sensitivities and specificities. In conclusion, the pathophysiology of PE involves a state of IR and an alteration of the TRYCAT system. These changes should be taken into consideration when PE is diagnosed or treated.
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Affiliation(s)
- Zainab Abdulameer Jasim
- Department of Biochemistry, Shiraz Branch, Islamic Azad University, Shiraz 7198774731, Iran;
| | | | - Samaneh Zolghadri
- Department of Biology, Jahrom Branch, Islamic Azad University, Jahrom 7414785318, Iran
| | - Agata Stanek
- Department and Clinic of Internal Medicine, Angiology and Physical Medicine, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Batorego 15 St., 41-902 Bytom, Poland
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15
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Moufarrej MN, Bianchi DW, Shaw GM, Stevenson DK, Quake SR. Noninvasive Prenatal Testing Using Circulating DNA and RNA: Advances, Challenges, and Possibilities. Annu Rev Biomed Data Sci 2023; 6:397-418. [PMID: 37196360 PMCID: PMC10528197 DOI: 10.1146/annurev-biodatasci-020722-094144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Prenatal screening using sequencing of circulating cell-free DNA has transformed obstetric care over the past decade and significantly reduced the number of invasive diagnostic procedures like amniocentesis for genetic disorders. Nonetheless, emergency care remains the only option for complications like preeclampsia and preterm birth, two of the most prevalent obstetrical syndromes. Advances in noninvasive prenatal testing expand the scope of precision medicine in obstetric care. In this review, we discuss advances, challenges, and possibilities toward the goal of providing proactive, personalized prenatal care. The highlighted advances focus mainly on cell-free nucleic acids; however, we also review research that uses signals from metabolomics, proteomics, intact cells, and the microbiome. We discuss ethical challenges in providing care. Finally, we look to future possibilities, including redefining disease taxonomy and moving from biomarker correlation to biological causation.
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Affiliation(s)
| | - Diana W Bianchi
- Eunice Kennedy Shriver National Institute of Child Health and Human Development and Section on Prenatal Genomics and Fetal Therapy, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gary M Shaw
- Department of Pediatrics and March of Dimes Prematurity Research Center at Stanford University, Stanford University School of Medicine, Stanford, California, USA
| | - David K Stevenson
- Department of Pediatrics and March of Dimes Prematurity Research Center at Stanford University, Stanford University School of Medicine, Stanford, California, USA
| | - Stephen R Quake
- Department of Bioengineering and Department of Applied Physics, Stanford University, Stanford, California, USA
- Chan Zuckerberg Initiative, Redwood City, California, USA
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16
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Alkhodari M, Xiong Z, Khandoker AH, Hadjileontiadis LJ, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Rev Cardiovasc Ther 2023; 21:531-543. [PMID: 37300317 DOI: 10.1080/14779072.2023.2223978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
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Affiliation(s)
- Mohanad Alkhodari
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Zhaohan Xiong
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ahsan H Khandoker
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Leontios J Hadjileontiadis
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paul Leeson
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Winok Lapidaire
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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17
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Espinosa CA, Khan W, Khanam R, Das S, Khalid J, Pervin J, Kasaro MP, Contrepois K, Chang AL, Phongpreecha T, Michael B, Ellenberger M, Mehmood U, Hotwani A, Nizar A, Kabir F, Wong RJ, Becker M, Berson E, Culos A, De Francesco D, Mataraso S, Ravindra N, Thuraiappah M, Xenochristou M, Stelzer IA, Marić I, Dutta A, Raqib R, Ahmed S, Rahman S, Hasan ASMT, Ali SM, Juma MH, Rahman M, Aktar S, Deb S, Price JT, Wise PH, Winn VD, Druzin ML, Gibbs RS, Darmstadt GL, Murray JC, Stringer JSA, Gaudilliere B, Snyder MP, Angst MS, Rahman A, Baqui AH, Jehan F, Nisar MI, Vwalika B, Sazawal S, Shaw GM, Stevenson DK, Aghaeepour N. Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries. SCIENCE ADVANCES 2023; 9:eade7692. [PMID: 37224249 PMCID: PMC10208584 DOI: 10.1126/sciadv.ade7692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 04/20/2023] [Indexed: 05/26/2023]
Abstract
Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.
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Affiliation(s)
- Camilo A. Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Waqasuddin Khan
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Rasheda Khanam
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sayan Das
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Javairia Khalid
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Jesmin Pervin
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Margaret P. Kasaro
- University of North Carolina Global Projects Zambia, Lusaka, Zambia
- Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Basil Michael
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Mathew Ellenberger
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Usma Mehmood
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Aneeta Hotwani
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Ambreen Nizar
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Furqan Kabir
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Ronald J. Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloise Berson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Davide De Francesco
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Neal Ravindra
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Melan Thuraiappah
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Ina A. Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Arup Dutta
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Rubhana Raqib
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | | | | | - Said M. Ali
- Public Health Laboratory—Ivo de Carneri, Pemba, Zanzibar, Tanzania
| | - Mohamed H. Juma
- Public Health Laboratory—Ivo de Carneri, Pemba, Zanzibar, Tanzania
| | - Monjur Rahman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Shaki Aktar
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Saikat Deb
- Centre for Public Health Kinetics, New Delhi, Delhi, India
- Public Health Laboratory—Ivo de Carneri, Pemba, Zanzibar, Tanzania
| | - Joan T. Price
- Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
- Department of Obstetrics and Gynaecology, University of Zambia School of Medicine, Lusaka, Zambia
| | - Paul H. Wise
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Virginia D. Winn
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Maurice L. Druzin
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald S. Gibbs
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary L. Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Jeffrey S. A. Stringer
- Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Anisur Rahman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Abdullah H. Baqui
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Fyezah Jehan
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Muhammad Imran Nisar
- Department of Pediatrics and Child Health, Faculty of Health Sciences, Medical College, The Aga Khan University, Karachi, Pakistan
| | - Bellington Vwalika
- Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
- Department of Obstetrics and Gynaecology, University of Zambia School of Medicine, Lusaka, Zambia
| | - Sunil Sazawal
- Centre for Public Health Kinetics, New Delhi, Delhi, India
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
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18
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Golob JL, Oskotsky TT, Tang AS, Roldan A, Chung V, Ha CWY, Wong RJ, Flynn KJ, Parraga-Leo A, Wibrand C, Minot SS, Andreoletti G, Kosti I, Bletz J, Nelson A, Gao J, Wei Z, Chen G, Tang ZZ, Novielli P, Romano D, Pantaleo E, Amoroso N, Monaco A, Vacca M, De Angelis M, Bellotti R, Tangaro S, Kuntzleman A, Bigcraft I, Techtmann S, Bae D, Kim E, Jeon J, Joe S, Theis KR, Ng S, Lee Li YS, Diaz-Gimeno P, Bennett PR, MacIntyre DA, Stolovitzky G, Lynch SV, Albrecht J, Gomez-Lopez N, Romero R, Stevenson DK, Aghaeepour N, Tarca AL, Costello JC, Sirota M. Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.07.23286920. [PMID: 36945505 PMCID: PMC10029035 DOI: 10.1101/2023.03.07.23286920] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.
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Affiliation(s)
- Jonathan L Golob
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
| | - Tomiko T Oskotsky
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | - Alice S Tang
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | - Alennie Roldan
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | | | - Connie W Y Ha
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
| | - Ronald J Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
| | | | - Antonio Parraga-Leo
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | - Camilla Wibrand
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | - Samuel S Minot
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
| | - Gaia Andreoletti
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | - Idit Kosti
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | | | | | - Jifan Gao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Zhoujingpeng Wei
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Guanhua Chen
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Zheng-Zheng Tang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Pierfrancesco Novielli
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Donato Romano
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Ester Pantaleo
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
| | - Nicola Amoroso
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
| | - Alfonso Monaco
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Mirco Vacca
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Maria De Angelis
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Roberto Bellotti
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Sabina Tangaro
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Abigail Kuntzleman
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
| | - Isaac Bigcraft
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Stephen Techtmann
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Daehun Bae
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
| | - Eunyoung Kim
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | | | - Soobok Joe
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Kevin R Theis
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
| | - Sherrianne Ng
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
| | - Yun S Lee Li
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Patricia Diaz-Gimeno
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Phillip R Bennett
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - David A MacIntyre
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Gustavo Stolovitzky
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Susan V Lynch
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
| | | | - Nardhy Gomez-Lopez
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Roberto Romero
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - David K Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
| | - Nima Aghaeepour
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
| | - Adi L Tarca
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - James C Costello
- Division of Infectious Disease. Department of Internal Medicine. University of Michigan. Ann Arbor, MI. USA
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
- Sage Bionetworks, Seattle, WA. USA
- Benioff Center for Microbiome Medicine, Department of Medicine, University of California, San Francisco, CA. USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. USA
- March of Dimes Prematurity Research Center at Stanford University, Stanford, CA USA
- Data Core, Shared Resources, Fred Hutchinson Cancer Center. Seattle, WA. USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI. USA
| | - Marina Sirota
- March of Dimes Prematurity Research Center at the University of California San Francisco, San Francisco, CA USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA. USA
- Department of Pediatrics. University of California San Francisco, San Francisco, CA. USA
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19
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Becker M, Nassar H, Espinosa C, Stelzer IA, Feyaerts D, Berson E, Bidoki NH, Chang AL, Saarunya G, Culos A, De Francesco D, Fallahzadeh R, Liu Q, Kim Y, Marić I, Mataraso SJ, Payrovnaziri SN, Phongpreecha T, Ravindra NG, Stanley N, Shome S, Tan Y, Thuraiappah M, Xenochristou M, Xue L, Shaw G, Stevenson D, Angst MS, Gaudilliere B, Aghaeepour N. Large-scale correlation network construction for unraveling the coordination of complex biological systems. NATURE COMPUTATIONAL SCIENCE 2023; 3:346-359. [PMID: 38116462 PMCID: PMC10727505 DOI: 10.1038/s43588-023-00429-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 03/10/2023] [Indexed: 12/21/2023]
Abstract
Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.
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Affiliation(s)
- Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany
- These authors contributed equally: Martin Becker, Huda Nassar, Camilo Espinosa
| | - Huda Nassar
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
- These authors contributed equally: Martin Becker, Huda Nassar, Camilo Espinosa
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
- These authors contributed equally: Martin Becker, Huda Nassar, Camilo Espinosa
| | - Ina A. Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Eloise Berson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Neda H. Bidoki
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Geetha Saarunya
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Davide De Francesco
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Qun Liu
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Yeasul Kim
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Samson J. Mataraso
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Seyedeh Neelufar Payrovnaziri
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Neal G. Ravindra
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sayane Shome
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Yuqi Tan
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Melan Thuraiappah
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Lei Xue
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Gary Shaw
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
| | - David Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA
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20
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Bacharaki D, Petrakis I, Stylianou K. Redefying the therapeutic strategies against cardiorenal morbidity and mortality: Patient phenotypes. World J Cardiol 2023; 15:76-83. [PMID: 37033683 PMCID: PMC10074996 DOI: 10.4330/wjc.v15.i3.76] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/31/2022] [Accepted: 02/22/2023] [Indexed: 03/21/2023] Open
Abstract
Chronic kidney disease (CKD) patients face an unacceptably high morbidity and mortality, mainly from cardiovascular diseases. Diabetes mellitus, arterial hypertension and dyslipidemia are highly prevalent in CKD patients. Established therapeutic protocols for the treatment of diabetes mellitus, arterial hypertension, and dyslipidemia are not as effective in CKD patients as in the general population. The role of non-traditional risk factors (RF) has gained interest in the last decades. These entail the deranged clinical spectrum of secondary hyperparathyroidism involving vascular and valvular calcification, under the term “CKD-mineral and bone disorder” (CKD-MBD), uremia per se, inflammation and oxidative stress. Each one of these non-traditional RF have been addressed in various study designs, but the results do not exhibit any applied clinical benefit for CKD-patients. The “crusade” against cardiorenal morbidity and mortality in CKD-patients is in some instances, derailed. We propose a therapeutic paradigm advancing from isolated treatment targets, as practiced today, to precision medicine involving patient phenotypes with distinct underlying pathophysiology. In this regard we propose two steps, based on current stratification management of corona virus disease-19 and sepsis. First, select patients who are expected to have a high mortality, i.e., a prognostic enrichment. Second, select patients who are likely to respond to a specific therapy, i.e., a predictive enrichment.
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Affiliation(s)
- Dimitra Bacharaki
- Nephrology Unit, 2nd Department of Internal Medicine, Attikon University Hospital, Chaidari 12462, Greece
| | - Ioannis Petrakis
- Department of Nephrology, Heraklion University Hospital, University of Crete, Heraklion 71500, Greece
| | - Kostas Stylianou
- Department of Nephrology, Heraklion University Hospital, University of Crete, Heraklion 71500, Greece
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21
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Hédou J, Marić I, Bellan G, Einhaus J, Gaudillière DK, Ladant FX, Verdonk F, Stelzer IA, Feyaerts D, Tsai AS, Ganio EA, Sabayev M, Gillard J, Bonham TA, Sato M, Diop M, Angst MS, Stevenson D, Aghaeepour N, Montanari A, Gaudillière B. Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data. RESEARCH SQUARE 2023:rs.3.rs-2609859. [PMID: 36909508 PMCID: PMC10002850 DOI: 10.21203/rs.3.rs-2609859/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.
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Affiliation(s)
- Julien Hédou
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Ivana Marić
- Department of Pediatrics, Stanford University, Stanford, CA
| | | | - Jakob Einhaus
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Dyani K. Gaudillière
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA
| | | | - Franck Verdonk
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
- Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Intensive Care, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris; Paris, France
| | - Ina A. Stelzer
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Amy S. Tsai
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Edward A. Ganio
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Maximilian Sabayev
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Joshua Gillard
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Thomas A. Bonham
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Masaki Sato
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Maïgane Diop
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
| | | | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
- Department of Pediatrics, Stanford University, Stanford, CA
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | - Andrea Montanari
- Department of Statistics, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA
- Department of Pediatrics, Stanford University, Stanford, CA
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22
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Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol (Lausanne) 2023; 14:1081667. [PMID: 36909346 PMCID: PMC9996332 DOI: 10.3389/fendo.2023.1081667] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.
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Affiliation(s)
- Simmi Kharb
- Department of Biochemistry, Postgraduate Institute of Medical Sciences, Rohtak, Haryana, India
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
| | - Anagha Joshi
- Computational Biology Unit (CBU), Department of Clinical Science, University of Bergen, Bergen, Norway
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
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