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Article: A data-driven approach to improve wellness and reduce recurrence in cancer survivors.

Hariharan, Ramkumar / Hood, Leroy / Price, Nathan D

2024 Volume 14, Page(s) 1397008

Abstract: For many cancer survivors, toxic side effects of treatment, lingering effects of the aftermath of disease and cancer recurrence adversely affect quality of life (QoL) and reduce healthspan. Data-driven approaches for quantifying and improving wellness in ...

Abstract	For many cancer survivors, toxic side effects of treatment, lingering effects of the aftermath of disease and cancer recurrence adversely affect quality of life (QoL) and reduce healthspan. Data-driven approaches for quantifying and improving wellness in healthy individuals hold great promise for improving the lives of cancer survivors. The data-driven strategy will also guide personalized nutrition and exercise recommendations that may help prevent cancer recurrence and secondary malignancies in survivors.
Language	English
Publishing date	2024-04-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2024.1397008
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Article ; Online: Healthy aging and the human gut microbiome: why we cannot just turn back the clock.

Wilmanski, Tomasz / Gibbons, Sean M / Price, Nathan D

Nature aging

2022 Volume 2, Issue 10, Page(s) 869–871

MeSH term(s)	Humans ; Gastrointestinal Microbiome ; Healthy Aging ; Microbiota
Language	English
Publishing date	2022-10-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2662-8465
ISSN (online)	2662-8465
DOI	10.1038/s43587-022-00294-w
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Article ; Online: Personal Dense Dynamic Data Clouds Connect Systems Biomedicine to Scientific Wellness.

Omenn, Gilbert S / Magis, Andrew T / Price, Nathan D / Hood, Leroy

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2486, Page(s) 315–334

Abstract: The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, ... ...

Abstract	The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, dynamic, integrative, cross-disciplinary approach to biological complexity that embraces experimentation, technology, computation, and clinical translation. Systems Medicine integrates genome analyses and longitudinal deep phenotyping with biological pathways and networks to understand mechanisms of disease, identify relevant blood biomarkers, define druggable molecular targets, and enhance the maintenance or restoration of wellness. Two programs initiated our understanding of data-driven population-based wellness. The Pioneer 100 Study of Scientific Wellness and the much larger Arivale commercial program that followed had two spectacular results: demonstrating the feasibility and utility of collecting longitudinal multiomic data, and then generating dense, dynamic data clouds for each individual to utilize actionable metrics for promoting health and preventing disease when combined with personalized coaching. Future developments in these domains will enable better population health and personal, preventive, predictive, participatory (P4) health care.
MeSH term(s)	Artificial Intelligence ; Computational Biology ; Genomics ; Proteomics ; Systems Biology
Language	English
Publishing date	2022-04-18
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2265-0_15
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Article ; Online: The impact of maternal SARS-CoV-2 infection and COVID-19 vaccination on maternal-fetal outcomes.

Piekos, Samantha N / Price, Nathan D / Hood, Leroy / Hadlock, Jennifer J

Reproductive toxicology (Elmsford, N.Y.)

2022 Volume 114, Page(s) 33–43

Abstract: The rapidly evolving COVID-19 pandemic has resulted in an upsurge of scientific productivity to help address the global health crisis. One area of active research is the impact of COVID-19 on pregnancy. Here, we provide an epidemiological overview about ... ...

Abstract	The rapidly evolving COVID-19 pandemic has resulted in an upsurge of scientific productivity to help address the global health crisis. One area of active research is the impact of COVID-19 on pregnancy. Here, we provide an epidemiological overview about what is known about the effects of maternal SARS-CoV-2 infection and COVID-19 vaccination on maternal-fetal outcomes, and identify gaps in knowledge. Pregnant people are at increased risk for severe COVID-19, and maternal SARS-CoV-2 infection increases the risk of negative maternal-fetal outcomes. Despite this elevated risk, there have been high rates of vaccine hesitancy, heightened by the initial lack of safety and efficacy data for COVID-19 vaccination in pregnancy. In response, retrospective cohort studies were performed to examine the impact of COVID-19 vaccination during pregnancy. Here, we report the vaccine's efficacy during pregnancy and its impact on maternal-fetal outcomes, as well as an overview of initial studies on booster shots in pregnancy. We found that pregnant people are at risk for more severe COVID-19 outcomes, maternal SARS-CoV-2 infection is associated with worse birth outcomes, COVID-19 vaccine hesitancy remains prevalent in the pregnant population, and COVID-19 vaccination and boosters promote better maternal-fetal outcomes. The results should help reduce vaccine hesitancy by alleviating concerns about the safety and efficacy of administering the COVID-19 vaccine during pregnancy. Overall, this review provides an introduction to COVID-19 during pregnancy. It is expected to help consolidate current knowledge, accelerate research of COVID-19 during pregnancy and inform clinical, policy, and research decisions regarding COVID-19 vaccination in pregnant people.
MeSH term(s)	Female ; Humans ; Pregnancy ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19 Vaccines/therapeutic use ; Pandemics ; Retrospective Studies ; SARS-CoV-2 ; Vaccination ; Vaccination Hesitancy ; Pregnancy Outcome ; Vaccine Efficacy ; Immunization, Secondary ; Risk
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2022-10-22
Publishing country	United States
Document type	Review ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	639342-1
ISSN	1873-1708 ; 0890-6238
ISSN (online)	1873-1708
ISSN	0890-6238
DOI	10.1016/j.reprotox.2022.10.003
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Article ; Online: Editorial overview: Systems biology: Data, discovery, delivery.

Price, Nathan D / Segal, Eran

Current opinion in biotechnology

2018 Volume 51, Page(s) vii–viii

Language	English
Publishing date	2018-06
Publishing country	England
Document type	Editorial
ZDB-ID	1052045-4
ISSN	1879-0429 ; 0958-1669
ISSN (online)	1879-0429
ISSN	0958-1669
DOI	10.1016/j.copbio.2018.05.002
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Zs.A 3071: Show issues

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Article ; Online: iREAD: a tool for intron retention detection from RNA-seq data.

Li, Hong-Dong / Funk, Cory C / Price, Nathan D

BMC genomics

2020 Volume 21, Issue 1, Page(s) 128

Abstract: Background: Intron retention (IR) has been traditionally overlooked as 'noise' and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has ... ...

Abstract	Background: Intron retention (IR) has been traditionally overlooked as 'noise' and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection. Results: Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread. Conclusion: Being complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome.
MeSH term(s)	Algorithms ; Animals ; Humans ; Introns ; Mice ; RNA-Seq ; Software
Language	English
Publishing date	2020-02-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2041499-7
ISSN	1471-2164 ; 1471-2164
ISSN (online)	1471-2164
ISSN	1471-2164
DOI	10.1186/s12864-020-6541-0
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Article ; Online: Systems modeling of metabolic dysregulation in neurodegenerative diseases.

Baloni, Priyanka / Funk, Cory C / Readhead, Ben / Price, Nathan D

Current opinion in pharmacology

2021 Volume 60, Page(s) 59–65

Abstract: Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's ... ...

Abstract	Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's diseases negatively impact both length and quality of life, due to lack of effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches, and integration with multiomics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multiomics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and predisease states.
MeSH term(s)	Brain ; Humans ; Huntington Disease ; Neurodegenerative Diseases ; Quality of Life ; Systems Biology
Language	English
Publishing date	2021-08-02
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	2037057-X
ISSN	1471-4973 ; 1471-4892
ISSN (online)	1471-4973
ISSN	1471-4892
DOI	10.1016/j.coph.2021.06.012
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Zs.A 5608: Show issues

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Article ; Online: Effect of COVID-19 vaccination and booster on maternal-fetal outcomes: a retrospective cohort study.

Piekos, Samantha N / Hwang, Yeon Mi / Roper, Ryan T / Sorensen, Tanya / Price, Nathan D / Hood, Leroy / Hadlock, Jennifer J

The Lancet. Digital health

2023 Volume 5, Issue 9, Page(s) e594–e606

Abstract: Background: COVID-19 in pregnant people increases the risk for poor maternal-fetal outcomes. However, COVID-19 vaccination hesitancy remains due to concerns over the vaccine's potential effects on maternal-fetal outcomes. Here we examine the impact of ... ...

Abstract	Background: COVID-19 in pregnant people increases the risk for poor maternal-fetal outcomes. However, COVID-19 vaccination hesitancy remains due to concerns over the vaccine's potential effects on maternal-fetal outcomes. Here we examine the impact of COVID-19 vaccination and boosters on maternal SARS-CoV-2 infections and birth outcomes. Methods: This was a retrospective multicentre cohort study on the impact of COVID-19 vaccination on maternal-fetal outcomes for people who delivered (n=106 428) at Providence St Joseph Health across seven western US states from Jan 26, 2021 to Oct 26, 2022. Cohorts were defined by vaccination status at delivery: vaccinated (n=35 926; two or more doses of mRNA-1273 Moderna or BNT162b2 Pfizer-BioNTech), unvaccinated (n=55 878), unvaccinated propensity score matched (n=16 771), boosted (n=10 927; three or more doses), vaccinated unboosted (n=13 243; two doses only), and vaccinated unboosted with propensity score matching (n=4414). We built supervised machine learning classification models, which we used to determine which people were more likely to be vaccinated or boosted at delivery. The primary outcome was maternal SARS-CoV-2 infection. COVID-19 vaccination status at delivery, COVID-19-related health care, preterm birth, stillbirth, and very low birthweight were evaluated as secondary outcomes. Findings: Vaccinated people were more likely to conceive later in the pandemic, have commercial insurance, be older, live in areas with lower household composition vulnerability, and have a higher BMI than unvaccinated people. Boosted people were more likely to have more days since receiving the second COVID-19 vaccine dose, conceive earlier in the pandemic, have commercial insurance, be older, and live in areas with lower household composition vulnerability than vaccinated unboosted people. Vaccinated pregnant people had lower rates of COVID-19 during pregnancy (4·0%) compared with unvaccinated matched people (5·3%; p<0·0001). COVID-19 rates were even lower in boosted people (3·2%) compared with vaccinated unboosted matched people (5·6%; p<0·0001). Vaccinated people were also less likely to have a preterm birth (7·9%; p<0·0001), stillbirth (0·3%; p<0·0002), or very low birthweight neonate (1·0%; p<0·0001) compared with unvaccinated matched people (preterm birth 9·4%; stillbirth 0·6%; very low birthweight 1·5%). Boosted people were less likely to have a stillbirth (0·3%; p<0·025) and have no differences in rates of preterm birth (7·6%; p=0·090) or very low birthweight neonates (0·8%; p=0·092) compared with vaccinated unboosted matched people (stillbirth 0·5%; preterm birth 8·4%; very low birthweight 1·1%). Interpretation: COVID-19 vaccination protects against adverse maternal-fetal outcomes, with booster doses conferring additional protection. Pregnant people should be high priority for vaccination and stay up to date with their COVID-19 vaccination schedule. Funding: National Institute for Child Health & Human Development and the William O and K Carole Ellison Foundation.
MeSH term(s)	Infant, Newborn ; Child ; Female ; Pregnancy ; Humans ; BNT162 Vaccine ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19 Vaccines ; Cohort Studies ; Premature Birth/epidemiology ; Retrospective Studies ; SARS-CoV-2 ; Stillbirth/epidemiology
Chemical Substances	BNT162 Vaccine ; COVID-19 Vaccines
Language	English
Publishing date	2023-08-01
Publishing country	England
Document type	Multicenter Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2589-7500
ISSN (online)	2589-7500
DOI	10.1016/S2589-7500(23)00093-6
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Article ; Online: The transition from genomics to phenomics in personalized population health.

Yurkovich, James T / Evans, Simon J / Rappaport, Noa / Boore, Jeffrey L / Lovejoy, Jennifer C / Price, Nathan D / Hood, Leroy E

Nature reviews. Genetics

2023 Volume 25, Issue 4, Page(s) 286–302

Abstract: Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data- ... ...

Abstract	Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
MeSH term(s)	Phenomics ; Genomics
Language	English
Publishing date	2023-12-13
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2035157-4
ISSN	1471-0064 ; 1471-0056
ISSN (online)	1471-0064
ISSN	1471-0056
DOI	10.1038/s41576-023-00674-x
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Zs.A 5474: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Timing of selective serotonin reuptake inhibitor use and risk for preterm birth and related adverse events: with a consideration of the COVID-19 pandemic period.

Hwang, Yeon Mi / Roper, Ryan T / Piekos, Samantha N / Enquobahrie, Daniel A / Hebert, Mary F / Paquette, Alison G / Baloni, Priyanka / Price, Nathan D / Hood, Leroy / Hadlock, Jennifer J

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians

2024 Volume 37, Issue 1, Page(s) 2313364

Abstract: Objective: There is uncertainty around the safety of SSRIs for treating depression during pregnancy. Nevertheless, the use of SSRIs has been gradually increasing, especially during the COVID-19 pandemic period. We aimed to (1) characterize maternal ... ...

Abstract	Objective: There is uncertainty around the safety of SSRIs for treating depression during pregnancy. Nevertheless, the use of SSRIs has been gradually increasing, especially during the COVID-19 pandemic period. We aimed to (1) characterize maternal depression rate and use of SSRIs in a recent 10-year period, (2) address confounding by indication, as well as socioeconomic and environmental factors, and (3) evaluate associations of the timing of SSRI exposure in pregnancy with risk for preterm birth (PTB), low birthweight (LBW), and small for gestational age (SGA) infants among women with depression before pregnancy. Methods: We conducted propensity score-adjusted regression to calculate odds ratios (ORs) of PTB, LBW, and SGA. We accounted for maternal/pregnancy characteristics, comorbidity, depression severity, time of delivery, social vulnerability, and rural residence. Results: There were 50.3% and 40.3% increases in the prevalence rate of prenatal depression and prenatal SSRI prescription rate during the pandemic. We identified women with depression ≤180 days before pregnancy ( Conclusions: These findings suggest an association between PTB/LBW and SSRI exposure is dependent on exposure timing during pregnancy. Small for gestational age is not associated with SSRI exposure.
MeSH term(s)	Pregnancy ; Infant ; Infant, Newborn ; Humans ; Female ; Selective Serotonin Reuptake Inhibitors/adverse effects ; Premature Birth/epidemiology ; Premature Birth/etiology ; Pandemics ; Pregnancy Complications/epidemiology ; COVID-19/epidemiology ; Fetal Growth Retardation/epidemiology ; Infant, Newborn, Diseases/epidemiology
Chemical Substances	Selective Serotonin Reuptake Inhibitors
Language	English
Publishing date	2024-02-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2077261-0
ISSN	1476-4954 ; 1057-0802 ; 1476-7058
ISSN (online)	1476-4954
ISSN	1057-0802 ; 1476-7058
DOI	10.1080/14767058.2024.2313364
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