Personal aging markers and ageotypes revealed by deep longitudinal profiling
The molecular changes that occur with aging are not well understood 1 – 4 . Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytok...
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| Published in | Nature medicine Vol. 26; no. 1; pp. 83 - 90 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.01.2020
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1078-8956 1546-170X 1546-170X |
| DOI | 10.1038/s41591-019-0719-5 |
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| Abstract | The molecular changes that occur with aging are not well understood
1
–
4
. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.
Longitudinal multiomics profiling of a cohort of healthy people reveals distinct aging patterns—termed ageotypes—in different individuals. |
|---|---|
| AbstractList | The molecular changes that occur with aging are not well understood
1
–
4
. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. The molecular changes that occur with aging are not well understood . Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. The molecular changes that occur with aging are not well understood1–4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.Longitudinal multiomics profiling of a cohort of healthy people reveals distinct aging patterns—termed ageotypes—in different individuals. The molecular changes that occur with aging are not well understood.sup.1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. The molecular changes that occur with aging are not well understood1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.The molecular changes that occur with aging are not well understood1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. The molecular changes that occur with aging are not well understood.sup.1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of 'omic' measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2-3 years. Further, we defined different types of aging patterns in different individuals, termed 'ageotypes', on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. Longitudinal multiomics profiling of a cohort of healthy people reveals distinct aging patterns--termed ageotypes--in different individuals. The molecular changes that occur with aging are not well understood 1 – 4 . Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process. Longitudinal multiomics profiling of a cohort of healthy people reveals distinct aging patterns—termed ageotypes—in different individuals. |
| Audience | Academic |
| Author | Schüssler-Fiorenza Rose, Sophia Miryam Ahadi, Sara Contrepois, Kévin Ashland, Melanie Snyder, Michael Sailani, M. Reza Zhou, Wenyu Brunet, Anne Avina, Monika |
| AuthorAffiliation | 1 Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA 2 These authors contributed equally: Sara Ahadi, Wenyu Zhou |
| AuthorAffiliation_xml | – name: 2 These authors contributed equally: Sara Ahadi, Wenyu Zhou – name: 1 Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA |
| Author_xml | – sequence: 1 givenname: Sara surname: Ahadi fullname: Ahadi, Sara organization: Department of Genetics, Stanford University School of Medicine – sequence: 2 givenname: Wenyu surname: Zhou fullname: Zhou, Wenyu organization: Department of Genetics, Stanford University School of Medicine – sequence: 3 givenname: Sophia Miryam orcidid: 0000-0002-6311-6671 surname: Schüssler-Fiorenza Rose fullname: Schüssler-Fiorenza Rose, Sophia Miryam organization: Department of Genetics, Stanford University School of Medicine – sequence: 4 givenname: M. Reza surname: Sailani fullname: Sailani, M. Reza organization: Department of Genetics, Stanford University School of Medicine – sequence: 5 givenname: Kévin orcidid: 0000-0001-9678-5161 surname: Contrepois fullname: Contrepois, Kévin organization: Department of Genetics, Stanford University School of Medicine – sequence: 6 givenname: Monika surname: Avina fullname: Avina, Monika organization: Department of Genetics, Stanford University School of Medicine – sequence: 7 givenname: Melanie surname: Ashland fullname: Ashland, Melanie organization: Department of Genetics, Stanford University School of Medicine – sequence: 8 givenname: Anne orcidid: 0000-0002-4608-6845 surname: Brunet fullname: Brunet, Anne organization: Department of Genetics, Stanford University School of Medicine – sequence: 9 givenname: Michael orcidid: 0000-0003-0784-7987 surname: Snyder fullname: Snyder, Michael email: mpsnyder@stanford.edu organization: Department of Genetics, Stanford University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31932806$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2020 COPYRIGHT 2020 Nature Publishing Group 2020© The Author(s), under exclusive licence to Springer Nature America, Inc. 2020 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 M. Ashland and W.Z. managed the cohort and coordinated clinical visits. A.B. and M.S. obtained funding and provided additional study resources. S.A., W.Z., A.B., S.M.S.-F.R. and M.S. wrote and revised the manuscript. M.S. supervised the overall study. S.A., W.Z., M.R.S., K.C. and M. Avina performed experimental bench work and collected data. W.Z. and S.A. analyzed the data and generated results, with contributions from S.M.S.-F.R. M. Avina managed and coordinated the biobank sample inventory. Author contributions |
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| Snippet | The molecular changes that occur with aging are not well understood
1
–
4
. Here, we performed longitudinal and deep multiomics profiling of 106 healthy... The molecular changes that occur with aging are not well understood . Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals... The molecular changes that occur with aging are not well understood.sup.1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy... The molecular changes that occur with aging are not well understood1–4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy... The molecular changes that occur with aging are not well understood1-4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy... |
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| SubjectTerms | 692/53/2422 692/700/459/284 Adult Age Aged Aging Aging - physiology Biological markers Biomarkers - metabolism Biomedical and Life Sciences Biomedicine Cancer Research Cohort Studies Cytokines Depth profiling Female Genetic aspects Genomics Humans Identification and classification Infectious Diseases Insulin Letter Longitudinal Studies Male Markers Metabolic Diseases Metabolites Middle Aged Molecular Medicine Neurosciences Physiological aspects Statistics, Nonparametric Young Adult |
| Title | Personal aging markers and ageotypes revealed by deep longitudinal profiling |
| URI | https://link.springer.com/article/10.1038/s41591-019-0719-5 https://www.ncbi.nlm.nih.gov/pubmed/31932806 https://www.proquest.com/docview/2342391530 https://www.proquest.com/docview/2338106069 https://pubmed.ncbi.nlm.nih.gov/PMC7301912 |
| Volume | 26 |
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