Whole Blood Gene Expression Associated With Clinical Biological Age

Honghuang Lin; Kathryn L Lunetta; Qiang Zhao; Pooja R Mandaviya; Jian Rong; Emelia J Benjamin; Roby Joehanes; Daniel Levy; Joyce B J van Meurs; Martin G Larson; Joanne M Murabito

doi:10.1093/gerona/gly164

Whole Blood Gene Expression Associated With Clinical Biological Age

Honghuang Lin^*, Kathryn L Lunetta, Qiang Zhao, Pooja R Mandaviya, Jian Rong, Emelia J Benjamin, Roby Joehanes, Daniel Levy, Joyce B J van Meurs, Martin G Larson, Joanne M Murabito

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Background: Biologic age may better reflect an individual's rate of aging than chronologic age.

Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array.

Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05).

Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.

Original language	English
Pages (from-to)	81-88
Number of pages	8
Journal	Journals of Gerontology Series A-Biological Sciences and Medical Sciences
Volume	74
Issue number	1
DOIs	https://doi.org/10.1093/gerona/gly164
Publication status	Published - Jan 2019

Keywords

ARRAY
Biologic aging
Epidemiology
Gene expression
HAPLOTYPES
KINASE
MORTALITY
PRESSURE
WIDE ASSOCIATION

Access to Document

10.1093/gerona/gly164Licence: CC BY

Cite this

@article{b58f9fc086de488888a25ec53b1c39e6,

title = "Whole Blood Gene Expression Associated With Clinical Biological Age",

abstract = "Background: Biologic age may better reflect an individual's rate of aging than chronologic age.Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array.Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05).Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.",

keywords = "ARRAY, Biologic aging, Epidemiology, Gene expression, HAPLOTYPES, KINASE, MORTALITY, PRESSURE, WIDE ASSOCIATION",

author = "Honghuang Lin and Lunetta, {Kathryn L} and Qiang Zhao and Mandaviya, {Pooja R} and Jian Rong and Benjamin, {Emelia J} and Roby Joehanes and Daniel Levy and {van Meurs}, {Joyce B J} and Larson, {Martin G} and Murabito, {Joanne M}",

note = "Funding Information: This work was supported by the National Institutes of Health grants R56AG029451 (J.M.M.), 3U24AG051129 (J.M.M.), 5R01HL092577 (E.J.B.), and 5R01HL128914 (E.J.B.). Measurement of clinical biomarkers was funded through R01 HL64753 (E.J.B.) and R01 HL076784 (E.J.B.) and further supported by R01AG028321 (E.J.B.). This project was also supported by Boston University Digital Health Initiative, and the National Center for Advancing Translational Sciences, National Institutes of Health, through BU-CTSI Grant Number 1UL1TR001430. Framingham gene expression profiling was funded through the Division of Intramural Research (D.L.), National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD. The Framingham Heart Study is supported by the National Heart, Lung and Blood Institute{\textquoteright}s Framingham Heart Study (Contracts No. N01-HC-25195 and HHSN268201500001I). Funding Information: The generation and management of the Illumina 450K methylation array data (EWAS data) for the Rotterdam Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. The EWAS data was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by the Netherlands Organization for Scientific Research (NWO; project number 184021007) and made available as a Rainbow Project (RP3; BIOS) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This Rotterdam work was done within the framework of the Biobank-Based Integrative Omics Studies (BIOS) Consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007). Publisher Copyright: {\textcopyright} 2018 The Author(s). Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.",

year = "2019",

month = jan,

doi = "10.1093/gerona/gly164",

language = "English",

volume = "74",

pages = "81--88",

journal = "Journals of Gerontology Series A-Biological Sciences and Medical Sciences",

issn = "1079-5006",

publisher = "Oxford University Press",

number = "1",

}

TY - JOUR

T1 - Whole Blood Gene Expression Associated With Clinical Biological Age

AU - Lin, Honghuang

AU - Lunetta, Kathryn L

AU - Zhao, Qiang

AU - Mandaviya, Pooja R

AU - Rong, Jian

AU - Benjamin, Emelia J

AU - Joehanes, Roby

AU - Levy, Daniel

AU - van Meurs, Joyce B J

AU - Larson, Martin G

AU - Murabito, Joanne M

N1 - Funding Information: This work was supported by the National Institutes of Health grants R56AG029451 (J.M.M.), 3U24AG051129 (J.M.M.), 5R01HL092577 (E.J.B.), and 5R01HL128914 (E.J.B.). Measurement of clinical biomarkers was funded through R01 HL64753 (E.J.B.) and R01 HL076784 (E.J.B.) and further supported by R01AG028321 (E.J.B.). This project was also supported by Boston University Digital Health Initiative, and the National Center for Advancing Translational Sciences, National Institutes of Health, through BU-CTSI Grant Number 1UL1TR001430. Framingham gene expression profiling was funded through the Division of Intramural Research (D.L.), National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD. The Framingham Heart Study is supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (Contracts No. N01-HC-25195 and HHSN268201500001I). Funding Information: The generation and management of the Illumina 450K methylation array data (EWAS data) for the Rotterdam Study was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Netherlands. The EWAS data was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, and by the Netherlands Organization for Scientific Research (NWO; project number 184021007) and made available as a Rainbow Project (RP3; BIOS) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL). The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This Rotterdam work was done within the framework of the Biobank-Based Integrative Omics Studies (BIOS) Consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007). Publisher Copyright: © 2018 The Author(s). Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.

PY - 2019/1

Y1 - 2019/1

N2 - Background: Biologic age may better reflect an individual's rate of aging than chronologic age.Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array.Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05).Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.

AB - Background: Biologic age may better reflect an individual's rate of aging than chronologic age.Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array.Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05).Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.

KW - ARRAY

KW - Biologic aging

KW - Epidemiology

KW - Gene expression

KW - HAPLOTYPES

KW - KINASE

KW - MORTALITY

KW - PRESSURE

KW - WIDE ASSOCIATION

U2 - 10.1093/gerona/gly164

DO - 10.1093/gerona/gly164

M3 - Article

C2 - 30010802

SN - 1079-5006

VL - 74

SP - 81

EP - 88

JO - Journals of Gerontology Series A-Biological Sciences and Medical Sciences

JF - Journals of Gerontology Series A-Biological Sciences and Medical Sciences

IS - 1

ER -