Epigenetic clock analysis of diet, exercise, education, and lifestyle factors
Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. Toward this end, we use recent epigenetic biomarkers of age that have previously been shown to predict all-caus...
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Veröffentlicht in: | Aging (Albany, NY.) NY.), 2017-02, Vol.9 (2), p.419-446 |
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creator | Quach, Austin Levine, Morgan E Tanaka, Toshiko Lu, Ake T Chen, Brian H Ferrucci, Luigi Ritz, Beate Bandinelli, Stefania Neuhouser, Marian L Beasley, Jeannette M Snetselaar, Linda Wallace, Robert B Tsao, Philip S Absher, Devin Assimes, Themistocles L Stewart, James D Li, Yun Hou, Lifang Baccarelli, Andrea A Whitsel, Eric A Horvath, Steve |
description | Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. Toward this end, we use recent epigenetic biomarkers of age that have previously been shown to predict all-cause mortality, chronic conditions, and age-related functional decline. We analyze cross-sectional data from 4,173 postmenopausal female participants from the Women's Health Initiative, as well as 402 male and female participants from the Italian cohort study, Invecchiare nel Chianti.Extrinsic epigenetic age acceleration (EEAA) exhibits significant associations with fish intake (p=0.02), moderate alcohol consumption (p=0.01), education (p=3x10
), BMI (p=0.01), and blood carotenoid levels (p=1x10
)-an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin-the first-line medication for the treatment of type 2 diabetes-does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome. |
doi_str_mv | 10.18632/aging.101168 |
format | Article |
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), BMI (p=0.01), and blood carotenoid levels (p=1x10
)-an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin-the first-line medication for the treatment of type 2 diabetes-does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome.</description><identifier>ISSN: 1945-4589</identifier><identifier>EISSN: 1945-4589</identifier><identifier>DOI: 10.18632/aging.101168</identifier><identifier>PMID: 28198702</identifier><language>eng</language><publisher>United States: Impact Journals LLC</publisher><subject>Aged ; Aged, 80 and over ; Aging - genetics ; Aging - metabolism ; Cohort Studies ; Cross-Sectional Studies ; Diet ; Educational Status ; Epigenesis, Genetic ; Exercise ; Female ; Humans ; Life Style ; Middle Aged ; Research Paper</subject><ispartof>Aging (Albany, NY.), 2017-02, Vol.9 (2), p.419-446</ispartof><rights>Copyright: © 2017 Quach et al. 2017</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c497t-dd9e3034b3f743b57865695fee2ea82363736b3b8f95143862cd29662b8875243</citedby><cites>FETCH-LOGICAL-c497t-dd9e3034b3f743b57865695fee2ea82363736b3b8f95143862cd29662b8875243</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361673/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361673/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28198702$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Quach, Austin</creatorcontrib><creatorcontrib>Levine, Morgan E</creatorcontrib><creatorcontrib>Tanaka, Toshiko</creatorcontrib><creatorcontrib>Lu, Ake T</creatorcontrib><creatorcontrib>Chen, Brian H</creatorcontrib><creatorcontrib>Ferrucci, Luigi</creatorcontrib><creatorcontrib>Ritz, Beate</creatorcontrib><creatorcontrib>Bandinelli, Stefania</creatorcontrib><creatorcontrib>Neuhouser, Marian L</creatorcontrib><creatorcontrib>Beasley, Jeannette M</creatorcontrib><creatorcontrib>Snetselaar, Linda</creatorcontrib><creatorcontrib>Wallace, Robert B</creatorcontrib><creatorcontrib>Tsao, Philip S</creatorcontrib><creatorcontrib>Absher, Devin</creatorcontrib><creatorcontrib>Assimes, Themistocles L</creatorcontrib><creatorcontrib>Stewart, James D</creatorcontrib><creatorcontrib>Li, Yun</creatorcontrib><creatorcontrib>Hou, Lifang</creatorcontrib><creatorcontrib>Baccarelli, Andrea A</creatorcontrib><creatorcontrib>Whitsel, Eric A</creatorcontrib><creatorcontrib>Horvath, Steve</creatorcontrib><title>Epigenetic clock analysis of diet, exercise, education, and lifestyle factors</title><title>Aging (Albany, NY.)</title><addtitle>Aging (Albany NY)</addtitle><description>Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. Toward this end, we use recent epigenetic biomarkers of age that have previously been shown to predict all-cause mortality, chronic conditions, and age-related functional decline. We analyze cross-sectional data from 4,173 postmenopausal female participants from the Women's Health Initiative, as well as 402 male and female participants from the Italian cohort study, Invecchiare nel Chianti.Extrinsic epigenetic age acceleration (EEAA) exhibits significant associations with fish intake (p=0.02), moderate alcohol consumption (p=0.01), education (p=3x10
), BMI (p=0.01), and blood carotenoid levels (p=1x10
)-an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin-the first-line medication for the treatment of type 2 diabetes-does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aging - genetics</subject><subject>Aging - metabolism</subject><subject>Cohort Studies</subject><subject>Cross-Sectional Studies</subject><subject>Diet</subject><subject>Educational Status</subject><subject>Epigenesis, Genetic</subject><subject>Exercise</subject><subject>Female</subject><subject>Humans</subject><subject>Life Style</subject><subject>Middle Aged</subject><subject>Research Paper</subject><issn>1945-4589</issn><issn>1945-4589</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkE1LAzEQhoMotlaPXmV_gFvzvclFkFI_oOJFzyGbnazR7W7ZpGL_vUurpZ7mhXnmHXgQuiR4SpRk9MbWoa2nBBMi1REaE81FzoXSxwd5hM5i_MBYCsHlKRpRRbQqMB2j5_kq1NBCCi5zTec-M9vaZhNDzDqfVQHSdQbf0LsQYUjV2tkUuvZ6wKqsCR5i2jSQeetS18dzdOJtE-Hid07Q2_38dfaYL14enmZ3i9xxXaS8qjQwzHjJfMFZKQolhdTCA1CwijLJCiZLViqvBeFMSeoqqqWkpVKFoJxN0O2ud7Uul1A5aFNvG7Pqw9L2G9PZYP5v2vBu6u7LCCaJLNhQkO8KXN_F2IPf3xJstl7N1qvZeR34q8OHe_pPJPsBHAJ1Dg</recordid><startdate>20170214</startdate><enddate>20170214</enddate><creator>Quach, Austin</creator><creator>Levine, Morgan E</creator><creator>Tanaka, Toshiko</creator><creator>Lu, Ake T</creator><creator>Chen, Brian H</creator><creator>Ferrucci, Luigi</creator><creator>Ritz, Beate</creator><creator>Bandinelli, Stefania</creator><creator>Neuhouser, Marian L</creator><creator>Beasley, Jeannette M</creator><creator>Snetselaar, Linda</creator><creator>Wallace, Robert B</creator><creator>Tsao, Philip S</creator><creator>Absher, Devin</creator><creator>Assimes, Themistocles L</creator><creator>Stewart, James D</creator><creator>Li, Yun</creator><creator>Hou, Lifang</creator><creator>Baccarelli, Andrea A</creator><creator>Whitsel, Eric A</creator><creator>Horvath, Steve</creator><general>Impact Journals LLC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope></search><sort><creationdate>20170214</creationdate><title>Epigenetic clock analysis of diet, exercise, education, and lifestyle factors</title><author>Quach, Austin ; Levine, Morgan E ; Tanaka, Toshiko ; Lu, Ake T ; Chen, Brian H ; Ferrucci, Luigi ; Ritz, Beate ; Bandinelli, Stefania ; Neuhouser, Marian L ; Beasley, Jeannette M ; Snetselaar, Linda ; Wallace, Robert B ; Tsao, Philip S ; Absher, Devin ; Assimes, Themistocles L ; Stewart, James D ; Li, Yun ; Hou, Lifang ; Baccarelli, Andrea A ; Whitsel, Eric A ; Horvath, Steve</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c497t-dd9e3034b3f743b57865695fee2ea82363736b3b8f95143862cd29662b8875243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aging - genetics</topic><topic>Aging - metabolism</topic><topic>Cohort Studies</topic><topic>Cross-Sectional Studies</topic><topic>Diet</topic><topic>Educational Status</topic><topic>Epigenesis, Genetic</topic><topic>Exercise</topic><topic>Female</topic><topic>Humans</topic><topic>Life Style</topic><topic>Middle Aged</topic><topic>Research Paper</topic><toplevel>online_resources</toplevel><creatorcontrib>Quach, Austin</creatorcontrib><creatorcontrib>Levine, Morgan E</creatorcontrib><creatorcontrib>Tanaka, Toshiko</creatorcontrib><creatorcontrib>Lu, Ake T</creatorcontrib><creatorcontrib>Chen, Brian H</creatorcontrib><creatorcontrib>Ferrucci, Luigi</creatorcontrib><creatorcontrib>Ritz, Beate</creatorcontrib><creatorcontrib>Bandinelli, Stefania</creatorcontrib><creatorcontrib>Neuhouser, Marian L</creatorcontrib><creatorcontrib>Beasley, Jeannette M</creatorcontrib><creatorcontrib>Snetselaar, Linda</creatorcontrib><creatorcontrib>Wallace, Robert B</creatorcontrib><creatorcontrib>Tsao, Philip S</creatorcontrib><creatorcontrib>Absher, Devin</creatorcontrib><creatorcontrib>Assimes, Themistocles L</creatorcontrib><creatorcontrib>Stewart, James D</creatorcontrib><creatorcontrib>Li, Yun</creatorcontrib><creatorcontrib>Hou, Lifang</creatorcontrib><creatorcontrib>Baccarelli, Andrea A</creatorcontrib><creatorcontrib>Whitsel, Eric A</creatorcontrib><creatorcontrib>Horvath, Steve</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Aging (Albany, NY.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Quach, Austin</au><au>Levine, Morgan E</au><au>Tanaka, Toshiko</au><au>Lu, Ake T</au><au>Chen, Brian H</au><au>Ferrucci, Luigi</au><au>Ritz, Beate</au><au>Bandinelli, Stefania</au><au>Neuhouser, Marian L</au><au>Beasley, Jeannette M</au><au>Snetselaar, Linda</au><au>Wallace, Robert B</au><au>Tsao, Philip S</au><au>Absher, Devin</au><au>Assimes, Themistocles L</au><au>Stewart, James D</au><au>Li, Yun</au><au>Hou, Lifang</au><au>Baccarelli, Andrea A</au><au>Whitsel, Eric A</au><au>Horvath, Steve</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Epigenetic clock analysis of diet, exercise, education, and lifestyle factors</atitle><jtitle>Aging (Albany, NY.)</jtitle><addtitle>Aging (Albany NY)</addtitle><date>2017-02-14</date><risdate>2017</risdate><volume>9</volume><issue>2</issue><spage>419</spage><epage>446</epage><pages>419-446</pages><issn>1945-4589</issn><eissn>1945-4589</eissn><abstract>Behavioral and lifestyle factors have been shown to relate to a number of health-related outcomes, yet there is a need for studies that examine their relationship to molecular aging rates. 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), BMI (p=0.01), and blood carotenoid levels (p=1x10
)-an indicator of fruit and vegetable consumption, whereas intrinsic epigenetic age acceleration (IEAA) is associated with poultry intake (p=0.03) and BMI (p=0.05). Both EEAA and IEAA were also found to relate to indicators of metabolic syndrome, which appear to mediate their associations with BMI. Metformin-the first-line medication for the treatment of type 2 diabetes-does not delay epigenetic aging in this observational study. Finally, longitudinal data suggests that an increase in BMI is associated with increase in both EEAA and IEAA.Overall, the epigenetic age analysis of blood confirms the conventional wisdom regarding the benefits of eating a high plant diet with lean meats, moderate alcohol consumption, physical activity, and education, as well as the health risks of obesity and metabolic syndrome.</abstract><cop>United States</cop><pub>Impact Journals LLC</pub><pmid>28198702</pmid><doi>10.18632/aging.101168</doi><tpages>28</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Aged Aged, 80 and over Aging - genetics Aging - metabolism Cohort Studies Cross-Sectional Studies Diet Educational Status Epigenesis, Genetic Exercise Female Humans Life Style Middle Aged Research Paper |
title | Epigenetic clock analysis of diet, exercise, education, and lifestyle factors |
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