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Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women
Genes, Volume: 13, Issue: 6, Pages: 982 - 982
Swansea University Author: Alun Williams
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DOI (Published version): 10.3390/genes13060982
Abstract
Background: Heritability explains 45-82% of muscle mass and strength variation, yet polygenic models for muscle phenotypes in older women are scarce. Therefore, the objective of the present study was to (1) assess if total genotype predisposition score (GPSTOTAL) for a set of polymorphisms differed...
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2022
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<?xml version="1.0"?><rfc1807><datestamp>2022-07-26T13:24:32.0969173</datestamp><bib-version>v2</bib-version><id>60636</id><entry>2022-07-26</entry><title>Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women</title><swanseaauthors><author><sid>050a482b2c9699d25870b9c591541998</sid><firstname>Alun</firstname><surname>Williams</surname><name>Alun Williams</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-07-26</date><deptcode>FGSEN</deptcode><abstract>Background: Heritability explains 45-82% of muscle mass and strength variation, yet polygenic models for muscle phenotypes in older women are scarce. Therefore, the objective of the present study was to (1) assess if total genotype predisposition score (GPSTOTAL) for a set of polymorphisms differed between older women with low and high muscle mass, and (2) utilise a data-driven GPS (GPSDD) to predict the variance in muscle size and strength-related phenotypes. Methods: In three-hundred 60- to 91-year-old Caucasian women (70.7 ± 5.7 years), skeletal muscle mass, biceps brachii thickness, vastus lateralis anatomical cross-sectional area (VLACSA), hand grip strength (HGS), and elbow flexion (MVCEF) and knee extension (MVCKE) maximum voluntary contraction were measured. Participants were classified as having low muscle mass if the skeletal muscle index (SMI) < 6.76 kg/m2 or relative skeletal muscle mass (%SMMr) < 22.1%. Genotyping was completed for 24 single-nucleotide polymorphisms (SNPs). GPSTOTAL was calculated from 23 SNPs and compared between the low and high muscle mass groups. A GPSDD was performed to identify the association of SNPs with other skeletal muscle phenotypes. Results: There was no significant difference in GPSTOTAL between low and high muscle mass groups, irrespective of classification based on SMI or %SMMr. The GPSDD model, using 23 selected SNPs, revealed that 13 SNPs were associated with at least one skeletal muscle phenotype: HIF1A rs11549465 was associated with four phenotypes and, in descending number of phenotype associations, ACE rs4341 with three; PTK2 rs7460 and CNTFR rs2070802 with two; and MTHFR rs17421511, ACVR1B rs10783485, CNTF rs1800169, MTHFR rs1801131, MTHFR rs1537516, TRHR rs7832552, MSTN rs1805086, COL1A1 rs1800012, and FTO rs9939609 with one phenotype. The GPSDD with age included as a predictor variable explained 1.7% variance of biceps brachii thickness, 12.5% of VLACSA, 19.0% of HGS, 8.2% of MVCEF, and 9.6% of MVCKE. Conclusions: In older women, GPSTOTAL did not differ between low and high muscle mass groups. However, GPSDD was associated with muscle size and strength phenotypes. Further advancement of polygenic models to understand skeletal muscle function during ageing might become useful in targeting interventions towards older adults most likely to lose physical independence.</abstract><type>Journal Article</type><journal>Genes</journal><volume>13</volume><journalNumber>6</journalNumber><paginationStart>982</paginationStart><paginationEnd>982</paginationEnd><publisher>MDPI AG</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2073-4425</issnPrint><issnElectronic>2073-4425</issnElectronic><keywords>polygenic model; predisposing allele; skeletal muscle phenotypes; low and high muscle mass</keywords><publishedDay>30</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-05-30</publishedDate><doi>10.3390/genes13060982</doi><url/><notes>Data Availability Statement: The data used in the present study are available from reasonablerequest from corresponding author.</notes><college>COLLEGE NANME</college><department>Science and Engineering - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGSEN</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>The current study was funded by the European Commission through MOVE-AGE, an Erasmus Mundus Joint Doctorate program (2011-0015) for Praval Khanal, with the project titled “The genetics of sarcopenia”</funders><projectreference/><lastEdited>2022-07-26T13:24:32.0969173</lastEdited><Created>2022-07-26T13:19:47.9201034</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>Praval</firstname><surname>Khanal</surname><orcid>0000-0003-2060-8446</orcid><order>1</order></author><author><firstname>Christopher I.</firstname><surname>Morse</surname><orcid>0000-0002-5261-2637</orcid><order>2</order></author><author><firstname>Lingxiao</firstname><surname>He</surname><orcid>0000-0002-2395-6035</orcid><order>3</order></author><author><firstname>Adam J.</firstname><surname>Herbert</surname><orcid>0000-0001-8964-0087</orcid><order>4</order></author><author><firstname>Gladys L.</firstname><surname>Onambélé-Pearson</surname><orcid>0000-0002-1466-3265</orcid><order>5</order></author><author><firstname>Hans</firstname><surname>Degens</surname><orcid>0000-0001-7399-4841</orcid><order>6</order></author><author><firstname>Martine</firstname><surname>Thomis</surname><orcid>0000-0001-9093-2191</orcid><order>7</order></author><author><firstname>Alun</firstname><surname>Williams</surname><order>8</order></author><author><firstname>Georgina K.</firstname><surname>Stebbings</surname><orcid>0000-0003-0706-2864</orcid><order>9</order></author></authors><documents><document><filename>60636__24744__84ef2d7c780d47aa8a048fb8cca0d4ff.pdf</filename><originalFilename>60636_VoR.pdf</originalFilename><uploaded>2022-07-26T13:22:51.5606856</uploaded><type>Output</type><contentLength>680206</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2022 by the authors. 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2022-07-26T13:24:32.0969173 v2 60636 2022-07-26 Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women 050a482b2c9699d25870b9c591541998 Alun Williams Alun Williams true false 2022-07-26 FGSEN Background: Heritability explains 45-82% of muscle mass and strength variation, yet polygenic models for muscle phenotypes in older women are scarce. Therefore, the objective of the present study was to (1) assess if total genotype predisposition score (GPSTOTAL) for a set of polymorphisms differed between older women with low and high muscle mass, and (2) utilise a data-driven GPS (GPSDD) to predict the variance in muscle size and strength-related phenotypes. Methods: In three-hundred 60- to 91-year-old Caucasian women (70.7 ± 5.7 years), skeletal muscle mass, biceps brachii thickness, vastus lateralis anatomical cross-sectional area (VLACSA), hand grip strength (HGS), and elbow flexion (MVCEF) and knee extension (MVCKE) maximum voluntary contraction were measured. Participants were classified as having low muscle mass if the skeletal muscle index (SMI) < 6.76 kg/m2 or relative skeletal muscle mass (%SMMr) < 22.1%. Genotyping was completed for 24 single-nucleotide polymorphisms (SNPs). GPSTOTAL was calculated from 23 SNPs and compared between the low and high muscle mass groups. A GPSDD was performed to identify the association of SNPs with other skeletal muscle phenotypes. Results: There was no significant difference in GPSTOTAL between low and high muscle mass groups, irrespective of classification based on SMI or %SMMr. The GPSDD model, using 23 selected SNPs, revealed that 13 SNPs were associated with at least one skeletal muscle phenotype: HIF1A rs11549465 was associated with four phenotypes and, in descending number of phenotype associations, ACE rs4341 with three; PTK2 rs7460 and CNTFR rs2070802 with two; and MTHFR rs17421511, ACVR1B rs10783485, CNTF rs1800169, MTHFR rs1801131, MTHFR rs1537516, TRHR rs7832552, MSTN rs1805086, COL1A1 rs1800012, and FTO rs9939609 with one phenotype. The GPSDD with age included as a predictor variable explained 1.7% variance of biceps brachii thickness, 12.5% of VLACSA, 19.0% of HGS, 8.2% of MVCEF, and 9.6% of MVCKE. Conclusions: In older women, GPSTOTAL did not differ between low and high muscle mass groups. However, GPSDD was associated with muscle size and strength phenotypes. Further advancement of polygenic models to understand skeletal muscle function during ageing might become useful in targeting interventions towards older adults most likely to lose physical independence. Journal Article Genes 13 6 982 982 MDPI AG 2073-4425 2073-4425 polygenic model; predisposing allele; skeletal muscle phenotypes; low and high muscle mass 30 5 2022 2022-05-30 10.3390/genes13060982 Data Availability Statement: The data used in the present study are available from reasonablerequest from corresponding author. COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University The current study was funded by the European Commission through MOVE-AGE, an Erasmus Mundus Joint Doctorate program (2011-0015) for Praval Khanal, with the project titled “The genetics of sarcopenia” 2022-07-26T13:24:32.0969173 2022-07-26T13:19:47.9201034 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Praval Khanal 0000-0003-2060-8446 1 Christopher I. Morse 0000-0002-5261-2637 2 Lingxiao He 0000-0002-2395-6035 3 Adam J. Herbert 0000-0001-8964-0087 4 Gladys L. Onambélé-Pearson 0000-0002-1466-3265 5 Hans Degens 0000-0001-7399-4841 6 Martine Thomis 0000-0001-9093-2191 7 Alun Williams 8 Georgina K. Stebbings 0000-0003-0706-2864 9 60636__24744__84ef2d7c780d47aa8a048fb8cca0d4ff.pdf 60636_VoR.pdf 2022-07-26T13:22:51.5606856 Output 680206 application/pdf Version of Record true © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/ |
title |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women |
spellingShingle |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women Alun Williams |
title_short |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women |
title_full |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women |
title_fullStr |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women |
title_full_unstemmed |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women |
title_sort |
Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women |
author_id_str_mv |
050a482b2c9699d25870b9c591541998 |
author_id_fullname_str_mv |
050a482b2c9699d25870b9c591541998_***_Alun Williams |
author |
Alun Williams |
author2 |
Praval Khanal Christopher I. Morse Lingxiao He Adam J. Herbert Gladys L. Onambélé-Pearson Hans Degens Martine Thomis Alun Williams Georgina K. Stebbings |
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Journal article |
container_title |
Genes |
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13 |
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6 |
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982 |
publishDate |
2022 |
institution |
Swansea University |
issn |
2073-4425 2073-4425 |
doi_str_mv |
10.3390/genes13060982 |
publisher |
MDPI AG |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised |
document_store_str |
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description |
Background: Heritability explains 45-82% of muscle mass and strength variation, yet polygenic models for muscle phenotypes in older women are scarce. Therefore, the objective of the present study was to (1) assess if total genotype predisposition score (GPSTOTAL) for a set of polymorphisms differed between older women with low and high muscle mass, and (2) utilise a data-driven GPS (GPSDD) to predict the variance in muscle size and strength-related phenotypes. Methods: In three-hundred 60- to 91-year-old Caucasian women (70.7 ± 5.7 years), skeletal muscle mass, biceps brachii thickness, vastus lateralis anatomical cross-sectional area (VLACSA), hand grip strength (HGS), and elbow flexion (MVCEF) and knee extension (MVCKE) maximum voluntary contraction were measured. Participants were classified as having low muscle mass if the skeletal muscle index (SMI) < 6.76 kg/m2 or relative skeletal muscle mass (%SMMr) < 22.1%. Genotyping was completed for 24 single-nucleotide polymorphisms (SNPs). GPSTOTAL was calculated from 23 SNPs and compared between the low and high muscle mass groups. A GPSDD was performed to identify the association of SNPs with other skeletal muscle phenotypes. Results: There was no significant difference in GPSTOTAL between low and high muscle mass groups, irrespective of classification based on SMI or %SMMr. The GPSDD model, using 23 selected SNPs, revealed that 13 SNPs were associated with at least one skeletal muscle phenotype: HIF1A rs11549465 was associated with four phenotypes and, in descending number of phenotype associations, ACE rs4341 with three; PTK2 rs7460 and CNTFR rs2070802 with two; and MTHFR rs17421511, ACVR1B rs10783485, CNTF rs1800169, MTHFR rs1801131, MTHFR rs1537516, TRHR rs7832552, MSTN rs1805086, COL1A1 rs1800012, and FTO rs9939609 with one phenotype. The GPSDD with age included as a predictor variable explained 1.7% variance of biceps brachii thickness, 12.5% of VLACSA, 19.0% of HGS, 8.2% of MVCEF, and 9.6% of MVCKE. Conclusions: In older women, GPSTOTAL did not differ between low and high muscle mass groups. However, GPSDD was associated with muscle size and strength phenotypes. Further advancement of polygenic models to understand skeletal muscle function during ageing might become useful in targeting interventions towards older adults most likely to lose physical independence. |
published_date |
2022-05-30T04:18:54Z |
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11.021648 |