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Polygenic Models Partially Predict Muscle Size and Strength but Not Low Muscle Mass in Older Women

Praval Khanal Orcid Logo, Christopher I. Morse Orcid Logo, Lingxiao He Orcid Logo, Adam J. Herbert Orcid Logo, Gladys L. Onambélé-Pearson Orcid Logo, Hans Degens Orcid Logo, Martine Thomis Orcid Logo, Alun Williams, Georgina K. Stebbings Orcid Logo

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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Published in: Genes
ISSN: 2073-4425 2073-4425
Published: MDPI AG 2022
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fullrecord <?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 &#xB1; 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) &lt; 6.76 kg/m2 or relative skeletal muscle mass (%SMMr) &lt; 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 &#x201C;The genetics of sarcopenia&#x201D;</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>Onamb&#xE9;l&#xE9;-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>&#xA9; 2022 by the authors. 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spelling 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
format Journal article
container_title Genes
container_volume 13
container_issue 6
container_start_page 982
publishDate 2022
institution Swansea University
issn 2073-4425
2073-4425
doi_str_mv 10.3390/genes13060982
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
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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