Evidence map›Paper›PMID 35741744›Full record

ArticleGenes2022

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

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

Open access · goldAbstract read
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. TheMetabolites · 2024
    Article
  5. Review
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 4 countries.

Praval KhanalMusculoskeletal Science and Sports Medicine Research Centre, Department of Sport and Exercise Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0003-2060-8446
Christopher I MorseMusculoskeletal Science and Sports Medicine Research Centre, Department of Sport and Exercise Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0002-5261-2637
Lingxiao HeMusculoskeletal Science and Sports Medicine Research Centre, Department of Sport and Exercise Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0002-2395-6035
Adam J HerbertResearch Centre for Life and Sport Sciences (CLaSS), Department of Sport and Exercise, School of Health Sciences, Birmingham City University, Birmingham B5 5JU, UK.ORCID 0000-0001-8964-0087
Gladys L Onambélé-PearsonMusculoskeletal Science and Sports Medicine Research Centre, Department of Sport and Exercise Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0002-1466-3265
Hans DegensMusculoskeletal Science and Sports Medicine Research Centre, Department of Life Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0001-7399-4841
Martine ThomisPhysical Activity, Sports & Health Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium.ORCID 0000-0001-9093-2191
Alun G WilliamsMusculoskeletal Science and Sports Medicine Research Centre, Department of Sport and Exercise Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0002-8052-8184
Georgina K StebbingsMusculoskeletal Science and Sports Medicine Research Centre, Department of Sport and Exercise Sciences, Manchester Metropolitan University Institute of Sport, Manchester Metropolitan University, Manchester M15 6BH, UK.ORCID 0000-0003-0706-2864
Manchester Metropolitan University · GBBirmingham City University · GBKU Leuven · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Hand StrengthMultifactorial InheritanceMuscle, SkeletalAgedAged, 80 and overFemaleGenotypeHumansMiddle AgedPolymorphism, Single Nucleotidelow and high muscle masspolygenic modelpredisposing alleleskeletal muscle phenotypes

Identifiers

PMID35741744
PMCPMC9223182
OpenAlexW4281809997

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.