English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Since both muscle mass and strength performance are polygenic in nature, the current study compared four genetic predisposition scores (GPS) in their ability to predict these phenotypes. Data were gathered within the framework of the first-generation Flemish Policy Research Centre “Sport, Physical Activity and Health” (2002–2004). Results are based on muscle characteristics data of 565 Flemish Caucasians (19–73 yr, 365 men). Skeletal muscle mass was determined from bioelectrical impedance. The Biodex dynamometer was used to measure isometric (PT static120° ) and isokinetic strength (PT dynamic60°  and PT dynamic240° ), ballistic movement speed (S 20% ), and muscular endurance (Work) of the knee extensors. Genotyping was done for 153 gene variants, selected on the basis of a literature search and the expression quantitative trait loci of selected genes. Four GPS were designed: a total GPS (based on the sum of all 153 variants, each favorable allele = score 1), a data-driven and weighted GPS [respectively, the sum of favorable alleles of those variants with significant b-coefficients in stepwise regression (GPS dd ), and the sum of these variants weighted with their respective partial r 2  (GPS w )], and an elastic net GPS (based on the variants that were selected by an elastic net regularization; GPS en ). It was found that four different models for a GPS were able to significantly predict up to ~7% of the variance in strength performance. GPS en  made the best prediction of SMM and Work. However, this was not the case for the remaining strength performance parameters, where best predictions were made by GPS dd  and GPS w .

physiological genomics/physiological genomics (print)

Limited potential of genetic predisposition scores to predict muscle mass and strength performance in Flemish Caucasians between 19 and 73 years of age