Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10−8) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.
The study of genetic influence in the making of an Olympic champion is still in its nascence, but recent work has provided findings regarding the association of the ACTN3 gene on athletic performance. The aim of this study was to examine genetic differences among elite Greek track and field athletes by analysing a mononucleotide polymorphism in exon 15 of the ACTN3 gene. Results showed that ACTN3 genotype and allele frequencies in the top power-oriented athletes were statistically significantly different from those in a representative random sample of the Greek population: the frequency of the RR ACTN3 genotype in power-oriented athletes vs. the general population was 47.94 % vs. 25.97 %. This result was even more prominent for comparison of the subgroup of sprinters to controls. The results suggest an overall strong association between the presence of the RR genotype and elite power performance.
There is heterogeneity in the observed O2peak response to similar exercise training, and different exercise approaches produce variable degrees of exercise response (trainability). The aim of this study was to combine data from different laboratories to compare O2peak trainability between various volumes of interval training and Moderate Intensity Continuous Training (MICT). For interval training, volumes were classified by the duration of total interval time. High-volume High Intensity Interval Training (HIIT) included studies that had participants complete more than 15 min of high intensity efforts per session. Low-volume HIIT/Sprint Interval Training (SIT) included studies using less than 15 min of high intensity efforts per session. In total, 677 participants across 18 aerobic exercise training interventions from eight different universities in five countries were included in the analysis. Participants had completed 3 weeks or more of either high-volume HIIT (n = 299), low-volume HIIT/SIT (n = 116), or MICT (n = 262) and were predominately men (n = 495) with a mix of healthy, elderly and clinical populations. Each training intervention improved mean O2peak at the group level (P < 0.001). After adjusting for covariates, high-volume HIIT had a significantly greater (P < 0.05) absolute O2peak increase (0.29 L/min) compared to MICT (0.20 L/min) and low-volume HIIT/SIT (0.18 L/min). Adjusted relative O2peak increase was also significantly greater (P < 0.01) in high-volume HIIT (3.3 ml/kg/min) than MICT (2.4 ml/kg/min) and insignificantly greater (P = 0.09) than low-volume HIIT/SIT (2.5 mL/kg/min). Based on a high threshold for a likely response (technical error of measurement plus the minimal clinically important difference), high-volume HIIT had significantly more (P < 0.01) likely responders (31%) compared to low-volume HIIT/SIT (16%) and MICT (21%). Covariates such as age, sex, the individual study, population group, sessions per week, study duration and the average between pre and post O2peak explained only 17.3% of the variance in O2peak trainability. In conclusion, high-volume HIIT had more likely responders to improvements in O2peak compared to low-volume HIIT/SIT and MICT.
BackgroundTo date, studies investigating the association between ACTN3 R577X and ACE I/D gene variants and elite sprint/power performance have been limited by small cohorts from mixed sport disciplines, without quantitative measures of performance. Aim: To examine the association between these variants and sprint time in elite athletes.MethodsWe collected a total of 555 best personal 100-, 200-, and 400-m times of 346 elite sprinters in a large cohort of elite Caucasian or African origin sprinters from 10 different countries. Sprinters were genotyped for ACTN3 R577X and ACE ID variants.ResultsOn average, male Caucasian sprinters with the ACTN3 577RR or the ACE DD genotype had faster best 200-m sprint time than their 577XX (21.19 ± 0.53 s vs. 21.86 ± 0.54 s, p = 0.016) and ACE II (21.33 ± 0.56 vs. 21.93 ± 0.67 sec, p = 0.004) counterparts and only one case of ACE II, and no cases of ACTN3 577XX, had a faster 200-m time than the 2012 London Olympics qualifying (vs. 12 qualified sprinters with 577RR or 577RX genotype). Caucasian sprinters with the ACE DD genotype had faster best 400-m sprint time than their ACE II counterparts (46.94 ± 1.19 s vs. 48.50 ± 1.07 s, p = 0.003). Using genetic models we found that the ACTN3 577R allele and ACE D allele dominant model account for 0.92 % and 1.48 % of sprint time variance, respectively.ConclusionsDespite sprint performance relying on many gene variants and environment, the % sprint time variance explained by ACE and ACTN3 is substantial at the elite level and might be the difference between a world record and only making the final.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2462-3) contains supplementary material, which is available to authorized users.
The gene SMART (genes and the Skeletal Muscle Adaptive Response to Training) Study aims to identify genetic variants that predict the response to both a single session of High-Intensity Interval Exercise (HIIE) and to four weeks of High-Intensity Interval Training (HIIT). While the training and testing centre is located at Victoria University, Melbourne, three other centres have been launched at Bond University, Queensland University of Technology, Australia, and the University of Brighton, UK. Currently 39 participants have already completed the study and the overall aim is to recruit 200 moderately-trained, healthy Caucasians participants (all males 18–45 y, BMI < 30). Participants will undergo exercise testing and exercise training by an identical exercise program. Dietary habits will be assessed by questionnaire and dietitian consultation. Activity history is assessed by questionnaire and current activity level is assessed by an activity monitor. Skeletal muscle biopsies and blood samples will be collected before, immediately after and 3 h post HIIE, with the fourth resting biopsy and blood sample taken after four weeks of supervised HIIT (3 training sessions per week). Each session consists of eight to fourteen 2-min intervals performed at the pre-training lactate threshold (LT) power plus 40 to 70% of the difference between pre-training lactate threshold (LT) and peak aerobic power (Wpeak). A number of muscle and blood analyses will be performed, including (but not limited to) genotyping, mitochondrial respiration, transcriptomics, protein expression analyses, and enzyme activity. The participants serve as their own controls. Even though the gene SMART study is tightly controlled, our preliminary findings still indicate considerable individual variability in both performance (in-vivo) and muscle (in-situ) adaptations to similar training. More participants are required to allow us to better investigate potential underlying genetic and molecular mechanisms responsible for this individual variability.
A common null polymorphism in the ACTN3 gene (rs1815739:C>T) results in replacement of an arginine (R) with a premature stop codon (X) at amino acid 577 in the fast muscle protein α‐actinin‐3. The ACTN3 p.Arg577Ter allele (aka p.R577* or R577X) has undergone positive selection, with an increase in the X allele frequency as modern humans migrated out of Africa into the colder, less species‐rich Eurasian climates suggesting that the absence of α‐actinin‐3 may be beneficial in these conditions. Approximately 1.5 billion people worldwide are completely deficient in α‐actinin‐3. While the absence of α‐actinin‐3 influences skeletal muscle function and metabolism this does not result in overt muscle disease. α‐Actinin‐3 deficiency (ACTN3 XX genotype) is constantly underrepresented in sprint/power performance athletes. However, recent findings from our group and others suggest that the ACTN3 R577X genotype plays a role beyond athletic performance with effects observed in ageing, bone health, and inherited muscle disorders such as McArdle disease and Duchenne muscle dystrophy. In this review, we provide an update on the current knowledge regarding the influence of ACTN3 R577X on skeletal muscle function and its potential biological and clinical implications. We also outline future research directions to explore the role of α‐actinin‐3 in healthy and diseased populations.
BackgroundStudies investigating associations between ACTN3 R577X and ACE I/D genotypes and endurance athletic status have been limited by small sample sizes from mixed sport disciplines and lack quantitative measures of performance. Aim: To examine the association between ACTN3 R577X and ACE I/D genotypes and best personal running times in a large homogeneous cohort of endurance runners.MethodsWe collected a total of 1064 personal best 1500, 3000, 5000 m and marathon running times of 698 male and female Caucasian endurance athletes from six countries (Australia, Greece, Italy, Poland, Russia and UK). Athletes were genotyped for ACTN3 R577X and ACE ID variants.ResultsThere was no association between ACTN3 R577X or ACE I/D genotype and running performance at any distance in men or women. Mean (SD) marathon times (in s) were for men: ACTN3 RR 9149 (593), RX 9221 (582), XX 9129 (582) p = 0.94; ACE DD 9182 (665), ID 9214 (549), II 9155 (492) p = 0.85; for women: ACTN3 RR 10796 (818), RX 10667 (695), XX 10675 (553) p = 0.36; ACE DD 10604 (561), ID 10766 (740), II 10771 (708) p = 0.21. Furthermore, there were no associations between these variants and running time for any distance in a sub-analysis of athletes with personal records within 20% of world records.ConclusionsThus, consistent with most case-control studies, this multi-cohort quantitative analysis demonstrates it is unlikely that ACTN3 XX genotype provides an advantage in competitive endurance running performance. For ACE II genotype, some prior studies show an association but others do not. Our data indicate it is also unlikely that ACE II genotype provides an advantage in endurance running.
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