Individual distribution of muscle hypertrophy among hamstring muscle heads: Adding muscle volume where you need is not so simple

PURPOSE

The aim of this study was to determine whether a 9-week resistance training program based on high load (HL) versus low load combined with blood flow restriction (LL-BFR) induced a similar (i) distribution of muscle hypertrophy among hamstring heads (semimembranosus, SM; semitendinosus, ST; and biceps femoris long head, BF) and (ii) magnitude of tendon hypertrophy of ST, using a parallel randomized controlled trial.

METHODS

A total of 45 participants were randomly allocated to one of three groups: HL, LL-BFR, and control (CON). Both HL and LL-BFR performed a 9-week resistance training program composed of seated leg curl and stiff-leg deadlift exercises. Freehand 3D ultrasound was used to assess the changes in muscle and tendon volume.

RESULTS

The increase in ST volume was greater in HL (26.5 ± 25.5%) compared to CON (p = 0.004). No difference was found between CON and LL-BFR for the ST muscle volume (p = 0.627). The change in SM muscle volume was greater for LL-BFR (21.6 ± 27.8%) compared to CON (p = 0.025). No difference was found between HL and CON for the SM muscle volume (p = 0.178).There was no change in BF muscle volume in LL-BFR (14.0 ± 16.5%; p = 0.436) compared to CON group. No difference was found between HL and CON for the BF muscle volume (p = 1.0). Regarding ST tendon volume, we did not report an effect of training regimens (p = 0.411).

CONCLUSION

These results provide evidence that the HL program induced a selective hypertrophy of the ST while LL-BFR induced hypertrophy of SM. The magnitude of the selective hypertrophy observed within each group varied greatly between individuals. This finding suggests that it is very difficult to early determine the location of the hypertrophy among a muscle group.

Identification of risk loci for primary aldosteronism in genome-wide association studies

Primary aldosteronism affects up to 10% of hypertensive patients and is responsible for treatment resistance and increased cardiovascular risk. Here we perform a genome-wide association study in a discovery cohort of 562 cases and 950 controls and identify three main loci on chromosomes 1, 13 and X; associations on chromosome 1 and 13 are replicated in a second cohort and confirmed by a meta-analysis involving 1162 cases and 3296 controls. The association on chromosome 13 is specific to men and stronger in bilateral adrenal hyperplasia than aldosterone producing adenoma. Candidate genes located within the two loci, CASZ1 and RXFP2, are expressed in human and mouse adrenals in different cell clusters. Their overexpression in adrenocortical cells suppresses mineralocorticoid output under basal and stimulated conditions, without affecting cortisol biosynthesis. Our study identifies the first risk loci for primary aldosteronism and highlights new mechanisms for the development of aldosterone excess.

Detection of primary aldosteronism, the most common form of secondary arterial hypertension, is essential for targeted management and prevention of cardiovascular complications. Here, the authors identify genetic loci associated with primary aldosteronism, suggesting new mechanisms of disease.

Exploring the Link Between Additive Heritability and Prediction Accuracy From a Ridge Regression Perspective

Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts for the many small contributions of SNPs in the explanation of a phenotype. This paper approaches heritability from a machine learning perspective, and examines the close link between mixed models and ridge regression. Our contribution is two-fold. First, we propose estimating genomic heritability using a predictive approach via ridge regression and Generalized Cross Validation (GCV). We show that this is consistent with classical mixed model based estimation. Second, we derive simple formulae that express prediction accuracy as a function of the ratio n p , where n is the population size and p the total number of SNPs. These formulae clearly show that a high heritability does not imply an accurate prediction when p > n. Both the estimation of heritability via GCV and the prediction accuracy formulae are validated using simulated data and real data from UK Biobank.