The effect of dietary creatine and supplementation on skeletal muscle creatine accumulation and subsequent degradation and on urinary creatinine excretion was investigated in 31 male subjects who ingested creatine in different quantities over varying time periods. Muscle total creatine concentration increased by approximately 20% after 6 days of creatine supplementation at a rate of 20 g/day. This elevated concentration was maintained when supplementation was continued at a rate of 2 g/day for a further 30 days. In the absence of 2 g/day supplementation, total creatine concentration gradually declined, such that 30 days after the cessation of supplementation the concentration was no different from the presupplementation value. During this period, urinary creatinine excretion was correspondingly increased. A similar, but more gradual, 20% increase in muscle total creatine concentration was observed over a period of 28 days when supplementation was undertaken at a rate of 3 g/day. In conclusion, a rapid way to "creatine load" human skeletal muscle is to ingest 20 g of creatine for 6 days. This elevated tissue concentration can then be maintained by ingestion of 2 g/day thereafter. The ingestion of 3 g creatine/day is in the long term likely to be as effective at raising tissue levels as this higher dose.

We determined the effects of intravenous infusion of amino acids (AA) at serum insulin of 5, 30, 72, and 167 mU/l on anabolic signaling, expression of ubiquitin-proteasome components, and protein turnover in muscles of healthy young men. Tripling AA availability at 5 mU/l insulin doubled incorporation of [1-¹³C]leucine [i.e., muscle protein synthesis (MPS), P < 0.01] without affecting the rate of leg protein breakdown (LPB; appearance of d₅-phenylalanine). While keeping AA availability constant, increasing insulin to 30 mU/l halved LPB ( P < 0.05) without further inhibition at higher doses, whereas rates of MPS were identical to that at 5 mU/l insulin. The phosphorylation of PKB Ser⁴⁷³ and p70^S6k Thr³⁸⁹ increased concomitantly with insulin, but whereas raising insulin to 30 mU/l increased the phosphorylation of mTOR Ser²⁴⁴⁸, 4E-BP1 Thr^37/46, or GSK3β Ser⁹ and decreased that of eEF2 Thr⁵⁶, higher insulin doses to 72 and 167 mU/l did not augment these latter responses. MAFbx and proteasome C2 subunit proteins declined as insulin increased, with MuRF-1 expression largely unchanged. Thus increasing AA and insulin availability causes changes in anabolic signaling and amounts of enzymes of the ubiquitin-proteasome pathway, which cannot be easily reconciled with observed effects on MPS or LPB.

A low maximal oxygen consumption (V̇o_2max) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇o_2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts V̇o_2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous V̇o_2max response. Two independent preintervention RNA expression data sets were generated ( n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in V̇o_2max (“predictor” genes). The HERITAGE Family Study ( n = 473) was used for genotyping. We discovered a 29-RNA signature that predicted V̇o_2max training response on a continuous scale; these genes contained ∼6 new single-nucleotide polymorphisms associated with gains in V̇o_2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., “reciprocal” RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in V̇o_2max, corresponding to ∼50% of the estimated genetic variance for V̇o_2max. In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. V̇o_2max responses to endurance training can be predicted by measuring a ∼30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.