Skeletal muscle mitochondrial energetics are associated with maximal aerobic capacity and walking speed in older adults.

BACKGROUND: Lower ambulatory performance with aging may be related to a reduced oxidative capacity within skeletal muscle. This study examined the associations between skeletal muscle mitochondrial capacity and efficiency with walking performance in a group of older adults. METHODS: Thirty-seven older adults (mean age 78 years; 21 men and 16 women) completed an aerobic capacity (VO peak) test and measurement of preferred walking speed over 400 m. Maximal coupled (State 3; St3) mitochondrial respiration was determined by high-resolution respirometry in saponin-permeabilized myofibers obtained from percutanous biopsies of vastus lateralis (n = 22). Maximal phosphorylation capacity (ATP) of vastus lateralis was determined in vivo by P magnetic resonance spectroscopy (n = 30). Quadriceps contractile volume was determined by magnetic resonance imaging. Mitochondrial efficiency (max ATP production/max O consumption) was characterized using ATP per St3 respiration (ATP/St3). RESULTS: In vitro St3 respiration was significantly correlated with in vivo ATP (r = .47, p = .004). Total oxidative capacity of the quadriceps (St3*quadriceps contractile volume) was a determinant of VO peak (r = .33, p = .006). ATP (r = .158, p = .03) and VO peak (r = .475, p < .0001) were correlated with preferred walking speed. Inclusion of both ATP/St3 and VO peak in a multiple linear regression model improved the prediction of preferred walking speed (r = .647, p < .0001), suggesting that mitochondrial efficiency is an important determinant for preferred walking speed. CONCLUSIONS: Lower mitochondrial capacity and efficiency were both associated with slower walking speed within a group of older participants with a wide range of function. In addition to aerobic capacity, lower mitochondrial capacity and efficiency likely play roles in slowing gait speed with age.

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

BACKGROUND: Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD. METHODS: Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity. RESULTS: In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P <0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P <0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results. CONCLUSIONS: A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.