Michelle C. Venables

Endurance training and obesity: effect on substrate metabolism and insulin sensitivity.

PURPOSE Obesity and type 2 diabetes mellitus are disease states associated with hallmark features such as insulin resistance and an impaired ability to oxidize lipids. It has recently been reported that an optimal exercise intensity for fat oxidation (FATmax) exists; we hypothesize that continuous exercise training at this specific intensity can lead to greater improvements in fat oxidation and insulin sensitivity than a eucaloric interval training program. METHODS In a counterbalanced, crossover design, eight sedentary, obese, but otherwise healthy male participants performed two 4-wk blocks of endurance training, either at a predetermined intensity eliciting maximal fat oxidation (TPCON) or at 5-min intervals of +/- 20% FATmax (TPINT). During the week preceding the exercise training and 48 h after the final exercise bout, an OGTT, V O2max test, steady-state exercise, and measurements of body composition were undertaken. Diet was controlled the day before all trials (50% carbohydrate, 35% fat, and 15% protein; approximately 2900 kcal.d). Variables were compared using two-way repeated-measures analyses of variance. RESULTS It was shown that fat oxidation rates were increased by 44% after TPCON (0.24 +/- 0.01 vs 0.35 +/- 0.03 g.min, P < 0.05) but not after TPINT, and the whole-body insulin sensitivity index was increased by 27% after TPCON (P < 0.05). These changes occurred despite no change in body weight, body mass index (BMI), waist to hip ratio (WHR), percent body fat (%BF), or V O2max. CONCLUSIONS A continuous exercise training protocol that can elicit high rates of fat oxidation increases the contribution of fat to substrate oxidation during exercise and can significantly increase insulin sensitivity compared with a eucaloric interval protocol.

Fat oxidation rates are higher during running compared with cycling over a wide range of intensities

The aim of the present study was to compare the intensity that elicits maximal fat oxidation (Fat(max)) determined using a cycle-ergometer and a treadmill-based protocol. Twelve moderately trained male subjects (66.9 +/- 1.8 mL. kg(-1). min(-1)) performed 2 graded exercise tests to exhaustion. One test was performed on a cycle ergometer while 1 test was performed on a motorized treadmill; stage duration during both trials was 3 minutes. Gas exchange measurements and heart rate (HR) recordings were performed throughout exercise. Fat oxidation rates were calculated using stoichiometric equations. Maximal fat oxidation rates were significantly higher during running compared with cycling (0.65 +/- 0.05 v 0.47 +/- 0.05 g. min(-1)). However, the intensity, which elicited maximal fat oxidation, was not significantly different between the cycle ergometer and treadmill test (62.1 +/- 3.1 v 59.2 +/- 2.8% Vo(2)max, respectively). Fat oxidation rates were significantly higher during the treadmill test compared with the cycle ergometer test from 55 to 80%Vo(2)max. Maximal oxygen uptake and maximal HR were significantly higher during the treadmill test. It was concluded that fat oxidation rates were higher during walking compared with cycling. Maximal fat oxidation was 28% higher when walking compared with cycling, but the intensity, which elicits maximal fat oxidation, is not different between these 2 exercise modes.

Training with low muscle glycogen enhances fat metabolism in well-trained cyclists.

PURPOSE To determine the effects of training with low muscle glycogen on exercise performance, substrate metabolism, and skeletal muscle adaptation. METHODS Fourteen well-trained cyclists were pair-matched and randomly assigned to HIGH- or LOW-glycogen training groups. Subjects performed nine aerobic training (AT; 90 min at 70% VO2max) and nine high-intensity interval training sessions (HIT; 8 × 5-min efforts, 1-min recovery) during a 3-wk period. HIGH trained once daily, alternating between AT on day 1 and HIT the following day, whereas LOW trained twice every second day, first performing AT and then, 1 h later, performing HIT. Pretraining and posttraining measures were a resting muscle biopsy, metabolic measures during steady-state cycling, and a time trial. RESULTS Power output during HIT was 297 ± 8 W in LOW compared with 323 ± 9 W in HIGH (P < 0.05); however, time trial performance improved by ∼10% in both groups (P < 0.05). Fat oxidation during steady-state cycling increased after training in LOW (from 26 ± 2 to 34 ± 2 μmol·kg−¹·min−¹, P < 0.01). Plasma free fatty acid oxidation was similar before and after training in both groups, but muscle-derived triacylglycerol oxidation increased after training in LOW (from 16 ± 1 to 23 ± 1 μmol·kg−¹·min−¹, P < 0.05). Training with low muscle glycogen also increased β-hydroxyacyl-CoA-dehydrogenase protein content (P < 0.01). CONCLUSIONS Training with low muscle glycogen reduced training intensity and, in performance, was no more effective than training with high muscle glycogen. However, fat oxidation was increased after training with low muscle glycogen, which may have been due to the enhanced metabolic adaptations in skeletal muscle.

Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: Tests with an easy model species, Homo sapiens

An important element in the measurement of energy budgets of free-living animals is the estimation of energy costs during locomotion. Using humans as a particularly tractable model species, we conducted treadmill experiments to test the validity of tri-axial accelerometry loggers, designed for use with animals in the field, to estimate rate of oxygen consumption (VO2: an indirect measure of metabolic rate) and speed during locomotion. The predictive power of overall dynamic body acceleration (ODBA) obtained from loggers attached to different parts of the body was compared to that of heart rate (fH). When subject identity was included in the statistical analysis, ODBA was a good, though slightly poorer, predictor of VO2 and speed during locomotion on the flat (mean of two-part regressions: R2=0.91 and 0.91, from a logger placed on the neck) and VO2 during gradient walking (single regression: R2=0.77 from a logger placed on the upper back) than was fH (R2=0.96, 0.94, 0.86, respectively). For locomotion on the flat, ODBA was still a good predictor when subject identity was replaced by subject mass and height (morphometrics typically obtainable from animals in the field; R2=0.92 and 0.89) and a slightly better overall predictor than fH (R2=0.92 and 0.85). For gradient walking, ODBA predicted VO2 more accurately than before (R2=0.83) and considerably better than did fH (R2=0.77). ODBA and fH combined were the most powerful predictor of VO2 and speed during locomotion. However, ODBA alone appears to be a good predictor and suitable for use in the field in particular, given that accelerometry traces also provide information on the timing, frequency and duration of locomotion events, and also the gait being used.

Physical inactivity and obesity: links with insulin resistance and type 2 diabetes mellitus

Data from the health survey for England 2006 1 , showed that the prevalence of type 2 diabetes mellitus (T2DM) has more than doubled in men and women since 1991. In the USA certain States have a prevalence of T2DM of greater than 10% 2 . Globally it has been reported that this increase is by no means slowing down and that the number of individuals with the disease is expected to rise from 171 million cases reported in 2000 to 366 million by the year 2030 3 . Physical inactivity and obesity are two major risk factors for the development of T2DM. In this review we will discuss evidence of an association between physical inactivity, obesity and T2DM from prospective cohort studies and clinical trials. We will also discuss some of the potential mechanisms that are thought to link obesity and physical inactivity with the major pathophysiological precursor of T2DM, insulin resistance. Copyright

Probiotic supplementation prevents high-fat, overfeeding-induced insulin resistance in human subjects

The purpose of the present study was to determine whether probiotic supplementation (Lactobacillus casei Shirota (LcS)) prevents diet-induced insulin resistance in human subjects. A total of seventeen healthy subjects were randomised to either a probiotic (n 8) or a control (n 9) group. The probiotic group consumed a LcS-fermented milk drink twice daily for 4 weeks, whereas the control group received no supplementation. Subjects maintained their normal diet for the first 3 weeks of the study, after which they consumed a high-fat (65 % of energy), high-energy (50 % increase in energy intake) diet for 7 d. Whole-body insulin sensitivity was assessed by an oral glucose tolerance test conducted before and after overfeeding. Body mass increased by 0·6 (SE 0·2) kg in the control group (P< 0·05) and by 0·3 (SE 0·2) kg in the probiotic group (P>0·05). Fasting plasma glucose concentrations increased following 7 d of overeating (control group: 5·3 (SE 0·1) v. 5·6 (SE 0·2) mmol/l before and after overfeeding, respectively, P< 0·05), whereas fasting serum insulin concentrations were maintained in both groups. Glucose AUC values increased by 10 % (from 817 (SE 45) to 899 (SE 39) mmol/l per 120 min, P< 0·05) and whole-body insulin sensitivity decreased by 27 % (from 5·3 (SE 1·4) to 3·9 (SE 0·9), P< 0·05) in the control group, whereas normal insulin sensitivity was maintained in the probiotic group (4·4 (SE 0·8) and 4·5 (SE 0·9) before and after overeating, respectively (P>0·05). These results suggest that probiotic supplementation may be useful in the prevention of diet-induced metabolic diseases such as type 2 diabetes.

An Investigation Into the Differences in Bone Density and Body Composition Measurements Between 2 GE Lunar Densitometers and Their Comparison to a 4-Component Model

We describe a study to assess the precision of the GE Lunar iDXA and the agreement between the iDXA and GE Lunar Prodigy densitometers for the measurement of regional- and total-body bone and body composition in normal to obese healthy adults. We compare the whole-body fat mass by dual-energy X-ray absorptiometry (DXA) to measurements by a 4-component (4-C) model. Sixty-nine participants, aged 37 ± 12 yr, with a body mass index of 26.2 ± 5.1 kg/cm2, were measured once on the Prodigy and twice on the iDXA. The 4-C model estimated fat mass from body mass, total body water by deuterium dilution, body volume by air displacement plethysmography, and bone mass by DXA. Agreements between measurements made on the 2 instruments and by the 4-C model were analyzed by Bland-Altman and linear regression analyses. Where appropriate, translational cross-calibration equations were derived. Differences between DXA software versions were investigated. iDXA precision was less than 2% of the measured value for all regional- and whole-body bone and body composition measurements with the exception of arm fat mass (2.28%). We found significant differences between iDXA and Prodigy (p < 0.05) whole-body and regional bone, fat mass (FM), and lean mass, with the exception of hip bone mass, area and density, and spine area. Compared to iDXA, Prodigy overestimated FM and underestimated lean mass. However, compared to 4-C, iDXA showed a smaller bias and narrower limits of agreement than Prodigy. No significant differences between software versions in FM estimations existed. Our results demonstrate excellent iDXA precision. However, significant differences exist between the 2 GE Lunar instruments, Prodigy and iDXA measurement values. A divergence from the reference 4-C observations remains in FM estimations made by DXA even following the recent advances in technology. Further studies are particularly warranted in individuals with large FM contents.

Implications of the variation in biological 18O natural abundance in body water to inform use of Bayesian methods for modelling total energy expenditure when using doubly labelled water.

Rationale Variation in 18O natural abundance can lead to errors in the calculation of total energy expenditure (TEE) when using the doubly labelled water (DLW) method. The use of Bayesian statistics allows a distribution to be assigned to 18O natural abundance, thus allowing a best‐fit value to be used in the calculation. The aim of this study was to calculate within‐subject variation in 18O natural abundance and apply this to our original working model for TEE calculation. Methods Urine samples from a cohort of 99 women, dosed with 50 g of 20% 2H2O, undertaking a 14‐day breast milk intake protocol, were analysed for 18O. The within‐subject variance was calculated and applied to a Bayesian model for the calculation of TEE in a separate cohort of 36 women. This cohort of 36 women had taken part in a DLW study and had been dosed with 80 mg/kg body weight 2H2O and 150 mg/kg body weight H2 18O. Results The average change in the δ18O value from the 99 women was 1.14‰ (0.77) [0.99, 1.29], with the average within‐subject 18O natural abundance variance being 0.13‰2 (0.25) [0.08, 0.18]. There were no significant differences in TEE (9745 (1414), 9804 (1460) and 9789 (1455) kJ/day, non‐Bayesian, Bluck Bayesian and modified Bayesian models, respectively) between methods. Conclusions Our findings demonstrate that using a reduced natural variation in 18O as calculated from a population does not impact significantly on the calculation of TEE in our model. It may therefore be more conservative to allow a larger variance to account for individual extremes.