PO-279 Bidirectional Associations Between Physical Activity and Adiposity From Childhood to Early Adulthood

Objective Inverse association between physical activity and adiposity in children and adolescents have been documented in numerous studies. However, few studies have examined the direction of causation between these two variables. We aimed to examine the prospective bidirectional associations between physical activity and adiposity from childhood to early adulthood. Methods A total of 396 girls (mean age, 11.2 years at baseline) participated in a longitudinal study with 1, 2, 4, and 7 year follow-ups. Body height and weight were measured, body composition was assessed by DXA and BMI and fat mass index (FMI) were calculated. Leisure-time physical activity (LTPA) and physical inactivity was obtained from questionnaire and physical activity score and inactivity time was calculated. A bivariate cross-lagged panel model was used to estimate the bidirectional associations between physical activity and measures of adiposity across follow-up waves. We further examined whether persistently high or persistently low physical activity or change of physical activity level from low to high and high to low during pubertal years had differential effects on adiposity. For this, the study participants were first divided into two groups according to the median values of their LTPA scores at baseline and at the 7 year follow-up visit. Then four activity groups were formed: consistently high (CH), consistently low (CL), change from high to low (HL), and change from low to high (LH). Analysis of variance (ANOVA) with least significant difference post hoc test was used to compare differences in adiposity between the LTPA groups. Results BMI at each measurement wave strongly predicted subsequent BMI (standardized path coefficients ranged from 0.87 to 0.95, p < 0.001 for all). Similar pattern was observed for LTPA and physical inactivity, though the path coefficients tended to be notably smaller. This auto-regressive part of the model indicates that the temporal stability of BMI from childhood to early adulthood is higher than the temporal stability of LTPA or physical inactivity over the same time period. The cross-lagged effects indicated that higher BMI at baseline and at 4-year follow-up predicted lower LTPA at 2-year and 7-year follow-ups, respectively (p<0.05 for both), but LTPA did not predict subsequent BMI at any time point. Similarly, higher FMI at baseline and at 2-year follow-up predicted lower LTPA at subsequent follow-up waves (p<0.05 for both). No associations were found between sedentary time and adiposity between any time points. The difference in participation in LTPA between consistently high and consistently low PA groups were on average 4 hours per week (p<0.001); however, no significant difference in FMI was found at baseline, 2-year or 7-year follow-up). Similarly, no significant difference in FMI was found between the groups whose LTPA level changed from high to low or from low to high.  Conclusions Our results suggest that reduced physical activity in children and adolescents is the result of increased fatness rather than its cause. Current physical activity recommendations may not be sufficient to combat pediatric obesity

PL - 036 Interactive effects of exercise and metformin on lactic metabolism in type 2 diabetes

Objective Lactic acidosis is typically caused by an imbalance in lactic metabolism. This may be attributed to several reasons and is usually a result of complex interactions. There may be an increased risk for lactic acidosis in type 2 diabetes mellitus (T2D) patients when metformin treatment and physical exercise are combined since both metformin and exercise acutely affect lactic metabolism. As timing of exercise following metformin ingestion may determine the magnitude of long-term metabolic adaptations, this study aimed to test the acute effects of exercise performed at different times following metformin ingestion on lactic metabolism in T2D patients with a randomized crossover time series study design. Methods Participants were recruited from two clinical health-care centers in China using a two-step screening procedure. First, approximately 2 523 patients with T2D were screened from the local diabetes database and clinical outpatient registration with inclusion criteria being men and women (30–65 years old) diagnosed with T2D no more than 5 years ago and treated with metformin (maximal daily dose of 2000 mg). Out of 100 potential participants who met the inclusion criteria, 56 were interested and invited to a laboratory visit. Finally, 34 patients participated in the study and of those, 26 patients (14 women and 12 men, mean age = 53.8 ± 8.6 years) completed all testing procedures. All patients visited the laboratory on 4 occasions, each separated by at least 48 hours. Initially a control visit was performed and consisted of metformin administration only (Metf) and a maximal incremental cycle ergometer test in the afternoon. Thereafter, all participants performed a high-intensity interval training session (HIIT, 3 minutes at 40% followed by 1 minute of 85% of maximum power output) 30 minutes (EX30), 60 minutes (EX60), and 90 minutes (EX90) post breakfast and metformin administration, respectively, in a randomized order. Serum lactate and glucose concentrations were assessed enzymatically, while insulin was assessed by an electrochemiluminescence immunoassay and superoxide dismutase (SOD) activity was determined by spectrophotometry. Measurements were performed before breakfast as well as both before and immediately after each exercise bout. In addition, capillary blood glucose concentrations were measured immediately after sampling using Omron AS1 glucose test strips (HGM-114) and lactate concentrations were assessed by ARKRAY Lactate Pro 2 test strips throughout each measurement day. Dietary intake was standardized on the evening prior to each laboratory day as well as between 8:00 a.m. and 4:00 p.m. during each testing day. This trial is registered with ChiCTR-IOR-16008469 on 13th of May 2016. Results During all three-exercise sessions, the capillary lactate concentrations were significantly increased to a similar extent. However, sixty minutes following metformin administration, serum lactate levels began to accumulate to the highest level, where 30% of patients showed lactate concentrations above resting values (≥2 mmol·L-1). The increased lactate concentrations were statistically associated with increased glucose when exercise was performed 60 minutes post metformin administration (r=0.384, p=0.048). Furthermore, in EX60 and EX90 lactate concentrations were 19% and 8% higher, respectively, compared to EX30. In addition, we found that after exercise but not before exercise, the lactate level was positively correlated with SOD (EX30 r=0.478 and p=0.012, EX60 r=0.562 and p=0.002, EX90 r=0.562 and p=0.003, respectively). Conclusions We found that the changes of lactate concentrations were related to the timing of exercise post meal and after metformin ingestion. Thus, timing of exercise appears to be an important factor to be considered when prescribing exercise for T2D patients treated with metformin. In the present study, the optimal timing of HIIT exercise was 30 minutes after metformin administration, which was indicated by a minimized fluctuation of both glucose and lactate levels in T2D patients. Our results also suggest that lactic metabolism and oxidative stress could be among the main underlying molecular mechanisms that elucidate the combinational therapy of exercise and metformin treatment on T2D. Since both acute exercise and metformin may induce opposite effects on ATP production and reactive oxygen species formation, it is important to conduct further studies in an attempt to define the “safe time” for exercise after metformin administration

PO-201 Aging attenuates the effect of aerobic capacity in muscle and serum metabolic profile but not in white adipose tissue

Objective Aerobic capacity is a quantitative predictor of the morbidity and mortality in many diverse patient populations. While aging is the main factor affecting aerobic capacity. The present study aimed to assess the effect of aerobic capacity and aging on metabolic profile in rats and to investigate the metabolic interactions between white adipose tissue (WAT), muscle and serum. Methods In this study, we used rat models that were selectively bred to differ in maximal running capacity (High capacity runners (HCR) and Low capacity runners (LCR)). Part of the rats were sacrificed after 9 months and the rest at 21 months. The effect of aerobic capacity on metabolic profile was assessed from 9 months old young rats (HCR-Y and LCR-Y), while the effect of aging on the metabolic profile in different capacity rats was determined comparing 9 months to 21 months old rats (HCR-O and LCR-O). Nuclear magnetic resonance (NMR) spectroscopy was performed to detect the metabolomics of WAT, muscle and serum. Partial least-squares-discriminant analysis (PLS-DA) was used for pattern recognition between HCR-Y and LCR-Y and between HCR-O and LCR-O. Metabolites with variable influence on projection (VIP) >1.0 and p<0.05 were classified as significantly different metabolites between groups. Spearman correlation was used to assess the metabolic interactions between white adipose tissue (WAT), muscle and serum. Results  HCR-Y rats had significantly higher skeletal muscle mass-to-body mass ratio (p<0.001), while lower body mass (p<0.001), fat mass (p<0.001), skeletal muscle mass (p=0.035) and fat mass to body mass ratio (p=0.004) than LCR-Y rats. The running capacity of HCR-Y rats was 132.7% (best running speed) better than LCR-Y rats (p<0.001). However, with age, the difference between body compositions between the two capacity groups became insignificant. HCR-O only had significantly lower body mass than the LCR-O (p=0.02). Running capacity (p=0.06) was 86.4% (best running speed) higher in the HCR-O rats than that of the LCR-O rats. PLS-DA revealed marked effects of aerobic capacity on metabolic profile in all three tissue types between HCR-Y and LCR-Y. The metabolic profile classification and prediction was best (i.e. sharper) in muscle than in WAT and serum. In addition, muscle and serum contained more significantly different metabolites than WAT in HCR-Y than in LCR-Y. Pathway analysis of the significantly different metabolites between HCR-Y and LCR-Y revealed that all the pathways belong to the lipid metabolism and amino acid metabolism in muscle while in serum it is only amino acid metabolism. However, in the case of the old groups, the PLS-DA gave reversed results. It revealed that WAT performed best in terms of classification and prediction of metabolites between HCR-O and LCR-O and had the most significantly different metabolites out of the three tissue types. The significantly different metabolites’ pathways belong to lipid metabolism in WAT. When assessing the metabolic interaction between different tissue types, all significantly different metabolites between HCR and LCR rats in young and old groups were moderately or strongly correlated (Spearman correlation between 0.45-0.9) with one or more metabolites in any of the three tissues. Conclusions In this study, we assessed the metabolic profile and body composition of WAT, muscle and serum in young and old rats with different aerobic capacities. We found that aerobic capacity greatly impacts body composition and the metabolic profile in muscle and serum in young rats, however the impact is attenuated with age. In addition, it is aging and not aerobic capacity that had the most influence on WAT metabolites. This suggest that WAT has more important role in aging process than previously assumed

Interactive effects of aging and aerobic capacity on energy metabolism-related metabolites of serum, skeletal muscle, and white adipose tissue

Aerobic capacity is a strong predictor of longevity. With aging, aerobic capacity decreases concomitantly with changes in whole body metabolism leading to increased disease risk. To address the role of aerobic capacity, aging, and their interaction on metabolism, we utilized rat models selectively bred for low and high intrinsic aerobic capacity (LCRs/HCRs) and compared the metabolomics of serum, muscle, and white adipose tissue (WAT) at two time points: Young rats were sacrificed at 9 months of age, and old rats were sacrificed at 21 months of age. Targeted and semi-quantitative metabolomics analysis was performed on the ultra-pressure liquid chromatography tandem mass spectrometry (UPLC-MS) platform. The effects of aerobic capacity, aging, and their interaction were studied via regression analysis. Our results showed that high aerobic capacity is associated with an accumulation of isovalerylcarnitine in muscle and serum at rest, which is likely due to more efficient leucine catabolism in muscle. With aging, several amino acids were downregulated in muscle, indicating more efficient amino acid metabolism, whereas in WAT less efficient amino acid metabolism and decreased mitochondrial beta-oxidation were observed. Our results further revealed that high aerobic capacity and aging interactively affect lipid metabolism in muscle and WAT, possibly combating unfavorable aging-related changes in whole body metabolism. Our results highlight the significant role of WAT metabolism for healthy aging.Peer reviewe

Effects of exercise and dietary intervention on serum metabolites in men with insomnia symptoms : a 6-month randomized-controlled trial

Accumulating evidence show that exercise and diet interventions are associated with improved sleep quality. Studies investigating the effects of exercise and dieting on circulating metabolomics in people with sleep disorders, particularly insomnia, are scarce. The present study is a part of a 6-month randomized lifestyle intervention on sleep disorder subjects. Seventy-two Finnish men (aged: 51.6 ± 10.1 years) with chronic insomnia symptoms who were assigned into different intervention groups completed this study (exercise n = 24, diet n = 27 and control n = 21). We found exercise and diet intervention were associated with improved sleep quality and a number of metabolites across different biochemical pathways. Although we cannot show causality, our findings may provide new insight into the biological mechanisms underlying the health effects of physical activity, diet and sleep quality. Further investigation is needed to better understand the link between lifestyle, sleep quality and metabolic health.peerReviewe

Differences in acoustic impedance of fresh and embedded human trabecular bone samples - Scanning acoustic microscopy and numerical evaluation

Trabecular bone samples are traditionally embedded and polished for scanning acoustic microscopy (SAM). The effect of sample processing, including dehydration, on the acoustic impedance of bone is unknown. In this study, acoustic impedance of human trabecular bone samples (n = 8) was experimentally assessed before (fresh) and after embedding using SAM and two-dimensional (2-D) finite-difference time domain simulations. Fresh samples were polished with sandpapers of different grit (P1000, P2500, and P4000). Experimental results indicated that acoustic impedance of samples increased significantly after embedding [mean values 3.7 MRayl (fresh), 6.1 MRayl (embedded), p < 0.001]. After polishing with different papers, no significant changes in acoustic impedance were found, even though higher mean values were detected after polishing with finer (P2500 and P4000) papers. A linear correlation (r = 0.854, p < 0.05) was found between the acoustic impedance values of embedded and fresh bone samples polished using P2500 SiC paper. In numerical simulations dehydration increased the acoustic impedance of trabecular bone (38%), whereas changes in surface roughness of bone had a minor effect on the acoustic impedance (-1.56%/0.1 μm). Thereby, the numerical simulations corroborated the experimental findings. In conclusion, acoustic impedance measurement of fresh trabecular bone is possible and may provide realistic material values similar to those of living bone

Adipose Tissue Dysfunction and Altered Systemic Amino Acid Metabolism Are Associated with Non-Alcoholic Fatty Liver Disease

Background Fatty liver is a major cause of obesity-related morbidity and mortality. The aim of this study was to identify early metabolic alterations associated with liver fat accumulation in 50- to 55- year-old men (n = 49) and women (n = 52) with and without NAFLD. Methods Hepatic fat content was measured using proton magnetic resonance spectroscopy (1 H MRS). Serum samples were analyzed using a nuclear magnetic resonance (NMR) metabolomics platform. Global gene expression profiles of adipose tissues and skeletal muscle were analyzed using Affymetrix microarrays and quantitative PCR. Muscle protein expression was analyzed by Western blot. Results Increased branched-chain amino acid (BCAA), aromatic amino acid (AAA) and orosomucoid were associated with liver fat accumulation already in its early stage, independent of sex, obesity or insulin resistance (p<0.05 for all). Significant down-regulation of BCAA catabolism and fatty acid and energy metabolism was observed in the adipose tissue of the NAFLD group (p<0.001for all), whereas no aberrant gene expression in the skeletal muscle was found. Reduced BCAA catabolic activity was inversely associated with serum BCAA and liver fat content (p<0.05 for all). Conclusions Liver fat accumulation, already in its early stage, is associated with increased serum branched-chain and aromatic amino acids. The observed associations of decreased BCAA catabolism activity, mitochondrial energy metabolism and serum BCAA concentration with liver fat content suggest that adipose tissue dysfunction may have a key role in the systemic nature of NAFLD pathogenesis.peerReviewe

Correlations between serum metabolites factor 2 (isoleucine, leucine, valine, phenylalanine, tyrosine and orosomucoid) and different adipose tissue gene expression clusters in certain pathways.

<p>Each dot represents an individual and the line is a linear regression fit line.</p

Mean metabolite component levels stratified by the healthy control and NAFLD groups (MIXED model estimated marginal means with 95% confidence intervals are given taking into account shared environment within family, and contrast estimates’ p-values were used to localize the significant differences between the two groups and group by gender interaction).

<p>NAFLD = non-alcohol fatty liver disease; values are given as mean and 95% confident interval (CI). Factor 1 (Omega 7 and 9 and saturated fatty acids, total fatty acids, mono-unsaturated fatty acids); Factor 2 (isoleucine, leucine, valine, phenylalanine, tyrosine and orosomucoid); Factor 3 (acetate, alanine, lactate, pyruvate); Factor 4 (esterified cholesterol, free cholesterol, omega 6 fatty acids, phosphoglycerides, phosphocholines and sphingomyelines); Factor 5 (beta-hydroxybutyrate, citrate, histidine); Factor 6 (acetoacetate, glutamine)</p><p>Mean metabolite component levels stratified by the healthy control and NAFLD groups (MIXED model estimated marginal means with 95% confidence intervals are given taking into account shared environment within family, and contrast estimates’ p-values were used to localize the significant differences between the two groups and group by gender interaction).</p

Physical characteristics, fat mass distribution, glucose metabolism hormones and liver enzymes in the healthy controls and NAFLD group (MIXED model estimated marginal means with 95% confidence intervals are given taking into account shared environment within family (husband and wife) and contrast estimates’ p-values were used to localize the significant differences between the two groups and group by gender interaction).

<p>NAFLD = non-alcohol fatty liver disease; BMI = body mass index; FM = fat mass of the whole body; SAT = abdominal subcutaneous adipose tissue; VAT = visceral adipose tissue; RAT = retroperitoneal adipose tissue; IMCL = intra-myocellular lipids; EMCL = extra-myocellular lipids; E = energy; SAFA = saturated fatty acids; MUFA = monounsaturated fatty acids; PUFA = polyunsaturated fatty acids; Ch = carbohydrate; PA = physical activity; hsCRP = high-sensitivity C-reactive protein; NEFA = non-esterified fatty acids; TG = triglycerides; ALP = alkaline phosphatase; ALT = alanine aminotransferase; AST = aspartate aminotransferase; GGT = γ-glutamyltransferase.</p><p>Physical characteristics, fat mass distribution, glucose metabolism hormones and liver enzymes in the healthy controls and NAFLD group (MIXED model estimated marginal means with 95% confidence intervals are given taking into account shared environment within family (husband and wife) and contrast estimates’ p-values were used to localize the significant differences between the two groups and group by gender interaction).</p