A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Background: The outstanding performance of an elite athlete might be associated with changes in their blood metabolic profile. The aims of this study were to compare the blood metabolic profiles between moderate- and high-power and endurance elite athletes and to identify the potential metabolic pathways underlying these differences. Methods: Metabolic profiling of serum samples from 191 elite athletes from different sports disciplines (121 high- and 70 moderate-endurance athletes, including 44 high- and 144 moderate-power athletes), who participated in national or international sports events and tested negative for doping abuse at anti-doping laboratories, was performed using non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography. Multivariate analysis was conducted using orthogonal partial least squares discriminant analysis. Differences in metabolic levels between high- and moderate-power and endurance sports were assessed by univariate linear models. Results: Out of 743 analyzed metabolites, gamma-glutamyl amino acids were significantly reduced in both high-power and high-endurance athletes compared to moderate counterparts, indicating active glutathione cycle. High-endurance athletes exhibited significant increases in the levels of several sex hormone steroids involved in testosterone and progesterone synthesis, but decreases in diacylglycerols and ecosanoids. High-power athletes had increased levels of phospholipids and xanthine metabolites compared to moderate-power counterparts. Conclusions: This pilot data provides evidence that high-power and high-endurance athletes exhibit a distinct metabolic profile that reflects steroid biosynthesis, fatty acid metabolism, oxidative stress, and energy-related metabolites. Replication studies are warranted to confirm differences in the metabolic profiles associated with athletes’ elite performance in independent data sets, aiming ultimately for deeper understanding of the underlying biochemical processes that could be utilized as biomarkers with potential therapeutic implications

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects

Long-Term Survival After Transhiatal Versus Transthoracic Esophagectomy: A Population-Based Nationwide Study in Finland

Background No population-based studies comparing long-term survival after transhiatal esophagectomy (THE) and transthoracic esophagectomy (TTE) exist. This study aimed to compare the 5-year survival of esophageal cancer patients undergoing THE or TTE in a population-based nationwide setting. Methods This study included all curatively intended THE and TTE for esophageal cancer in Finland during 1987-2016, with follow-up evaluation until 31 December 2019. Cox proportional hazard models provided hazard ratios (HRs) with 95% confidence intervals (CIs) of 5-year and 90-day mortality. The results were adjusted for age, sex, year of operation, comorbidities, histology, neoadjuvant treatment, and pathologic stage. Results A total of 1338 patients underwent THE (n = 323) or TTE (n = 1015). The observed 5-year survival rate was 39.3% after THE and 45.0% after TTE (p = 0.072). In adjusted model 1, THE was not associated with greater 5-year mortality (HR 0.99; 95% CI 0.82-1.20) than TTE. In adjusted model 2, including T stage instead of pathologic stage, the 5-year mortality hazard rates after THE (HR 0.87, 95% CI 0.72-1.05) and TTE were comparable. The 90-day mortality rate for THE was higher than for TTE (adjusted HR 0.72; 95% CI 0.45-1.14). In subgroup analyses, no differences between THE and TTE were observed in Siewert II gastroesophageal junction cancers, esophageal cancers, or pN0 tumors, nor in the comparison of THE and TTE with two-field lymphadenectomy. The sensitivity analysis, including patients with missing patient records, who underwent surgery during 1996-2016 mirrored the main analysis. Conclusions This Finnish population-based nationwide study suggests no difference in 5-year or 90-day mortality after THE and TTE for esophageal cancer.</p

Multiple interactions between the alpha2C- and beta1-adrenergic receptors influence heart failure survival

<p>Abstract</p> <p>Background</p> <p>Persistent stimulation of cardiac β₁-adrenergic receptors by endogenous norepinephrine promotes heart failure progression. Polymorphisms of this gene are known to alter receptor function or expression, as are polymorphisms of the α_2C-adrenergic receptor, which regulates norepinephrine release from cardiac presynaptic nerves. The purpose of this study was to investigate possible synergistic effects of polymorphisms of these two intronless genes (<it>ADRB1 </it>and <it>ADRA2C</it>, respectively) on the risk of death/transplant in heart failure patients.</p> <p>Methods</p> <p>Sixteen sequence variations in <it>ADRA2C </it>and 17 sequence variations in <it>ADRB1 </it>were genotyped in a longitudinal study of 655 white heart failure patients. Eleven sequence variations in each gene were polymorphic in the heart failure cohort. Cox proportional hazards modeling was used to identify polymorphisms and potential intra- or intergenic interactions that influenced risk of death or cardiac transplant. A leave-one-out cross-validation method was utilized for internal validation.</p> <p>Results</p> <p>Three polymorphisms in <it>ADRA2C </it>and five polymorphisms in <it>ADRB1 </it>were involved in eight cross-validated epistatic interactions identifying several two-locus genotype classes with significant relative risks ranging from 3.02 to 9.23. There was no evidence of intragenic epistasis. Combining high risk genotype classes across epistatic pairs to take into account linkage disequilibrium, the relative risk of death or transplant was 3.35 (1.82, 6.18) relative to all other genotype classes.</p> <p>Conclusion</p> <p>Multiple polymorphisms act synergistically between the <it>ADRA2C </it>and <it>ADRB1 </it>genes to increase risk of death or cardiac transplant in heart failure patients.</p

Investigating the complex genetic architecture of ankle-brachial index, a measure of peripheral arterial disease, in non-Hispanic whites

<p>Abstract</p> <p>Background</p> <p>Atherosclerotic peripheral arterial disease (PAD) affects 8–10 million people in the United States and is associated with a marked impairment in quality of life and an increased risk of cardiovascular events. Noninvasive assessment of PAD is performed by measuring the ankle-brachial index (ABI). Complex traits, such as ABI, are influenced by a large array of genetic and environmental factors and their interactions. We attempted to characterize the genetic architecture of ABI by examining the main and interactive effects of individual single nucleotide polymorphisms (SNPs) and conventional risk factors.</p> <p>Methods</p> <p>We applied linear regression analysis to investigate the association of 435 SNPs in 112 positional and biological candidate genes with ABI and related physiological and biochemical traits in 1046 non-Hispanic white, hypertensive participants from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. The main effects of each SNP, as well as SNP-covariate and SNP-SNP interactions, were assessed to investigate how they contribute to the inter-individual variation in ABI. Multivariable linear regression models were then used to assess the joint contributions of the top SNP associations and interactions to ABI after adjustment for covariates. We reduced the chance of false positives by 1) correcting for multiple testing using the false discovery rate, 2) internal replication, and 3) four-fold cross-validation.</p> <p>Results</p> <p>When the results from these three procedures were combined, only two SNP main effects in <it>NOS3</it>, three SNP-covariate interactions (<it>ADRB2 </it>Gly 16 – lipoprotein(a) and <it>SLC4A5 </it>– diabetes interactions), and 25 SNP-SNP interactions (involving SNPs from 29 different genes) were significant, replicated, and cross-validated. Combining the top SNPs, risk factors, and their interactions into a model explained nearly 18% of variation in ABI in the sample. SNPs in six genes (<it>ADD2, ATP6V1B1, PRKAR2B, SLC17A2, SLC22A3, and TGFB3</it>) were also influencing triglycerides, C-reactive protein, homocysteine, and lipoprotein(a) levels.</p> <p>Conclusion</p> <p>We found that candidate gene SNP main effects, SNP-covariate and SNP-SNP interactions contribute to the inter-individual variation in ABI, a marker of PAD. Our findings underscore the importance of conducting systematic investigations that consider context-dependent frameworks for developing a deeper understanding of the multidimensional genetic and environmental factors that contribute to complex diseases.</p

Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study

<p>Abstract</p> <p>Background</p> <p>Subcortical white matter hyperintensity on magnetic resonance imaging (MRI) of the brain, referred to as leukoaraiosis, is associated with increased risk of stroke and dementia. Hypertension may contribute to leukoaraiosis by accelerating the process of arteriosclerosis involving penetrating small arteries and arterioles in the brain. Leukoaraiosis volume is highly heritable but shows significant inter-individual variability that is not predicted well by any clinical covariates (except for age) or by single SNPs.</p> <p>Methods</p> <p>As part of the Genetics of Microangiopathic Brain Injury (GMBI) Study, 777 individuals (74% hypertensive) underwent brain MRI and were genotyped for 1649 SNPs from genes known or hypothesized to be involved in arteriosclerosis and related pathways. We examined SNP main effects, epistatic (gene-gene) interactions, and context-dependent (gene-environment) interactions between these SNPs and covariates (including conventional and novel risk factors for arteriosclerosis) for association with leukoaraiosis volume. Three methods were used to reduce the chance of false positive associations: 1) false discovery rate (FDR) adjustment for multiple testing, 2) an internal replication design, and 3) a ten-iteration four-fold cross-validation scheme.</p> <p>Results</p> <p>Four SNP main effects (in <it>F3</it>, <it>KITLG</it>, <it>CAPN10</it>, and <it>MMP2</it>), 12 SNP-covariate interactions (including interactions between <it>KITLG </it>and homocysteine, and between <it>TGFB3 </it>and both physical activity and C-reactive protein), and 173 SNP-SNP interactions were significant, replicated, and cross-validated. While a model containing the top single SNPs with main effects predicted only 3.72% of variation in leukoaraiosis in independent test samples, a multiple variable model that included the four most highly predictive SNP-SNP and SNP-covariate interactions predicted 11.83%.</p> <p>Conclusion</p> <p>These results indicate that the genetic architecture of leukoaraiosis is complex, yet predictive, when the contributions of SNP main effects are considered in combination with effects of SNP interactions with other genes and covariates.</p

Medium Chain Acylcarnitines Dominate the Metabolite Pattern in Humans under Moderate Intensity Exercise and Support Lipid Oxidation

Background: Exercise is an extreme physiological challenge for skeletal muscle energy metabolism and has notable health benefits. We aimed to identify and characterize metabolites, which are components of the regulatory network mediating the beneficial metabolic adaptation to exercise