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Serum Predictors of Percent Lean Mass in Young Adults
Abstract
Elevatedlean (skeletal muscle)mass is associated with increased muscle strength and anaerobic exercise performance, whereas low levels of lean mass are associated with insulin resistance and, sarcopenia. Therefore, studies aimed at obtaining an improved understanding of mechanisms related to the quantity of lean mass are of interest. Percent lean mass (total lean mass/body weight x 100) in 77 young subjects (18-35y) was measured withdual-energy X-ray absorptiometry. Twenty analytes and two-hundred ninety six metabolites were evaluated with use of the standard chemistry screen and mass spectrometry (MS)-based metabolomic profiling, respectively. Sex-adjusted multivariable linear regression was used to determine serum analytes and metabolites significantly (p≤0.05 and q≤0.30) associated with percent lean mass.Two enzymes (ALP and SGOT) and, twenty-nine metaboliteswere found to be significantly associated with percent lean mass, including metabolites related to microbial metabolism, uremia, inflammation, oxidative stress, branched chain and amino acid metabolism, insulin sensitivity, glycerolipid metabolism and xenobiotics. Use of sex-adjusted stepwise regressionto obtain a final covariate predictor model identified the combination of five analytes and metabolites as overall predictors of percent lean mass (model R =82.5%). Collectively, these data suggest that a complex interplay of various metabolic processes underlies the maintenance of lean mass in young, healthy adults.
... Metabolomics (blood, urine) can accurately distinguish age and sex differences in humans [12][13][14][15][16][17][18][19], but can only partially explain the components of body composition [20][21][22][23] such as LBM, muscle and fat mass. It remains unclear whether the discrimination of LBM is nothing more than the discrimination of sex, because most of the metabolites for sex seem to be identical to the metabolites discriminating LBM [24]. ...
... It is known from other studies that age and sex differences exist in the human plasma and urine metabolome [12][13][14][15][16][17][18]. Nevertheless, if and to which extent the discrimination of sex is responsible for the discrimination of body composition, has not been clarified definitely [20][21][22][23]. Furthermore, to which extent sex is important in the prediction of REE still remains unknown. ...
... It was unclear whether metabolite profiles may also be used as a suitable tool in the prediction of REE and LBM in healthy humans. Only limited data from metabolomics studies are available so far [20][21][22][23]. In the present crosssectional study, we therefore investigated whether urine as well as plasma metabolite profiles are associated with REE or LBM in healthy subjects under resting conditions. ...
PurposeDifferences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects. Methods
We investigated 301 healthy male and female subjects (18–80 years) under standardized conditions in the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC–MS, GC–MS, LC–MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS. ResultsWhen evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately. Conclusions
We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.
... hundreds of metabolites. Several studies have identified metabolites associated with body mass index (BMI) (8,9), lean mass (10)(11)(12), and fat mass (13), which has led to the discovery of amino acids as potentially important mediators of lean and fat mass as well as insulin resistance (9,13) and diabetes development (14,15). However, with few exceptions (10), these studies used targeted metabolomics which limits the number of metabolites examined (9,16), have had samples fewer than 100 individuals (11,12), and have predominately assessed white populations (14). ...
... Several studies have identified metabolites associated with body mass index (BMI) (8,9), lean mass (10)(11)(12), and fat mass (13), which has led to the discovery of amino acids as potentially important mediators of lean and fat mass as well as insulin resistance (9,13) and diabetes development (14,15). However, with few exceptions (10), these studies used targeted metabolomics which limits the number of metabolites examined (9,16), have had samples fewer than 100 individuals (11,12), and have predominately assessed white populations (14). ...
... As the body composition of individuals may vary by race/ ethnicity, gender, and age, it is unclear whether these results are generalizable. However, the replication of prior associations with lean mass and BMI (8,(11)(12)(13)16,34) suggests that not all of the findings are not specific to our population demographics. ...
To identify biomarkers of body mass index, body fat, trunk fat, and appendicular lean mass, nontargeted metabolomics was performed in plasma from 319 black men in the Health, Aging and Body Composition study (median age 72 years, median body mass index 26.8 kg/m(2)). Body mass index was calculated from measured height and weight; percent fat, percent trunk fat, and appendicular lean mass were measured with dual-energy x-ray absorptiometry. Pearson partial correlations between body composition measures and metabolites were adjusted for age, study site, and smoking. Out of 350 metabolites, body mass index, percent fat, percent trunk fat, and appendicular lean mass were significantly correlated with 92, 48, 96, and 43 metabolites at p less than .0014. Metabolites most strongly correlated with body composition included carnitine, a marker of fatty acid oxidation (positively correlated), triacylglycerols (positively correlated), and amino acids including branched-chain amino acids (positively correlated except for acetylglycine and serine). Gaussian Graphical Models of metabolites found that 25 lipid metabolites clustered into a single network. Groups of five amino acids, three plasmalogens, and two carnitines were also observed. Findings confirm prior reports of associations between amino acids, lean mass, and fat mass in addition to associations not previously reported. Future studies should consider whether these metabolites are relevant for metabolic disease processes.
... Recent studies on metabolomic biomarkers for sarcopenia have reported that some amino acids, lipids, and metabolites related to gut bacterial metabolism are linked with sarcopenia [7][8][9][10][11][12][13][14] ; however, metabolomic analysis of sarcopenia is in its early stages. 6 The use of untargeted metabolomics allows for the observation of a wide range of metabolic features, and recent advances in untargeted metabolomics allow better compound identification due to the use of larger libraries of MS/MS spectra; comparatively, targeted metabolomics provides reliable quantitation. ...
Background
Sarcopenia is an age‐related progressive loss of muscle mass and function. Sarcopenia is a multifactorial disorder, including metabolic disturbance; therefore, metabolites may be used as circulating biomarkers for sarcopenia. We aimed to investigate potential biomarkers of sarcopenia using metabolomics.
Methods
After non‐targeted metabolome profiling of plasma from mice of an aging mouse model of sarcopenia, sphingolipid metabolites and muscle cells from the animal model were evaluated using targeted metabolome profiling. The associations between sphingolipid metabolites identified from mouse and cell studies and sarcopenia status were assessed in men in an age‐matched discovery (72 cases and 72 controls) and validation (36 cases and 128 controls) cohort; women with sarcopenia (36 cases and 36 controls) were also included as a discovery cohort.
Results
Both non‐targeted and targeted metabolome profiling in the experimental studies showed an association between sphingolipid metabolites, including ceramides (CERs) and sphingomyelins (SMs), and sarcopenia. Plasma SM (16:0), CER (24:1), and SM (24:1) levels in men with sarcopenia were significantly higher in the discovery cohort than in the controls (all P < 0.05). There were no significant differences in plasma sphingolipid levels for women with or without sarcopenia. In men in the discovery cohort, an area under the receiver‐operating characteristic curve (AUROC) of SM (16:0) for low muscle strength and low muscle mass was 0.600 (95% confidence interval [CI]: 0.501–0.699) and 0.647 (95% CI: 0.557–0.737). The AUROC (95% CI) of CER (24:1) and SM (24:1) for low muscle mass in men was 0.669 (95% CI: 0.581–0.757) and 0.670 (95% CI: 0.582–0.759), respectively. Using a regression equation combining CER (24:1) and SM (16:0) levels, a sphingolipid (SphL) score was calculated; an AUROC of the SphL score for sarcopenia was 0.712 (95% CI: 0.626–0.798). The addition of the SphL score to HGS significantly improved the AUC from 0.646 (95% CI: 0.575–0.717; HGS only) to 0.751 (95% CI: 0.671–0.831, P = 0.002; HGS + SphL) in the discovery cohort. The predictive ability of the SphL score for sarcopenia was confirmed in the validation cohort (AUROC = 0.695, 95% CI: 0.591–0.799).
Conclusions
SM (16:0), reflecting low muscle strength, and CER (24:1) and SM (16:0), reflecting low muscle mass, are potential circulating biomarkers for sarcopenia in men. Further research on sphingolipid metabolites is required to confirm these results and provide additional insights into the metabolomic changes relevant to the pathogenesis and diagnosis of sarcopenia.
... It has been suggested that several factors including genetic, physiologic, metabolic, and behavioral may explain this link [5]. Prior studies have identified different circulating metabolites such as amino acids, acylcarnitines, or lipid species associated with body fat [6][7][8], lean mass [7,[9][10][11], and metabolic risk [6,12,13]. However, to date, limited metabolomic-analysis has been conducted using combinations of different metabolomic platforms to cover a wide range of metabolites and examine their association with these body composition compartments. ...
The interplay between fat mass and lean mass within human metabolism is not completely understood. We aimed to identify specific circulating metabolomic profiles associated with these body composition compartments. Cross-sectional analyses were conducted over 236 adults with overweight/obesity from the Satiety Innovation (SATIN) study. Body composition was assessed by dual-energy X-ray absorptiometry. A targeted multiplatform metabolite profiling approach was applied. Associations between 168 circulating metabolites and the body composition measures were assessed using elastic net regression analyses. The accuracy of the multimetabolite weighted models was evaluated using a 10-fold cross-validation approach and the Pearson’s correlation coefficients between metabolomic profiles and body compartments were estimated. Two different profiles including 86 and 65 metabolites were selected for % body fat and lean mass. These metabolites mainly consisted of lipids (sphingomyelins, phosphatidylcholines, lysophosphatidylcholines), acylcarnitines, and amino acids. Several metabolites overlapped between these body composition measures but none of them towards the same direction. The Pearson correlation coefficients between the metabolomic profiles and % body fat or lean mass were 0.80 and 0.79, respectively. Our findings suggest alterations in lipid metabolism, fatty acid oxidation, and protein degradation with increased adiposity and decreased lean body mass. These findings could help us to better understand the interplay between body composition compartments with human metabolic processes.
... In addition, metabolomics studies have revealed potential pathways underlying the sodium-health relations. In 119 US adults from the crossover sodium-intake feeding trial within the Dietary Approaches to Stop Hypertension (DASH)-Sodium trial, reduced sodium intake was associated with increased plasma metabolites from the microbiota-mediated tryptophan and benzoate metabolic pathways (11), such as 4ethylphenylsulfate, which has been linked to lean body mass in adults (12). In a double-blinded, crossover trial of 64 untreated UK patients with hypertension, sodium reduction was associated with elevated serum methionine sulfone and β-hydroxyisovalerate, which were associated with reduced diastolic and systolic blood pressure in the same sample, respectively (13). ...
Background:
There is increasing evidence that sodium consumption alters the gut microbiota and host metabolome in murine models and small studies in humans. However, there is a lack of population-based studies that capture large variations in sodium consumption as well as potassium consumption.
Objective:
We examined the associations of energy-adjusted dietary sodium (milligrams/kilocalorie), potassium, and sodium-to-potassium (Na/K) ratio with the microbiota and plasma metabolome in a well-characterized Chinese cohort with habitual excessive sodium and deficient potassium consumption.
Methods:
We estimated dietary intakes from 3 consecutive validated 24-h recalls and household inventories. In 2833 adults (18-80 y old, 51.2% females), we analyzed microbial (genus-level 16S ribosomal RNA) between-person diversity, using distance-based redundancy analysis (dbRDA), and within-person diversity and taxa abundance using linear regression, accounting for geographic variation in both. In a subsample (n = 392), we analyzed the overall metabolome (dbRDA) and individual metabolites (linear regression). P values for specific taxa and metabolites were false discovery rate adjusted (q-value).
Results:
Sodium, potassium, and Na/K ratio were associated with microbial between-person diversity (dbRDA P < 0.01) and several specific taxa with large geographic variation, including pathogenic Staphylococcus and Moraxellaceae, and SCFA-producing Phascolarctobacterium and Lachnospiraceae (q-value < 0.05). For example, sodium and Na/K ratio were positively associated with Staphylococcus and Moraxellaceae in Liaoning, whereas potassium was positively associated with 2 genera from Lachnospiraceae in Shanghai. Additionally, sodium, potassium, and Na/K ratio were associated with the overall metabolome (dbRDA P ≤ 0.01) and several individual metabolites, including butyrate/isobutyrate and gut-derived phenolics such as 1,2,3-benzenetriol sulfate, which was negatively associated with sodium in Guizhou (q-value < 0.05).
Conclusions:
Our findings suggest that sodium and potassium consumption is associated with taxa and metabolites that have been implicated in cardiometabolic health, providing insights into the potential roles of gut microbiota and host metabolites in the pathogenesis of sodium- and potassium-associated diseases. More studies are needed to confirm our results.
... In addition, higher levels of microbial product of HPLA were found in children with obesity [46]. HPLA has been proposed as a potential biomarker of a higher percentage of lean mass in young and healthy adults, though with an unknown mechanism [51]. Furthermore, positive correlations between changes in HPLA and weight loss, dyslipidemia parameters and OGTT and fasting glucose may suggest a possible global metabolic improvement in those subjects that benefited more from of lifestyle intervention. ...
Background & aims:
The benefits of weight loss in subjects with metabolically healthy obesity (MHO) are still a matter of controversy. We aimed to identify metabolic fingerprints and their associated pathways that discriminate women with MHO with high or low weight loss response after a lifestyle intervention, based on a hypocaloric Mediterranean diet (MedDiet) and physical activity.
Methods:
A UPLC-Q-Exactive-MS/MS metabolomics workflow was applied to plasma samples from 27 women with MHO before and after 12 months of a hypocaloric weight loss intervention with a MedDiet and increased physical activity. The subjects were stratified into two age-matched groups according to weight loss: <10% (low weight loss group, LWL) and >10% (high weight loss group, HWL). Random forest analysis was performed to identify metabolites discriminating between the LWL and the HWL as well as within-status effects. Modulated pathways and associations between metabolites and anthropometric and biochemical variables were also investigated.
Results:
Thirteen metabolites discriminated between the LWL and the HWL, including 1,5-anhydroglucitol, carotenediol, 3-(4-hydroxyphenyl)lactic acid, N-acetylaspartate and several lipid species (steroids, a plasmalogen, sphingomyelins, a bile acid and long-chain acylcarnitines). 1,5-anhydroglucitol, 3-(4-hydroxyphenyl)lactic acid and sphingomyelins were positively associated with weight variables whereas N-acetylaspartate and the plasmalogen correlated negatively with them. Changes in very long-chain acylcarnitines and hydroxyphenyllactic levels were observed in the HWL and positively correlated with fasting glucose, and changes in levels of the plasmalogen negatively correlated with insulin resistance. Additionally, the cholesterol profile was positively associated with changes in acid hydroxyphenyllactic, sphingolipids and 1,5-AG.
Conclusions:
Higher weight loss after a hypocaloric MedDiet and increased physical activity for 12 months is associated with changes in the plasma metabolome in women with MHO. These findings are associated with changes in biochemical variables and may suggest an improvement of the cardiometabolic risk profile in those patients that lose greater weight. Further studies are needed to investigate whether the response of those subjects with MHO to this intervention differs from those with unhealthy obesity.
... Because metabolites represent the downstream expression of genome, transcriptome, and proteome, their study is hence most powerful to reveal inherent omics variation closest to the disease risk/phenotype [11]. Some metabolomics studies of muscle mass have been reported, recently [12][13][14][15][16][17]. Some amino acids (e.g., leucine, isoleucine, and glutamic acid) [12,16], lipids [14], and metabolites related to gut bacterial metabolism have been linked with muscle mass. ...
Both loss of muscle mass and strength are important sarcopenia-related traits. In this study, we investigated both specific and shared serum metabolites associated with these two traits in 136 Caucasian women using a liquid chromatography-mass spectrometry method. A joint analysis of multivariate traits was used to examine the associations of individual metabolites with muscle mass measured by the body mass index-adjusted appendicular lean mass (ALM/BMI) and muscle strength measured by hand grip strength (HGS). After adjusting for multiple testing, nine metabolites including two amino acids (aspartic acid and glutamic acid) and an amino acid derive (pipecolic acid), one peptide (phenylalanyl-threonine), one carbohydrate (methyl beta-D-glucopyranoside), and four lipids (12S-HETRE, arachidonic acid, 12S-HETE, and glycerophosphocholine) were significant in the joint analysis. Of them, the two amino acids (aspartic acid and glutamic acid) and two lipids (12S-HETRE and 12S-HETE) were associated with both ALM/BMI and HGS, and the other five were only associated with ALM/BMI. The pathway analysis showed the amino acid metabolism pathways (aspartic acid and glutamic acid) might play important roles in the regulation of muscle mass and strength. In conclusion, our study identified novel metabolites associated with sarcopenia-related traits, suggesting novel metabolic pathways for muscle regulation.
... 4-Ethylphenylsulfate increased with sodium restriction and was the most strongly associated with change in sodium intake. This metabolite is produced by the gut microflora (11), and higher serum concentrations are associated with various health outcomes such as periodontal disease and lean body mass in animals or humans (12)(13)(14)(15)(16). 4-Ethylphenylsulfate is correlated with the consumption of soy products (17,18) and is elevated in the plasma of vegans compared with omnivores; a vegan diet increases concentrations of several products produced by the gut microbiota (19). ...
Background: High sodium intake is known to increase blood pressure and is difficult to measure in epidemiologic studies.
Objective: We examined the effect of sodium intake on metabolites within the DASH (Dietary Approaches to Stop Hypertension Trial)–Sodium Trial to further our understanding of the biological effects of sodium intake beyond blood pressure.
Design: The DASH-Sodium Trial randomly assigned individuals to either the DASH diet (low in fat and high in protein, low-fat dairy, and fruits and vegetables) or a control diet for 12 wk. Participants within each diet arm received, in random order, diets containing high (150 nmol or 3450 mg), medium (100 nmol or 2300 mg), and low (50 nmol or 1150 mg) amounts of sodium for 30 d (crossover design). Fasting blood samples were collected at the end of each sodium intervention. We measured 531 identified plasma metabolites in 73 participants at the end of their high- and low-sodium interventions and in 46 participants at the end of their high- and medium-sodium interventions (N = 119). We used linear mixed-effects regression to model the relation between each log-transformed metabolite and sodium intake. We also combined the resulting P values with Fisher's method to estimate the association between sodium intake and 38 metabolic pathways or groups.
Results: Six pathways were associated with sodium intake at a Bonferroni-corrected threshold of 0.0013 (e.g., fatty acid, food component or plant, benzoate, γ-glutamyl amino acid, methionine, and tryptophan). Although 82 metabolites were associated with sodium intake at a false discovery rate ≤0.10, only 4-ethylphenylsufate, a xenobiotic related to benzoate metabolism, was significant at a Bonferroni-corrected threshold (P < 10⁻⁵). Adjustment for coinciding change in blood pressure did not substantively alter the association for the top-ranked metabolites.
Conclusion: Sodium intake is associated with changes in circulating metabolites, including gut microbial, tryptophan, plant component, and γ-glutamyl amino acid–related metabolites. This trial was registered at clinicaltrials.gov as NCT00000608.
Background:
Rapid loss of lean mass during catabolic states is associated with impaired convalescence and increased mortality rates. Understanding metabolic pathways related to lean mass is needed to enable future interventions designed to combat malnutrition. This study assessed the plasma metabolome in relation to lean mass in clinically stable working adults in a US cohort.
Methods:
This cross-sectional study included 180 adults (mean ± SD, age 49.7 ± 10.0 yr, BMI 27.3 ± 5.5 kg/m2 , 64% female). Fasting plasma was analyzed using high-resolution metabolomics (HRM) via liquid chromatography/mass spectrometry. Lean mass was assessed by dual energy X-ray absorptiometry and expressed as lean mass index (LMI, lean mass kg/height m2 ). Multiple linear regression, metabolic pathway enrichment, and module analyses were used to characterize systemic metabolism associated with LMI.
Results:
Of 5,360 metabolites used in analyses, 593 were related to LMI, either upregulated or downregulated (p<0.05). These were enriched within 11 metabolic pathways, including branched chain amino acid degradation, metabolism of alanine and aspartate and other amino acids, butyrate, purines, and niacin metabolism. Module analysis revealed central associations between LMI and L-glutamate, L-leucine/L-isoleucine, L-valine, L-phenylalanine, L-methionine, and L-aspartate, among other validated metabolites.
Discussion:
These novel plasma HRM data demonstrate the wide-reaching associations of lean mass with systemic metabolism in a single snapshot. Such data may inform targeted nutrition support interventions designed to mitigate loss of lean mass and promote the regain of skeletal muscle mass and function after illness or injury. This article is protected by copyright. All rights reserved.
Objective
Obesity is associated with metabolic abnormalities, including insulin resistance and dyslipidemias. Previous studies demonstrated that genistein intake modifies the gut microbiota in mice by selectively increasing Akkermansia muciniphila , leading to reduction of metabolic endotoxemia and insulin sensitivity. However, it is not known whether the consumption of genistein in humans with obesity could modify the gut microbiota reducing the metabolic endotoxemia and insulin sensitivity.
Research design and methods
45 participants with a Homeostatic Model Assessment (HOMA) index greater than 2.5 and body mass indices of ≥30 and≤40 kg/m ² were studied. Patients were randomly distributed to consume (1) placebo treatment or (2) genistein capsules (50 mg/day) for 2 months. Blood samples were taken to evaluate glucose concentration, lipid profile and serum insulin. Insulin resistance was determined by means of the HOMA for insulin resistance (HOMA-IR) index and by an oral glucose tolerance test. After 2 months, the same variables were assessed including a serum metabolomic analysis, gut microbiota, and a skeletal muscle biopsy was obtained to study the gene expression of fatty acid oxidation.
Results
In the present study, we show that the consumption of genistein for 2 months reduced insulin resistance in subjects with obesity, accompanied by a modification of the gut microbiota taxonomy, particularly by an increase in the Verrucomicrobia phylum. In addition, subjects showed a reduction in metabolic endotoxemia and an increase in 5′-adenosine monophosphate-activated protein kinase phosphorylation and expression of genes involved in fatty acid oxidation in skeletal muscle. As a result, there was an increase in circulating metabolites of β-oxidation and ω-oxidation, acyl-carnitines and ketone bodies.
Conclusions
Change in the gut microbiota was accompanied by an improvement in insulin resistance and an increase in skeletal muscle fatty acid oxidation. Therefore, genistein could be used as a part of dietary strategies to control the abnormalities associated with obesity, particularly insulin resistance; however, long-term studies are needed.
AimsThe association between alkaline phosphatase (ALP) and mortality was reported in several subgroups of patients. But, the role of ALP in overall coronary artery disease (CAD) patients after percutaneous coronary intervention (PCI) remains unknown. The aim of this study was to examine the prognostic value of the ALP level in patients with CAD who underwent PCI with drug-eluting stent (DES).Methods and resultsWe prospectively included CAD patients who underwent PCI with DES. After exclusion of patients with liver disease and cancer, 1636 patients were selected for the analysis of clinical outcomes (median duration of follow-up; 762 days, inter-quartile range; 494-1068 days), and were classified into tertiles by baseline measurements of ALP (<63, 63-78, and >78 IU/L). After adjustment of potential confounders including angiographic data, the independent and dose-dependent association was observed between tertile of ALP and the adjusted hazard ratio (HR) of all-cause mortality (P for trend < 0.0001). Specifically, compared with the lowest ALP tertile, the adjusted HR of all-cause mortality in the highest tertile was 4.21 (95% confidence interval 2.03-8.71). In subgroup of patients with stable or unstable angina, a similar association was noted (P for trend < 0.0001). In terms of cardiovascular mortality, myocardial infarction, and stent thrombosis, the adjusted HRs in the highest ALP tertile were 3.92 (1.37-11.20), 1.98 (0.91-4.29), and 2.73 (1.33-5.61), respectively, compared with the lowest tertile. Furthermore, evaluation of both ALP and C-reactive protein provided better predictive value than either alone. Interesting result suggesting the mechanism was that ALP was significantly associated with the presence of angiographic coronary calcification (P for trend = 0.046).Conclusion
Our study demonstrated that the higher serum ALP level is an independent predictor of mortality, myocardial infarction, and stent thrombosis in CAD patients after PCI with DES.
Studies aimed at identifying serum markers of cellular metabolism (biomarkers) that are associated at baseline with aerobic capacity (VO2max) in young, healthy individuals have yet to be reported. Therefore, the goal of the present study was to use the standard chemistry screen and untargeted mass spectrometry (MS)-based metabolomic profiling to identify significant associations between baseline levels of serum analytes or metabolites with VO2max (77 subjects, age range 18–35 years). Use of multivariable linear regression identified three analytes (standard chemistry screen) and twenty-three metabolites (MS-based metabolomics) containing significant, sex-adjusted associations with VO2max. In addition, fourteen metabolites were found to contain sex-specific associations with aerobic capacity. Subsequent stepwise multivariable linear regression identified the combination of SGOT, 4-ethylphenylsulfate, tryptophan, γ-tocopherol, and α-hydroxyisovalerate as overall, sex-adjusted baseline predictors of VO2max (adjusted R
2 = 0.66). However, the results of the stepwise model were found to be sensitive to outliers; therefore, random forest (RF) regression was performed. Use of RF regression identified a combination of seven covariates that explained 57.6 % of the variability inherent in VO2max. Furthermore, inclusion of significant analytes, metabolites and sex-specific metabolites into a stepwise regression model identified the combination of five metabolites in males and seven metabolites in females as being able to explain 80 and 58 % of the variability inherent in VO2max, respectively. In conclusion, the evidence presented in the current report is the first attempt to identify baseline serum biomarkers that are significantly associated with VO2max in young, healthy adult humans.
To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study.
Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).
We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.
A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
The aim of this study was to compare and examine the biochemical and hematologic parameters of female elite athletes in different branches with sedenters. Study has been performed with voluntary participation of healthy female elite athletes and sedanters. This work started after being confirmed by ethic committee. Search has been performed on female athletes doing sport in Olympic sport branches at elite level and control group of sedentary female college students, totally 103 volunteers. In the samples of blood taken, parameters of leukocyte (WBC), erytrocyte (RBC), hemoglobin (HGB), hematocrit (HCT), platelet (PLT), glucose, creatine, aspartate aminotransferase (AST), alanine aminotransferase (ALT) were investigated. Levels of glucose, AST, ALT and creatin were analyzed using spectrophotometric method in autoanalyzer (Architect16000, Abbott) and other parameters analyzed using Backman Coulter test in the Backman LX-20 autoanalyzer. In the determination of difference among parameters, ANOVA test was used. The result of research indicated significant differences found between sedentary group and experimental group formed from female athletes in different sports branches. It can be said that more comprehensive studies on the effects of training should also be examined to determine relationship between hematologic and biochemical values, and sportive performance.
γ-Glutamyltransferase (GGT) cleaves the γ-glutamyl moiety of glutathione (GSH), an endogenous antioxidant, and is involved in mercapturic acid metabolism and in cancer drug resistance when overexpressed. Moreover, GGT converts leukotriene (LT) C4 into LTD4 implicated in various inflammatory pathologies. So far the effect of inflammatory stimuli on regulation of GGT expression and activity remained to be addressed.We found that the proinflammatory cytokine tumor necrosis factor alpha (TNFα) induced GGT promoter transactivation, mRNA and protein synthesis, as well as enzymatic activity. Remicade, a clinically used anti-TNFα antibody, small interfering RNA (siRNA) against p50 and p65 nuclear factor-kappaB (NF-κB) isoforms, curcumin, a well characterized natural NF-κB inhibitor, as well as a dominant negative inhibitor of kappaB alpha (IκBα), prevented GGT activation at various levels, illustrating the involvement of this signaling pathway in TNFα-induced stimulation. Over-expression of receptor of TNFα-1 (TNFR1), TNFR-associated factor-2 (TRAF2), TNFR-1 associated death domain (TRADD), dominant negative (DN) IκBα or NF-κB p65 further confirmed GGT promoter activation via NF-κB.Linker insertion mutagenesis of 536bp of the proximal GGT promoter revealed NF-κB and Sp1 binding sites at −110 and −78 relative to the transcription start site, responsible for basal GGT transcription. Mutation of the NF-κB site located at −110 additionally inhibited TNFα-induced promoter induction. Chromatin immunoprecipitation (ChIP) assays confirmed mutagenesis results and further demonstrated that TNFα treatment induced in vivo binding of both NF-κB and Sp1, explaining increased GGT expression, and led to RNA polymerase II recruitment under inflammatory conditions.
Purpose:
Sugars in the aqueous humour of the eye serve both as a source of nutrients to the lens and other anterior ocular tissues, and potentially as an indicator of waste products from these tissues. In this work we intended to measure the levels of sugars in human blood and aqueous humour from cataract patients with and without diabetes. After initial results we decided to identify an unknown sugar component.
Methods:
Sugars were measured by hplc. The unknown sugar peak was identified by gas chromatography/mass spectrometry
Results:
Very little fructose and sorbitol were found. Glucose levels were higher in both blood and aqueous from diabetic patients. During these analyses we found a major component that did not correspond to any sugar reported previously in aqueous humour. This was identified as a mixture of threonic and erythronic acids.
Conclusions:
Glucose levels increase in human aqueous humour in diabetes without markedly raised levels of sorbitol or fructose. Erythronic and threonic acids are normal components of aqueous humour and blood. They may be derived from glycated proteins or from degradation of ascorbic acid.
Although metabolic risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and cardiovascular disease, the underlying biological mechanisms remain poorly understood.
To identify pathways associated with cardiometabolic risk, we used liquid chromatography/mass spectrometry to determine the plasma concentrations of 45 distinct metabolites and to examine their relation to cardiometabolic risk in the Framingham Heart Study (FHS; n=1015) and the Malmö Diet and Cancer Study (MDC; n=746). We then interrogated significant findings in experimental models of cardiovascular and metabolic disease. We observed that metabolic risk factors (obesity, insulin resistance, high blood pressure, and dyslipidemia) were associated with multiple metabolites, including branched-chain amino acids, other hydrophobic amino acids, tryptophan breakdown products, and nucleotide metabolites. We observed strong associations of insulin resistance traits with glutamine (standardized regression coefficients, -0.04 to -0.22 per 1-SD change in log-glutamine; P<0.001), glutamate (0.05 to 0.14; P<0.001), and the glutamine-to-glutamate ratio (-0.05 to -0.20; P<0.001) in the discovery sample (FHS); similar associations were observed in the replication sample (MDC). High glutamine-to-glutamate ratio was associated with lower risk of incident diabetes mellitus in FHS (odds ratio, 0.79; adjusted P=0.03) but not in MDC. In experimental models, administration of glutamine in mice led to both increased glucose tolerance (P=0.01) and decreased blood pressure (P<0.05).
Biochemical profiling identified circulating metabolites not previously associated with metabolic traits. Experimentally interrogating one of these pathways demonstrated that excess glutamine relative to glutamate, resulting from exogenous administration, is associated with reduced metabolic risk in mice.