Metabolic consequences of obesity on the hypercoagulable state of polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) women have a hypercoagulable state; however, whether this is intrinsically due to PCOS or, alternatively, a consequence of its metabolic complications is unclear. We determined plasma coagulation pathway protein levels in PCOS (n = 146) and control (n = 97) women recruited to a PCOS biobank. Circulating levels of a panel of 18 clotting pathway proteins were determined by Slow Off-rate Modified Aptamer-scan plasma protein measurement. Cohorts were age matched, though PCOS had elevated body mass index (p < 0.001), insulin (p < 0.001) and C-reactive protein (CRP) (p < 0.0001). Eight pro-coagulation proteins were elevated in PCOS: plasminogen activator inhibitor-1 (p < 0.0001), fibrinogen (p < 0.01), fibrinogen gamma chain (p < 0.0001), fibronectin (p < 0.01), von Willebrand factor (p < 0.05), D-dimer (p < 0.0001), P-selectin (p < 0.05), and plasma kallikrein (p < 0.001). However, two anticoagulant proteins, vitamin K-dependent protein-S (p < 0.0001) and heparin cofactor-II (p < 0.001) were elevated and prothrombin was decreased (p < 0.05). CRP, as a marker of inflammation, and insulin resistance (HOMA-IR) correlated with 11 and 6 of the clotting proteins, respectively (p < 0.05). When matched for BMI < 25 (16 PCOS, 53 controls) HOMA-IR remained elevated (p < 0.05) and heparin cofactor-II was increased (p < 0.05). In a multivariate analysis accounting for inflammation, insulin resistance and BMI, there was no correlation of PCOS with any of the coagulation proteins. The hypercoagulable state in PCOS is not intrinsic to the disease as it can be fully accounted for by BMI, inflammation and insulin resistance

Hypoglycemia-induced changes in complement pathways in type 2 diabetes

Background and aims: An association between hypoglycaemia and adverse cardiovascular events has been suggested from longitudinal and retrospective cohort studies. The complement pathway proteins in hypoglycemia are not well studied. Here, we hypothesized that these circulating proteins would be elevated in response to hypoglycemia in type 2 diabetes (T2D) through the inflammatory response.  Methods: A prospective, parallel study in T2D (n = 23) and controls (n = 23). Subjects underwent insulin-induced hypoglycemia with blood sampling at baseline, hypoglycemia and post-hypoglycemia; SOMAscan proteomic analysis of complement pathway-related proteins, cytokines and inflammatory proteins was undertaken.  Results: At baseline: Complement C2 (p Conclusions: Baseline elevations in C2 and Factor B indicate upregulation of the complement pathway in T2D. Changes in complement pathway-related protein levels in response to hypoglycemia suggest both intrinsic and alternative pathway activation at 2-h that appears driven by the underlying inflammation in T2D and could contribute to a cardiovascular event.  ClinicalTrials.gov NCT03102801. Date of registration April 6, 2017, retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT03102801?term=NCT03102801&draw=2&rank=1.</p

Additional file 3: Figure S1. of A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Heatmap (left) and hierarchical clustering (right) of steroid metabolites featured in this study. The significant metabolites from the linear model associated with endurance are highlighted in red (right). (PPTX 73 kb

Additional file 2: Table S1. of A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Comparison of previously published metabolite changes in plasma at 60 min after completion of exercise [1] and their corresponding PC2 loading values obtained in this study. Table S2. Metabolites differentiating between moderate- and high-endurance athletes (p ≤ 0.05). Table S3. Metabolites differentiating between moderate- and high-endurance athletes (p ≤ 0.05) in males only. Table S4. Pearson’s Correlations between various sex steroid metabolites. Significant p values are highlighted (* < 0.05, ** < 0.01, *** < 0.001). Table S5. Metabolites differentiating between moderate- and high-power athletes (p ≤ 0.05). Table S6. Metabolites differentiating between moderate- and high-power athletes (p ≤ 0.05) in males only. Table S7. Gender-endurance interaction metabolites. Columns A–F show the effect of endurance on gender-interaction metabolites in males only. Columns H to L show the different effect in females. Table S8. Gender-power interaction metabolites. Columns A–F show the effect of power on gender-interaction metabolites in males only. Columns H to L show the different effect in females. (XLSX 1377 kb

MOESM1 of Triglyceride profiling in adipose tissues from obese insulin sensitive, insulin resistant and type 2 diabetes mellitus individuals

Additional file 1: Table S1. Comparison of participants’ characteristics by gender. Table S2. Differences between obese and morbidly obese subjects. Table S3. TAGs exhibiting BMI interaction

MOESM2 of Triglyceride profiling in adipose tissues from obese insulin sensitive, insulin resistant and type 2 diabetes mellitus individuals

Additional file 2: Figure S1. Adipogenic capacity of preadipocytes derived from subcutaneous (SC) and omental (OM) adipose tissues from insulin sensitive (IS), insulin resistant (IR) and type 2 diabetes mellitus (T2DM) patients. Representative images of SC and OM adipocytes form IS and IR individuals stained with DAPI in blue (nuclear staining) and lipidtox in green (lipid droplet staining) (A). A bar chart showing differences in the adipogenic capacity (percentage of differentiated adipocytes to total number of nuclei) in SC and OM preadipocytes derived from IS, IR and T2DM individuals (B). Significant differences in adipogenic capacity with disease progression were detected as reported previously [12, 15]

Additional file 1: of A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Materials and Methods. (DOCX 16 kb

Optimisation à l'échelle du paysage pour un compromis entre services écosystémiques

Metabolomics data from the second sample collection, measured by Metabolon (DS2). (XLSX 379 kb

Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance

Genetic research of elite athletic performance has been hindered by the complex phenotype and the relatively small effect size of the identified genetic variants. The aims of this study were to identify genetic predisposition to elite athletic performance by investigating genetically-influenced metabolites that discriminate elite athletes from non-elite athletes and to identify those associated with endurance sports. By conducting a genome wide association study with high-resolution metabolomics profiling in 490 elite athletes, common variant metabolic quantitative trait loci (mQTLs) were identified and compared with previously identified mQTLs in non-elite athletes. Among the identified mQTLs, those associated with endurance metabolites were determined. Two novel genetic loci in FOLH1 and VNN1 are reported in association with N-acetyl-aspartyl-glutamate and Linoleoyl ethanolamide, respectively. When focusing on endurance metabolites, one novel mQTL linking androstenediol (3alpha, 17alpha) monosulfate and SULT2A1 was identified. Potential interactions between the novel identified mQTLs and exercise are highlighted. This is the first report of common variant mQTLs linked to elite athletic performance and endurance sports with potential applications in biomarker discovery in elite athletic candidates, non-conventional anti-doping analytical approaches and therapeutic strategies.Other Information Published in: Scientific Reports License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41598-019-56496-7</p

Additional file 1: of Metabolomics of dates (Phoenix dactylifera) reveals a highly dynamic ripening process accounting for major variation in fruit composition

Figure S1. Phenotyping the date samples. Figure S2. Iterative optimization of the O2PLS-DA classifier. Figure S3. Quality control based on Metabolon/MetaSysX replicate measurements. Figure S4. Heatmap analysis based on DS1-bolon data. Figure S5. Boxplots of PC3&4 loading values arranged by metabolic class. (PPTX 567 kb