Identification of macrophage activation-related biomarkers in obese type 2 diabetes that may be indicative of enhanced respiratory risk in COVID-19

Hyperactivation of the immune system through obesity and diabetes may enhance infection severity complicated by Acute Respiratory Distress Syndrome (ARDS). The objective was to determine the circulatory biomarkers for macrophage activation at baseline and after serum glucose normalization in obese type 2 diabetes (OT2D) subjects. A case-controlled interventional pilot study in OT2D (n = 23) and control subjects (n = 23). OT2D subjects underwent hyperinsulinemic clamp to normalize serum glucose. Plasma macrophage-related proteins were determined using Slow Off-rate Modified Aptamer-scan plasma protein measurement at baseline (control and OT2D subjects) and after 1-h of insulin clamp (OT2D subjects only). Basal M1 macrophage activation was characterized by elevated levels of M1 macrophage-specific surface proteins, CD80 and CD38, and cytokines or chemokines (CXCL1, CXCL5, RANTES) released by activated M1 macrophages. Two potent M1 macrophage activation markers, CXCL9 and CXCL10, were decreased in OT2D. Activated M2 macrophages were characterized by elevated levels of plasma CD163, TFGβ-1, MMP7 and MMP9 in OT2D. Conventional mediators of both M1 and M2 macrophage activation markers (IFN-γ, IL-4, IL-13) were not altered. No changes were observed in plasma levels of M1/M2 macrophage activation markers in OT2D in response to acute normalization of glycemia. In the basal state, macrophage activation markers are elevated, and these reflect the expression of circulatory cytokines, chemokines, growth factors and matrix metalloproteinases in obese individuals with type 2 diabetes, that were not changed by glucose normalisation. These differences could potentially predispose diabetic individuals to increased infection severity complicated by ARDS. Clinical trial reg. no: NCT03102801; registration date April 6, 2017

Statistical methods and resources for biomarker discovery using metabolomics.

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.Open Access funding provided by the Qatar National Library. This research was funded by the Qatar National Research Fund (QNRF), grant number NPRP13S-1230-190008

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

Metabolic Signature of Leukocyte Telomere Length in Elite Male Soccer Players

Introduction: Biological aging is associated with changes in the metabolic pathways. Leukocyte telomere length (LTL) is a predictive marker of biological aging; however, the underlying metabolic pathways remain largely unknown. The aim of this study was to investigate the metabolic alterations and identify the metabolic predictors of LTL in elite male soccer players. Methods: Levels of 837 blood metabolites and LTL were measured in 126 young elite male soccer players who tested negative for doping abuse at anti-doping laboratory in Italy. Multivariate analysis using orthogonal partial least squares (OPLS), univariate linear models and enrichment analyses were conducted to identify metabolites and metabolic pathways associated with LTL. Generalized linear model followed by receiver operating characteristic (ROC) analysis were conducted to identify top metabolites predictive of LTL. Results: Sixty-seven metabolites and seven metabolic pathways showed significant associations with LTL. Among enriched pathways, lysophospholipids, benzoate metabolites, and glycine/serine/threonine metabolites were elevated with longer LTL. Conversely, monoacylglycerols, sphingolipid metabolites, long chain fatty acids and polyunsaturated fatty acids were enriched with shorter telomeres. ROC analysis revealed eight metabolites that best predict LTL, including glutamine, N-acetylglutamine, xanthine, beta-sitosterol, N2-acetyllysine, stearoyl-arachidonoyl-glycerol (18:0/20:4), N-acetylserine and 3-7-dimethylurate with AUC of 0.75 (0.64–0.87, p &lt; 0.0001). Conclusion: This study characterized the metabolic activity in relation to telomere length in elite soccer players. Investigating the functional relevance of these associations could provide a better understanding of exercise physiology and pathophysiology of elite athletes.</p

Metabolic profiling of pre-gestational and gestational diabetes mellitus identifies novel predictors of pre-term delivery.

Pregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to identify potential diagnostic biomarkers in GDM/T2DM and compared to pregnancy outcome. Sixty-seven pregnant women (21 controls, 32 GDM, 14 T2DM) in their second trimester underwent targeted metabolomics of plasma samples using tandem mass spectrometry with the Biocrates MxP Quant 500 Kit. Linear regression models were used to identify the metabolic signature of GDM and T2DM, followed by generalized linear model (GLMNET) and Receiver Operating Characteristic (ROC) analysis to determine best predictors of GDM, T2DM and pre-term labor. The gestational age at delivery was 2 weeks earlier in T2DM compared to GDM and controls and correlated negatively with maternal HbA1C and systolic blood pressure and positively with serum albumin. Linear regression models revealed elevated glutamate and branched chain amino acids in GDM + T2DM group compared to controls. Regression models also revealed association of lower levels of triacylglycerols and diacylglycerols containing oleic and linoleic fatty acids with pre-term delivery. A generalized linear model ROC analyses revealed that that glutamate is the best predictors of GDM compared to controls (area under curve; AUC = 0.81). The model also revealed that phosphatidylcholine diacyl C40:2, arachidonic acid, glycochenodeoxycholic acid, and phosphatidylcholine acyl-alkyl C34:3 are the best predictors of GDM + T2DM compared to controls (AUC = 0.90). The model also revealed that the triacylglycerols C17:2/36:4 and C18:1/34:1 are the best predictors of pre-term delivery (≤ 37 weeks) (AUC = 0.84). This study highlights the metabolite alterations in women in their second trimester with diabetes mellitus and identifies predictive indicators of pre-term delivery. Future studies to confirm these associations in other cohorts and investigate their functional relevance and potential utilization for targeted therapies are warranted.This research was sponsored by QNRF, Grant no. NPRP10-1205-160010 (NAM)

Association of microRNAs With Embryo Development and Fertilization in Women Undergoing Subfertility Treatments: A Pilot Study

Objective: Small non-coding RNAs, known as microRNAs (miRNAs), have emerging regulatory functions within the ovary that have been related to fertility. This study was undertaken to determine if circulating miRNAs reflect the changes associated with the parameters of embryo development and fertilization. Methods: In this cross-sectional pilot study. Plasma miRNAs were collected from 48 sequentially presenting women in the follicular phase prior to commencing in vitro fertilization (IVF). Circulating miRNAs were measured using locked nucleic acid (LNA)-based quantitative PCR (qPCR), while an updated miRNA data set was used to determine their level of expression. Results: Body mass index and weight were associated with the miRNAs let7b-3p and miR-375, respectively (p < 0.05), with the same relationship being found between endometrium thickness at oocyte retrieval and miR-885-5p and miR-34a-5p (p < 0.05). In contrast, miR-1260a was found to be inversely associated with anti-Mullerian hormone (AMH; p = 0.007), while miR-365a-3p, miR122-5p, and miR-34a-5p correlated with embryo fertilization rates (p < 0.05). However, when omitting cases of male infertility (n = 15), miR122-5p remained significant (p < 0.05), while miR-365a-3p and miR-34a-5p no longer differed; interestingly, however, miR1260a and mir93.3p became significant (p = 0.0087/0.02, respectively). Furthermore, age was negatively associated with miR-335-3p, miR-28-5p, miR-155-5p, miR-501-3p, and miR-497-5p (p < 0.05). Live birth rate was negatively associated with miR-335-3p, miR-100-5p, miR-497-5p, let-7d, and miR-574-3p (p < 0.05), but these were not significant when age was accounted for.However, with the exclusion of male factor infertility, all those miRNAs were no longer significant, though miR.150.5p emerged as significant (p = 0.042). A beta-regression model identified miR-1260a, miR-486-5p, and miR-132-3p (p < 0.03, p = 0.0003, p < 0.00001, respectively) as the most predictive for fertilization rate. Notably, changes in detectable miRNAs were not linked to cleavage rate, top quality embryos (G3D3), and blastocyst or antral follicle count. An ingenuity pathway analysis showed that miRNAs associated with age were also associated with the variables found in reproductive system diseases. Conclusion: Plasma miRNAs prior to the IVF cycle were associated with differing demographic and IVF parameters, including age, and may be predictive biomarkers of fertilization rate

Metabolic Signatures of Type 2 Diabetes Mellitus and Hypertension in COVID-19 Patients With Different Disease Severity.

Increased COVID-19 disease severity is higher among patients with type 2 diabetes mellitus and hypertension. However, the metabolic pathways underlying this association are not fully characterized. This study aims to identify the metabolic signature associated with increased COVID-19 severity in patients with diabetes mellitus and hypertension. One hundred and fifteen COVID-19 patients were divided based on disease severity, diabetes status, and hypertension status. Targeted metabolomics of serum samples from all patients was performed using tandem mass spectrometry followed by multivariate and univariate models. Reduced levels of various triacylglycerols were observed with increased disease severity in the diabetic patients, including those containing palmitic (C16:0), docosapentaenoic (C22:5, DPA), and docosahexaenoic (C22:6, DHA) acids (FDR < 0.01). Functional enrichment analysis revealed triacylglycerols as the pathway exhibiting the most significant changes in severe COVID-19 in diabetic patients (FDR = 7.1 × 10). Similarly, reduced levels of various triacylglycerols were also observed in hypertensive patients corresponding with increased disease severity, including those containing palmitic, oleic (C18:1), and docosahexaenoic acids. Functional enrichment analysis revealed long-chain polyunsaturated fatty acids (n-3 and n-6) as the pathway exhibiting the most significant changes with increased disease severity in hypertensive patients (FDR = 0.07). Reduced levels of triacylglycerols containing specific long-chain unsaturated, monounsaturated, and polyunsaturated fatty acids are associated with increased COVID-19 severity in diabetic and hypertensive patients, offering potential novel diagnostic and therapeutic targets.This research was funded by the Qatar National Research Fund, Grant Number NPRP11S-1212-170092

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

Identification of Prognostic Metabolomic Biomarkers at the Interface of Mortality and Morbidity in Pre-Existing TB Cases Infected With SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection currently remains one of the biggest global challenges that can lead to acute respiratory distress syndrome (CARDS) in severe cases. In line with this, prior pulmonary tuberculosis (TB) is a risk factor for long-term respiratory impairment. Post-TB lung dysfunction often goes unrecognized, despite its relatively high prevalence and its association with reduced quality of life. In this study, we used a metabolomics analysis to identify potential biomarkers that aid in the prognosis of COVID-19 morbidity and mortality in post-TB infected patients. This analysis involved blood samples from 155 SARS-CoV-2 infected adults, of which 23 had a previous diagnosis of TB (post-TB), while 132 did not have a prior or current TB infection. Our analysis indicated that the vast majority (~92%) of post-TB individuals showed severe SARS-CoV-2 infection, required intensive oxygen support with a significantly high mortality rate (52.2%). Amongst individuals with severe COVID-19 symptoms, we report a significant decline in the levels of amino acids, notably the branched chains amino acids (BCAAs), more so in the post-TB cohort (FDR <= 0.05) in comparison to mild and asymptomatic cases. Indeed, we identified betaine and BCAAs as potential prognostic metabolic biomarkers of severity and mortality, respectively, in COVID-19 patients who have been exposed to TB. Moreover, we identified serum alanine as an important metabolite at the interface of severity and mortality. Hence, our data associated COVID-19 mortality and morbidity with a long-term metabolically driven consequence of TB infection. In summary, our study provides evidence for a higher mortality rate among COVID-19 infection patients who have history of prior TB infection diagnosis, which mandates validation in larger population cohorts