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.

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.

The Prevalence and Genetic Spectrum of Familial Hypercholesterolemia in Qatar Based on Whole Genome Sequencing of 14,000 Subjects.

Familial hypercholesterolemia (FH) is an inherited disease characterized by reduced efficiency of low-density lipoprotein-cholesterol (LDL-C) removal from the blood and, consequently, an increased risk of life-threatening early cardiovascular complications. In Qatar, the prevalence of FH has not been determined and the disease, as in many countries, is largely underdiagnosed. In this study, we combined whole-genome sequencing data from the Qatar Genome Program with deep phenotype data from Qatar Biobank for 14,056 subjects to determine the genetic spectrum and estimate the prevalence of FH in Qatar. We used the Dutch Lipid Clinic Network (DLCN) as a diagnostic tool and scrutinized 11 FH-related genes for known pathogenic and possibly pathogenic mutations. Results revealed an estimated prevalence of 0.8% (1:125) for definite/probable cases of FH in the Qatari population. We detected 16 known pathogenic/likely pathogenic mutations in LDLR and one in PCSK9; all in a heterozygous state with high penetrance. The most common mutation was rs1064793799 (c.313+3A >C) followed by rs771019366 (p.Asp90Gly); both in LDLR. In addition, we identified 18 highly penetrant possibly pathogenic variants, of which 5 were Qatari-specific, in LDLR, APOB, PCSK9 and APOE, which are predicted to be among the top 1% most deleterious mutations in the human genome but further validations are required to confirm their pathogenicity. We did not detect any homozygous FH or autosomal recessive mutations in our study cohort. This pioneering study provides a reliable estimate of FH prevalence in Qatar based on a significantly large population-based cohort, whilst uncovering the spectrum of genetic variants associated with FH.

Metabolomics Profiling of Vitamin D Status in Relation to Dyslipidemia.

Vitamin D deficiency is a global disorder associated with several chronic illnesses including dyslipidemia and metabolic syndrome. The impact of this association with both dyslipidemia and vitamin D deficiency on metabolomics profile is not yet fully understood. This study analyses the metabolomics and lipidomic signatures in relation to vitamin D status and dyslipidemia. Metabolomics data were collected from Qatar Biobank database and categorized into four groups based on vitamin D and dyslipidemia status. Metabolomics multivariate analysis was performed using the orthogonal partial least square discriminate analysis (OPLS-DA) whilst linear models were used to assess the per-metabolite association with each of the four dyslipidemia/vitamin D combination groups. Our results indicate a high prevalence of vitamin D deficiency among the younger age group, while dyslipidemia was more prominent in the older group. A significant alteration of metabolomics profile was observed among the dyslipidemic and vitamin D deficient individuals in comparison with control groups. These modifications reflected changes in some key pathways including ceramides, diacylglycerols, hemosylceramides, lysophospholipids, phosphatidylcholines, phosphatidylethanol amines, and sphingomyelins. Vitamin D deficiency and dyslipidemia have a deep impact on sphingomyelins profile. The modifications were noted at the level of ceramides and are likely to propagate through downstream pathways.

Elevated Adipsin and Reduced C5a Levels in the Maternal Serum and Follicular Fluid During Implantation Are Associated With Successful Pregnancy in Obese Women.

Introduction: Complement factors mediate the recruitment and activation of immune cells and are associated with metabolic changes during pregnancy. The aim of this study was to determine whether complement factors in the maternal serum and follicular fluid (FF) are associated with in vitro fertilization (IVF) outcomes in overweight/obese women. Methods: Forty overweight/obese (BMI = 30.8 ± 5.2 kg/m2) female patients, 33.6 ± 6.3 years old, undergoing IVF treatment for unexplained infertility were recruited. Baseline demographic information, including biochemical hormonal, metabolic, and inflammatory markers, and pregnancy outcome, was collected. Levels of 14 complement markers (C2, C4b, C5, C5a, C9, adipsin, mannose-binding lectin, C1q, C3, C3b/iC3b, C4, factor B, factor H, and properdin) were assessed in the serum and FF and compared to IVF outcome, inflammatory, and metabolic markers using multivariate and univariate models. Results: Out of 40 IVF cycles, 14 (35%) resulted in pregnancy. Compared to women with failed pregnancies, women with successful pregnancies had higher levels of adipsin in the serum and FF (p = 0.01) but lower C5a levels (p = 0.05). Serum adipsin levels were positively correlated with circulating levels of vitamin D (R = 0.5, p = 0.02), glucagon (R = 0.4, p = 0.03), leptin (R = 0.4, p = 0.01), resistin (R = 0.4, p = 0.02), and visfatin (R = 0.4, p = 0.02), but negatively correlated with total protein (R = -0.5, p = 0.03). Higher numbers of top-quality embryos were associated with increased levels of C3, properdin, C1q, factors H and B, C4, and adipsin, but with reduced C2 and C5a levels (p ≤ 0.01). Conclusions: Higher adipsin and lower C5a levels in the maternal serum during implantation are potential markers of successful outcome in obese women undergoing IVF-assisted pregnancies.

Epigenetic DNA Methylation Signatures Associated With the Severity of Paget's Disease of Bone.

Background: Paget's disease of bone (PDB) is characterized by focal areas of dysregulated bone turnover resulting in increased bone loss and abnormal bone formation with variable severity. PDB has a complex etiology and both genetics and environmental factors have been implicated. A recent study has identified many differentially methylated loci in PDB compared to healthy subjects. However, associations between DNA methylation profiles and disease severity of PDB have not been investigated. Objectives: To investigate the association between DNA methylation signals and PDB severity. Methods: Using 232 well-characterized PDB subjects from the PRISM trial, a disease severity score was devised based on the clinical features of PDB. DNA methylation profiling was performed using Illumina Infinium HumanMethylation 450K array. Results: We identified 100 CpG methylation sites significantly associated with PDB severity at FDR <0.05. Additionally, methylation profiles in 11 regions showed Bonferroni-significant association with disease severity including six islands (located in VCL, TBX5, CASZ1, ULBP2, NUDT15 and SQSTM1), two gene bodies (CXCR6 and DENND1A), and 3 promoter regions (RPL27, LINC00301 and VPS29). Moreover, FDR-significant effects from region analysis implicated genes with genetic variants previously associated with PDB severity, including RIN3 and CSF1. A multivariate predictor model featuring the top severity-associated CpG sites revealed a significant correlation (R = 0.71, p = 6.9 × 10-16) between observed and predicted PDB severity scores. On dichotomizing the severity scores into low and high severity, the model featured an area under curve (AUC) of 0.80, a sensitivity of 0.74 and a specificity of 0.68. Conclusion: We identified several CpG methylation markers that are associated with PDB severity in this pioneering study while also highlighting the novel molecular pathways associated with disease progression. Further work is warranted to affirm the suitability of our model to predict the severity of PDB in newly diagnosed patients or patients with family history of PDB.

Identification of Novel Circulating miRNAs in Patients with Acute Ischemic Stroke.

Ischemic strokes are associated with significant morbidity and mortality, but currently there are no reliable prognostic or diagnostic blood biomarkers. MicroRNAs (miRNAs) regulate various molecular pathways and may be used as biomarkers. Using RNA-Seq, we conducted comprehensive circulating miRNA profiling in patients with ischemic stroke compared with healthy controls. Samples were collected within 24 h of clinical diagnosis. Stringent analysis criteria of discovery (46 cases and 95 controls) and validation (47 cases and 96 controls) cohorts led to the identification of 10 differentially regulated miRNAs, including 5 novel miRNAs, with potential diagnostic significance. Hsa-miR-451a was the most significantly upregulated miRNA (FC; 4.8, FDR; 3.78 × 10-85), while downregulated miRNAs included hsa-miR-574-5p and hsa-miR-142-3p, among others. Importantly, we computed a multivariate classifier based on the identified miRNA panel to differentiate between ischemic stroke patients and healthy controls, which showed remarkably high sensitivity (0.94) and specificity (0.99). The area under the ROC curve was 0.97 and it is superior to other current available biomarkers. Moreover, in samples collected one month following stroke, we found sustained upregulation of hsa-miR-451a and downregulation of another 5 miRNAs. Lastly, we report 3 miRNAs that were significantly associated with poor clinical outcomes of stroke, as defined by the modified Rankin scores. The clinical translation of the identified miRNA panel may be explored further.

The Genetic Spectrum of Maturity-Onset Diabetes of the Young (MODY) in Qatar, a Population-Based Study.

Maturity-onset diabetes of the young (MODY) is a rare monogenic form of diabetes mellitus. In this study, we estimated the prevalence and genetic spectrum of MODY in the Middle Eastern population of Qatar using whole-genome sequencing (WGS) of 14,364 subjects from the population-based Qatar biobank (QBB) cohort. We focused our investigations on 14 previously identified genes ascribed to the cause of MODY and two potentially novel MODY-causing genes, RFX6 and NKX6-1. Genetic variations within the 16 MODY-related genes were assessed for their pathogenicity to identify disease-causing mutations. Analysis of QBB phenotype data revealed 72 subjects (0.5%) with type 1 diabetes, 2915 subjects (20.3%) with type 2 diabetes and 11,377 (79.2%) without diabetes. We identified 22 mutations in 67 subjects that were previously reported in the Human Genetic Mutation Database (HGMD) as disease-causing (DM) or likely disease causing (DM?) for MODY. We also identified 28 potentially novel MODY-causing mutations, predicted to be among the top 1% most deleterious mutations in the human genome, which showed complete (100%) disease penetrance in 34 subjects. Overall, we estimated that MODY accounts for around 2.2-3.4% of diabetes patients in Qatar. This is the first population-based study to determine the genetic spectrum and estimate the prevalence of MODY in the Middle East. Further research to characterize the newly identified mutations is warranted.

Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients.

Introduction: Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management. Methods: Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 h of intubation and a week later. A generalized linear model (GLM) was used to identify, at both time points, metabolites and clinical traits that predict the length of stay (LOS) at ICU (short ≤ 14 days/long >14 days) as well as the duration under IMV. All models were initially trained on a set of randomly selected individuals and validated on the remaining individuals in the cohort. Further validation in recently published metabolomics data of COVID-19 severity was performed. Results: A model based on hypoxanthine and betaine measured at first time point was best at predicting whether a patient is likely to experience a short or long stay at ICU [area under curve (AUC) = 0.92]. A further model based on kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, and C24.0 sphingomyelin, measured 1 week later, accurately predicted the duration of IMV (Pearson correlation = 0.94). Both predictive models outperformed Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and differentiated COVID-19 severity in published data. Conclusion: This study has identified specific metabolites that can predict in advance LOS and IMV, which could help in the management of COVID-19 cases at ICU.

Metabolomic Profiling of Pregnancies With Polycystic Ovary Syndrome Identifies a Unique Metabolic Signature and Potential Predictive Biomarkers of Low Birth Weight.

Background: Polycystic ovary syndrome (PCOS) is a complex syndrome with clinical features of an endocrine/metabolic disorder. Various metabolites show significant association with PCOS; however, studies comparing the metabolic profile of pregnant women with and without PCOS are lacking. In this study, metabolomics analysis of blood samples collected from PCOS women and age and BMI matched controls in the second trimester of pregnancy was performed to identify metabolic differences between the two groups and determine their association with pregnancy outcome. Methods: Sixteen PCOS and fifty-two healthy women 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 alterations associated with PCOS, followed by enrichment and Receiver Operating Characteristic (ROC) analyses to determine the best indicators of pregnancy outcomes. Results: PCOS women had lower birth weight babies compared to healthy controls. As a group, systolic blood pressure (SBP) at both second trimester and at delivery negatively correlated with birth weight. Regression models indicated significant increases in the triglycerides C20:4_C34:3 and C18:2_C38:6 in the PCOS group [false discovery rate (FDR) <0.05]. Enrichment analysis revealed significant elevations in triglycerides containing arachidonic acid, linoleic acid and palmitic acid in the PCOS group. A number of indicators of baby birth weight were identified including SBP at delivery, hexosylceramide (d18:2/24:0), ceramide (d18.0/24.1) and serine, with an AUC for all predictors combined for low birth weight (≤2500grams) of 0.88 (95%CI: 0.75-1.005, p<0.001). Conclusions: PCOS pregnancies resulted in babies with a lower birth weight, marked by a unique metabolic signature that was enriched with specific triglycerides and unsaturated fatty acids. The functional significance of these associations needs further investigation.

Epigenetic analysis of Paget's disease of bone identifies differentially methylated loci that predict disease status.

Paget's disease of bone (PDB) is characterized by focal increases in disorganized bone remodeling. This study aims to characterize PDB-associated changes in DNA methylation profiles in patients' blood. Meta-analysis of data from the discovery and cross-validation set, each comprising 116 PDB cases and 130 controls, revealed significant differences in DNA methylation at 14 CpG sites, 4 CpG islands, and 6 gene-body regions. These loci, including two characterized as functional through expression quantitative trait-methylation analysis, were associated with functions related to osteoclast differentiation, mechanical loading, immune function, and viral infection. A multivariate classifier based on discovery samples was found to discriminate PDB cases and controls from the cross-validation with a sensitivity of 0.84, specificity of 0.81, and an area under curve of 92.8%. In conclusion, this study has shown for the first time that epigenetic factors contribute to the pathogenesis of PDB and may offer diagnostic markers for prediction of the disease.

Our skeleton stays healthy through an endless regeneration process, with specialized cells constantly absorbing and creating new bone tissue. Illnesses emerge when this breaking down and rebuilding cycle becomes imbalanced. For instance, in Paget's disease of bone (PDB for short) the skeleton becomes misshapen and fragile, with complications including pain, fractures, neurological problems, hearing loss and even cancer. For most patients however, symptoms are only present at an advanced stage, when irreversible damage to the skeleton has already occurred. Certain inherited genetic changes play a role in the development of PDB, but lifestyle and environmental factors are also thought to contribute. Indeed, accumulating evidence suggests that diet, pollution and infection may influence how genes involved in bone metabolism are activated. In this process, the environment may trigger chemical marks to be added onto DNA sequences, which ultimately switches specific genes on and off. To investigate whether the pattern of chemical marks in individuals with PDB may be characteristic, Diboun et al. scanned the genetic information of over 200 PDB patients, and compared it to healthy counterparts. Combining genomic analysis and machine learning revealed several chemical signatures that were remarkably different in the DNA of PDB individuals. These signatures affected sites close to genes involved in bone development, as well as response to mechanical loading and infection. This provides strong evidence that PDB could be, in part, triggered by the environment, as the placement of these marks is highly influenced by external factors. This research sheds light onto the underlying changes that trigger PDB. Future experiments should explore whether it may be possible to use these genetic changes to identify patients before the onset of irreversible and debilitating damage.

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, TFGß-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.

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.

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

Introduction: 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. Methods: 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. Results: 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 × 10-27). 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). Conclusions: 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.

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 < 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.

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.

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 < 0.05) and Factor B (p < 0.05) were elevated in T2D. At hypoglycemia: Complement C2 (p < 0.05) and Factor B (p < 0.01) remained elevated, whilst Factor I became elevated (p < 0.05) in T2D; Complement C4b became elevated in controls (p < 0.05). In the post-hypoglycemia follow up period, Complement C2, Factor B and Factor I remained elevated in T2D; in addition, Factor D, Factor H and mannose-binding protein C showed elevations in T2D, whilst properdin, complement C3b, Factor H-related protein 5, complement C1q and decay-accelerating factor (DAF) showed elevations in controls. Granger causality analysis showed that inflammatory proteins appeared to drive complement protein changes in T2D; conversely, in controls, complement proteins drove inflammatory protein changes. 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.

Metabolomics of Lean/Overweight Insulin-Resistant Females Reveals Alterations in Steroids and Fatty Acids.

BACKGROUND: The global diabetes epidemic is largely attributed to obesity-triggered metabolic syndrome. However, the impact of insulin resistance (IR) prior to obesity on the high prevalence of diabetes and the molecular mediators remain largely unknown. This study aims to compare the metabolic profiling of apparently healthy lean/overweight participants with IR and insulin sensitivity (IS), and identify the metabolic pathways underlying IR. METHODS: In this cross-sectional study, clinical and metabolic data for 200 seemingly healthy young female participants (100 IR and 100 IS) was collected from Qatar Biobank. Orthogonal partial least square analysis was performed to assess the extent of separation between individuals from the 2 groups based on measured metabolites. Classical linear models were used to identify the metabolic signature of IR, followed by elastic-net-regularized generalized linear model (GLMNET) and receiver operating characteristic (ROC) analysis to determine top metabolites associated with IR. RESULTS: Compared to lean/overweight participants with IS, those with IR showed increased androgenic steroids, including androsterone glucuronide, in addition to various microbiota byproducts, such as the phenylalanine derivative carboxyethylphenylalanine. On the other hand, participants with IS had elevated levels of long-chain fatty acids. A ROC analysis suggested better discriminatory performance using 20 metabolites selected by GLMNET in comparison to the classical clinical traits (area under curve: 0.93 vs 0.73, respectively). CONCLUSION: Our data confirm the multifactorial mechanism of IR with a diverse spectrum of emerging potential biomarkers, including steroids, long-chain fatty acids, and microbiota metabolites. Further studies are warranted to validate these markers for diagnostic and therapeutic applications.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Genome-Wide Association Study Reveals a Novel Association Between MYBPC3 Gene Polymorphism, Endurance Athlete Status, Aerobic Capacity and Steroid Metabolism.

BACKGROUND: The genetic predisposition to elite athletic performance has been a controversial subject due to the underpowered studies and the small effect size of identified genetic variants. The aims of this study were to investigate the association of common single-nucleotide polymorphisms (SNPs) with endurance athlete status in a large cohort of elite European athletes using GWAS approach, followed by replication studies in Russian and Japanese elite athletes and functional validation using metabolomics analysis. RESULTS: The association of 476,728 SNPs of Illumina DrugCore Gene chip and endurance athlete status was investigated in 796 European international-level athletes (645 males, 151 females) by comparing allelic frequencies between athletes specialized in sports with high (n = 662) and low/moderate (n = 134) aerobic component. Replication of results was performed by comparing the frequencies of the most significant SNPs between 242 and 168 elite Russian high and low/moderate aerobic athletes, respectively, and between 60 elite Japanese endurance athletes and 406 controls. A meta-analysis has identified rs1052373 (GG homozygotes) in Myosin Binding Protein (MYBPC3; implicated in cardiac hypertrophic myopathy) gene to be associated with endurance athlete status (P = 1.43 × 10-8, odd ratio 2.2). Homozygotes carriers of rs1052373 G allele in Russian athletes had significantly greater VO2 max than carriers of the AA + AG (P = 0.005). Subsequent metabolomics analysis revealed several amino acids and lipids associated with rs1052373 G allele (1.82 × 10-05) including the testosterone precursor androstenediol (3beta,17beta) disulfate. CONCLUSIONS: This is the first report of genome-wide significant SNP and related metabolites associated with elite athlete status. Further investigations of the functional relevance of the identified SNPs and metabolites in relation to enhanced athletic performance are warranted.