Newborn Meconium and Urinary Metabolome Response to Maternal Gestational Diabetes Mellitus: A Preliminary Case–Control Study

Recently, the number of women suffering from gestational diabetes mellitus (GDM) has risen dramatically. GDM attracts increasing attention due to its potential harm to the heath of both the fetus and the mother. We designed this case–control study to investigate the metabolome response of newborn meconium and urine to maternal GDM. GDM mothers (<i>n</i> = 142) and healthy controls (<i>n</i> = 197) were recruited during June–July 2012 in Xiamen, China. The newborns’ metabolic profiles were acquired using liquid chromatography coupled to mass spectrometry. The data showed that meconium and urine metabolome patterns clearly discriminated GDM cases from controls. Fourteen meconium metabolic biomarkers and three urinary metabolic biomarkers were tentatively identified for GDM. Altered levels of various endogenous biomarkers revealed that GDM may induce disruptions in lipid metabolism, amino acid metabolism, and purine metabolism. An unbalanced lipid pattern is suspected to be a GDM-specific feature. Furthermore, the relationships between the potential biomarkers and GDM risk were evaluated by binary logistic regression and receiver operating characteristic analysis. A combined model of nine meconium biomarkers showed a great potential in diagnosing GDM-induced disorders

Metabolomic Analysis Reveals a Unique Urinary Pattern in Normozoospermic Infertile Men

Normozoospermic infertility has become a common and important health problem worldwide. We designed this metabolomic case-control study to investigate the possible mechanism and urinary biomarkers of normozoospermic infertility. Normozoospermic infertile cases (<i>n</i> = 71) and fertile controls (<i>n</i> = 47) were recruited. A urinary metabolome pattern could discriminate normozoospermic infertile cases from fertile controls. A total of 37 potential biomarkers were identified; these have functionally important roles in energy production, antioxidation, and hormone regulation in spermatogenesis. This gave rise to a combined biomarker pattern of leukotriene E₄, 3-hydroxypalmitoylcarnitine, aspartate, xanthosine, and methoxytryptophan pointing to a diagnostic capability (AUC = 0.901, sensitivity = 85.7%, and specificity = 86.8%) in a ROC model; these markers may highlight keynote events of normozoospermic infertility. Stalled medium- and long-chain fatty acid metabolism with improved ketone body metabolism, plus decreased levels of malate and aspartate could result in citrate cycle alterations via a malate-aspartate shuttle in ATP generation in spermatogenesis. Inhibitory alterations in the normal hormone-secreting activity in spermatogenesis were suggested in normozoospermic infertility. Folate deficiency and oxidative stress may jointly impact infertile patients. The disruption of eicosanoid metabolism and xanthine oxidase system, which were tightly associated with energy metabolism and oxidative stress, was also a potential underlying mechanism. In addition, depression might be associated with normozoospermic infertility via neural activity-related metabolites. This study suggests that the urinary metabolome can be used to differentiate normozoospermic infertile men from fertile individuals. Potential metabolic biomarkers derived from these analyses might be used to diagnose what remains a somewhat idiopathic condition and provide functional insights into its pathogenesis

Urinary Metabolomics Revealed Arsenic Internal Dose-Related Metabolic Alterations: A Proof-of-Concept Study in a Chinese Male Cohort

Urinary biomonitoring provides the most accurate arsenic exposure assessment; however, to improve the risk assessment, arsenic-related metabolic biomarkers are required to understand the internal processes that may be perturbed, which may, in turn, link the exposure to a specific health outcome. This study aimed to investigate arsenic-related urinary metabolome changes and identify dose-dependent metabolic biomarkers as a proof-of-concept of the information that could be obtained by combining metabolomics and targeted analyses. Urinary arsenic species such as inorganic arsenic, methylarsonic acid, dimethylarsinic acid and arsenobetaine were quantified using high performance liquid chromatography (HPLC)-inductively coupled plasma-mass spectrometry in a Chinese adult male cohort. Urinary metabolomics was conducted using HPLC-quadrupole time-of-flight mass spectrometry. Arsenic-related metabolic biomarkers were investigated by comparing the samples of the first and fifth quintiles of arsenic exposure classifications using a partial least-squares discriminant model. After the adjustments for age, body mass index, smoking, and alcohol consumption, five potential biomarkers related to arsenic exposure (i.e., testosterone, guanine, hippurate, acetyl-<i>N</i>-formyl-5-methoxykynuren­amine, and serine) were identified from 61 candidate metabolites; these biomarkers suggested that endocrine disruption and oxidative stress were associated with urinary arsenic levels. Testosterone, guanine, and hippurate showed a high or moderate ability to discriminate the first and fifth quintiles of arsenic exposure with area-under-curve (AUC) values of 0.89, 0.87, and 0.83, respectively; their combination pattern showed an AUC value of 0.91 with a sensitivity of 88% and a specificity of 80%. Arsenic dose-dependent AUC value changes were also observed. This study demonstrated that metabolomics can be used to investigate arsenic-related biomarkers of metabolic changes; the dose-dependent trends of arsenic exposure to these biomarkers may translate into the potential use of metabolic biomarkers in arsenic risk assessment. Since this was a proof-of-concept study, more research is needed to confirm the relationships we observed between arsenic exposure and biochemical changes