Comparative assessment of Graves’ disease and main extrathyroidal manifestation, Graves’ ophthalmopathy, by non-targeted metabolite profiling of blood and orbital tissue

et al. 2020

Clinical Endocrinology

123

Objective Thyroid eye disease (TED) is a debilitating condition that frequently manifests in patients suffering from Graves’ disease (GD). This study aims to analyse the prevalence of TED among GD patients, with a focus on geographical region‐specific rates. Methods Medline and Embase were searched for articles examining TED prevalence on April 2020, and articles were retrieved and sieved. Statistical analysis was performed after Freeman‐Tukey double arcsine transformation. Thereafter, results were pooled with random effects by DerSimonian and Laird model. Results Fifty‐seven articles involving 26,804 patients were included in the review. The overall pooled prevalence of TED was 40% (CI: 0.32 to 0.48) and by continent was 38% (CI: 0.31 to 0.46) for Europe, 44% (CI: 0.32 to 0.56) for Asia, 27% (CI: 0.06 to 0.56) for North America and 58% (CI: 0.55 to 0.61) for Oceania. The prevalence of TED in Southeast Asia was 35% (CI: 0.24 to 0.47) and Middle East 48% (CI: 0.19 to 0.78). Subgroup analysis showed regions with predominantly Caucasians (37%; CI: 0.28 to 0.46) had a lower prevalence of TED compared to Asians (45%; CI: 0.33 to 0.58). The pooled prevalence of lid retraction was 57% (CI: 0.39 to 0.74), proptosis 57% (CI: 0.48 to 0.65), diplopia 36% (CI: 0.24 to 0.48) and ocular hypertension 13% (CI: 0.06 to 0.19). Conclusion A substantial proportion of patients with GD have TED and often manifest as lid retraction, proptosis and diplopia. Early detection through active screening might help to mitigate the progression of TED and its associated complications.

Section: Resultsmentioning

confidence: 99%

Prevalence of thyroid eye disease in Graves’ disease: A meta‐analysis and systematic review

Chin

et al. 2020

Clinical Endocrinology

123

“…Maternal plasma samples were centrifuged at 2,000 rpm for 10 min and the supernatant stored at -70 °C until assayed. Samples were comprehensively analyzed for primary metabolomic and lipidomic signatures using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS) [See Appendix S1 for further details of methodology] [55][56][57] . In addition, all analytical procedures, including extraction, reconstitution, and MS analysis were performed in random order to minimize potential systematic errors.…”

Section: Metabolomic Profiling Of Maternal Plasmamentioning

confidence: 99%

Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia

Kang

et al. 2020

Sci Rep

Self Cite

Early identification of patients at risk of developing preeclampsia (PE) would allow providers to tailor their prenatal management and adopt preventive strategies, such as low-dose aspirin. Nevertheless, no mid-trimester biomarkers have as yet been proven useful for prediction of PE. This study investigates the ability of metabolomic biomarkers in mid-trimester maternal plasma to predict PE. A case–control study was conducted including 33 pregnant women with mid-trimester maternal plasma (gestational age [GA], 16–24 weeks) who subsequently developed PE and 66 GA-matched controls with normal outcomes (mid-trimester cohort). Plasma samples were comprehensively profiled for primary metabolic and lipidomic signatures based on gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). A potential biomarker panel was computed based on binary logistic regression and evaluated using receiver operating characteristic (ROC) analysis. To evaluate whether this panel can be also used in late pregnancy, a retrospective cohort study was conducted using plasma collected from women who delivered in the late preterm period because of PE (n = 13) or other causes (n = 21) (at-delivery cohort). Metabolomic biomarkers were compared according to the indication for delivery. Performance of the metabolomic panel to identify patients with PE was compared also to a commonly used standard, the plasma soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio. In the mid-trimester cohort, a total of 329 metabolites were identified and semi-quantified in maternal plasma using GC-TOF MS and LC-Orbitrap-MS. Binary logistic regression analysis proposed a mid-trimester biomarker panel for the prediction of PE with five metabolites (SM C28:1, SM C30:1, LysoPC C19:0, LysoPE C20:0, propane-1,3-diol). This metabolomic model predicted PE better than PlGF (AUC [95% CI]: 0.868 [0.844–0.891] vs 0.604 [0.485–0.723]) and sFlt-1/PlGF ratio. Analysis of plasma from the at-delivery cohort confirmed the ability of this biomarker panel to distinguish PE from non-PE, with comparable discrimination power to that of the sFlt-1/PlGF ratio. In conclusion, an integrative metabolomic biomarker panel in mid-trimester maternal plasma can accurately predict the development of PE and showed good discriminatory power in patients with PE at delivery.

“…Missing values replacement was performed based on the BinBase algorithm, which processes raw data for post-matching and replacement 38 . For quality control purposes, every tenth sample of a mixture of 33 pure reference compounds was analyzed, and this ensured reproducible performance during the analysis 39 .…”

Section: Liver Resection and Blood Sampling Liver Resection Was Perfmentioning

confidence: 99%

Explorative study of serum biomarkers of liver failure after liver resection

Yoon

Kwon

et al. 2020

Sci Rep

Self Cite

conventional biochemical markers have limited usefulness in the prediction of early liver dysfunction. We, therefore, tried to find more useful liver failure biomarkers after liver resection that are highly sensitive to internal and external challenges in the biological system with a focus on liver metabolites. Twenty pigs were divided into the following 3 groups: sham operation group (n = 6), 70% hepatectomy group (n = 7) as a safety margin of resection model, and 90% hepatectomy group (n = 7) as a liver failure model. Blood sampling was performed preoperatively and at 1, 6, 14, 30, 38, and 48 hours after surgery, and 129 primary metabolites were profiled. Orthogonal projection to latent structures-discriminant analysis revealed that, unlike in the 70% hepatectomy and sham operation groups, central carbon metabolism was the most significant factor in the 90% hepatectomy group. Binary logistic regression analysis was used to develop a predictive model for mortality risk following hepatectomy. the recommended variables were malic acid, methionine, tryptophan, glucose, and γ-aminobutyric acid. Area under the curve of the linear combination of five metabolites was 0.993 (95% confidence interval: 0.927-1.000, sensitivity: 100.0, specificity: 94.87). We proposed robust biomarker panels that can accurately predict mortality risk associated with hepatectomy. Liver resection is inevitable in the treatment of specific liver diseases. However, reduced liver volume can have many clinical manifestations such as small-for-size syndrome and post-hepatectomy liver failure, especially in cases of large volume resections of the liver. One of the main reasons for deteriorating liver function is high portal pressure, which results in high shear stress and sinusoidal injury 1. Conventional biochemical parameters for the evaluation of liver function in clinical settings, such as total bilirubin and prothrombin time (PT), are used as predictive markers. However, these biomarkers have limited usefulness in the prediction of early dysfunction. Total bilirubin and PT only have a 59% sensitivity in the prediction of postoperative mortality 2. Therefore, there is a need for more specific and accurate predictive markers. Metabolites are the final readouts of a range of endogenous biochemical activities. Changes in the metabolic profile are highly sensitive to internal and external challenges in biological systems. Thus, comprehensive metabolome information has great value in the diagnosis and prognosis of various diseases. In this study, we used liver failure animal models to determine changes in the levels of serum metabolites that can be identified before conventional biochemical markers. Two pig models were used to investigate the regenerating or failing liver after liver resection. Resection of 70% of the liver provided a model of liver injuries that did not lead to liver failure and served as a successful regenerative model, and resection of 90% of the liver provided a model of liver failure that eventually led to death. We identified ...