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Emerging Protein Biomarkers for the Diagnosis or Prediction of Gestational Diabetes-A Scoping Review.

ABSTRACT: Introduction: Gestational diabetes (GDM), defined as hyperglycemia with onset or initial recognition during pregnancy, has a rising prevalence paralleling the rise in type 2 diabetes (T2DM) and obesity. GDM is associated with short-term and long-term consequences for both mother and child. Therefore, it is crucial we efficiently identify all cases and initiate early treatment, reducing fetal exposure to hyperglycemia and reducing GDM-related adverse pregnancy outcomes. For this reason, GDM screening is recommended as part of routine pregnancy care. The current screening method, the oral glucose tolerance test (OGTT), is a lengthy, cumbersome and inconvenient test with poor reproducibility. Newer biomarkers that do not necessitate a fasting sample are needed for the prompt diagnosis of GDM. The aim of this scoping review is to highlight and describe emerging protein biomarkers that fulfill these requirements for the diagnosis of GDM. Materials and Methods: This scoping review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for scoping reviews using Cochrane Central Register of Controlled Trials (CENTRAL), the Cumulative Index to Nursing & Allied Health Literature (CINAHL), PubMed, Embase and Web of Science with a double screening and extraction process. The search included all articles published in the literature to July 2020. Results: Of the 3519 original database citations identified, 385 were eligible for full-text review. Of these, 332 (86.2%) were included in the scoping review providing a total of 589 biomarkers studied in relation to GDM diagnosis. Given the high number of biomarkers identified, three post hoc criteria were introduced to reduce the items set for discussion: we chose only protein biomarkers with at least five citations in the articles identified by our search and published in the years 2017-2020. When applied, these criteria identified a total of 15 biomarkers, which went forward for review and discussion. Conclusions: This review details protein biomarkers that have been studied to find a suitable test for GDM diagnosis with the potential to replace the OGTT used in current GDM screening protocols. Ongoing research efforts will continue to identify more accurate and practical biomarkers to take GDM screening and diagnosis into the 21st century.

SUBMITTER: Bogdanet D

PROVIDER: S-EPMC8038821 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundExcessive gestational weight gain (GWG) and gestational diabetes mellitus (GDM) are common pregnancy complications that have been shown to be preventable through the use of lifestyle interventions. However, a significant gap exists between research on pregnancy lifestyle interventions and translation into clinical practice. App-supported interventions might aid in overcoming previous implementation barriers. The current status in this emerging research area is unknown.ObjectiveThis scoping review aims to provide a comprehensive overview of planned, ongoing, and completed studies on eHealth and mobile health (mHealth) app-supported lifestyle interventions in pregnancy to manage GWG and prevent GDM. The review assesses the scope of the literature in the field; describes the population, intervention, control, outcomes, and study design (PICOS) characteristics of included studies as well as the findings on GWG and GDM outcomes; and examines app functionalities.MethodsThe scoping review was conducted according to a preregistered protocol and followed established frameworks. Four electronic databases and 2 clinical trial registers were systematically searched. All randomized and quasi-randomized controlled trials (RCTs) of app-supported lifestyle interventions in pregnancy and related qualitative and quantitative research across the different study phases were considered for inclusion. Eligible studies and reports of studies were included until June 2022. Extracted data were compiled in descriptive analyses and reported in narrative, tabular, and graphical formats.ResultsThis review included 97 reports from 43 lifestyle intervention studies. The number of published reports has steadily increased in recent years; of the 97 included reports, 38 (39%) were trial register entries. Of the 39 identified RCTs, 10 efficacy or effectiveness trials and 8 pilot trials had published results on GWG (18/39, 46%); of these 18 trials, 7 (39%) trials observed significant intervention effects on GWG outcomes. Of all 39 RCTs, 5 (13%) efficacy or effectiveness trials reported GDM results, but none observed significant intervention effects on GDM. The RCTs included in the review were heterogeneous in terms of their PICOS characteristics. Most of the RCTs were conducted in high-income countries, included women with overweight or obesity and from all BMI categories, delivered multicomponent interventions, delivered interventions during pregnancy only, and focused on diet and physical activity. The apps used in the studies were mostly mHealth apps that included features for self-monitoring, feedback, goal setting, prompts, and educational content. Self-monitoring was often supported by wearable activity monitors and Bluetooth-connected weight scales.ConclusionsResearch in this field is nascent, and the effectiveness and implementability of app-supported interventions have yet to be determined. The complexity and heterogeneity of intervention approaches pose challenges in identifying the most beneficial app features and intervention components and call for consistent and comprehensive intervention and outcome reporting.

Project description:BackgroundAt present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS).MethodsWe systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans.ResultsWe identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans 2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs.ConclusionsQuantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

Project description:IntroductionGestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M.MethodsPlasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model.ResultsWe found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR < 0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P Hosmer-Lemeshow test = 0.897).ConclusionThe Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

Project description:BACKGROUND:Gestational diabetes mellitus (GDM) - a transitory form of diabetes induced by pregnancy - has potentially important short and long-term health consequences for both the mother and her baby. There is no globally agreed definition of GDM, but definition changes have increased the incidence in some countries in recent years, with some research suggesting minimal clinical improvement in outcomes. The aim of this qualitative systematic review was to identify the psychosocial experiences a diagnosis of GDM has on women during pregnancy and the postpartum period. METHODS:We searched CINAHL, EMBASE, MEDLINE and PsycINFO databases for studies that provided qualitative data on the psychosocial experiences of a diagnosis of GDM on women across any stage of pregnancy and/or the postpartum period. We appraised the methodological quality of the included studies using the Critical Appraisal Skills Programme Checklist for Qualitative Studies and used thematic analysis to synthesis the data. RESULTS:Of 840 studies identified, 41 studies of diverse populations met the selection criteria. The synthesis revealed eight key themes: initial psychological impact; communicating the diagnosis; knowledge of GDM; risk perception; management of GDM; burden of GDM; social support; and gaining control. The identified benefits of a GDM diagnosis were largely behavioural and included an opportunity to make healthy eating changes. The identified harms were emotional, financial and cultural. Women commented about the added responsibility (eating regimens, appointments), financial constraints (expensive food, medical bills) and conflicts with their cultural practices (alternative eating, lack of information about traditional food). Some women reported living in fear of risking the health of their baby and conducted extreme behaviours such as purging and starving themselves. CONCLUSION:A diagnosis of GDM has wide reaching consequences that are common to a diverse group of women. Threshold cut-offs for blood glucose levels have been determined using the risk of physiological harms to mother and baby. It may also be advantageous to consider the harms and benefits from a psychosocial and a physiological perspective. This may avoid unnecessary burden to an already vulnerable population.

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Emerging Protein Biomarkers for the Diagnosis or Prediction of Gestational Diabetes-A Scoping Review.

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