The molecular profiling of EVs is represents a promising approach for identifying disease-specific biomarkers and developing diagnostic tests for early detection. The clinical translation of EV-based diagnostics into routine clinical practice is hindered by the lack of efficient, reproducible and high-throughput EV isolation and downstream analysis method that can be easily integrated into clinical laboratory workflows. In this study, we used a high-throughput automated bead-based immunoaffinity system (EXO-NET®) to isolate EVs and identify differentially expressed miRNAs from serum samples obtained from women diagnosed with breast cancer to identify EV-associated biomarkers for earlier detection of breast to improve disease prognosis. EVs were isolated from 500 µl of serum obtained from women diagnosed with breast cancer (I, II, III, IV n=12 per stage) and age matched normal healthy women (n=48) using EXO-NET on high-throughput automated KingFisher Apex system. Small RNAs were isolated on the same system using Promega Maxwell® HT miRNA Plasma and Serum kit for small RNA sequencing and RT-PCR analysis. High-throughput EVs isolation and their associated miRNAs from 96 serum samples was conducted in less than 2 hours. Small RNA seq analysis identified top 50 significantly differentially expressed miRNAs (log2 =2, p < 0.01) between normal healthy women and women diagnosed with breast cancer. Data modelling including GO and KEGG pathway analysis identified molecular pathways associated with breast cancer and EV compartments. We have established an efficient, reproducible and fit-for-purpose high-throughput automated system for the isolation of EVs and the analysis of associated RNAs and proteins. This system shows great potential for facilitating the translation of EV diagnostics into routine clinical practice. Ramin Khanabdali, Scott Zhu, Mathew Moore, Eric Vincent, Gregory Rice. Automated high-throughput isolation of extracellular vesicles (EVs) and small RNA sequencing profile in serum of breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB258.

The aim of this study was to test the hypothesis that the diagnostic performance (as defined by the classification accuracy, sensitivity and specificity) of CA15-3 to detect breast cancer is increased by detection of the cancer-associated Neu5Gc sialic acids on the mucin MUC1 using the novel lectin, SubB2M. A genetically engineered lectin (SubB2M) that binds Neu5Gc was used as a detection reagent in a sandwich ELISA format CA15-3 assay. In a case : control cohort equivalence study the classification accuracy for the SubB2M-based CA15-3 assay (neuCA15-3) was determined and compared to an FDA-approved CA15-3 IVD test (Elecsys CA15-3 II, Roche Diagnostics). Classification accuracy and AUC for neuCA15-3 were 81% and 0.886 ± 0.015 (standard error, n=567) and for Elecsys Ca15-3 II, 55% and 0.642 ± 0.023 (n=558), respectively. At a threshold cut-off serum concentration of 23.6 Units/ml, overall breast cancer classification accuracy of the neuCA15-3 was 81% (compared to 55% for the comparator assay, p < 0.001). At 95% specificity, the sensitivity of the NeuCA15-3 assay was 69.5%, significantly greater than the competitor assay (11.9%, p<0.001). NeuCA15-3 concentrations did not vary significantly with breast cancer receptor subtype. The diagnostic performance of the neuCA15-3 assay was substantially improved over the classical CA15-3 sandwich assay that detects all forms of MUC1. The reporter signal generated for the neuCA15-3 assay depends on capture of the MUC1 protein and the presence of the aberrant Neu5Gc sialic acid residues on the MUC1 protein, thus increasing the assay specificity. The presence of multiple Neu5Gc lectin binding sites per glycoprotein molecule increases signal generation and assay sensitivity. The inclusion of additional cancer biomarkers in a multivariate index assay format may further increase diagnostic performance for breast cancer. Sara Nikseresht, Ramin Khanabdali, Lucy Shewell, Christopher Day, Amy McCart Reed, Michael Jennings, Haarika Haarika, Sunil Sunil, Peter Simpson, Romina Nabiee, Mathew Moore, Leearne Hinch, Gregory Rice. Enhancing sensitivity and specificity of the CA15-3 (MUC1) breast cancer assay by detection of N-glycolylneuraminic acid (Neu5Gc) using the lectin SubB2M [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB407.

5582 Background: The high mortality of Ovarian cancer (OC) has been attributed to late-stage diagnosis and the lack of an effective early detection strategy, particularly for asymptomatic women. In this study, we developed and validated a high-throughput OC detection test based on plasma extracellular vesicle (EV)-associated biomarkers. Methods: A case-control study was conducted to evaluate blood-borne EV-associated ovarian cancer biomarkers, including miRNAs, proteins, lncRNAs, miscRNAs, MtrRNAs, MttRNAs, rRNAs, scaRNAs, snRNAs, and tRNAs. Protein and RNA biomarkers were identified by mass spectrometry and RNA sequencing, respectively. Training (n=453) and independent test (n=471) sample sets were used to develop and validate a multivariate index assay (MIA). The MIA was further validated using a high-throughput, pathology laboratory compatible, EV isolation platform (EXO-NET) and two independent sample cohorts (n=97 and n=532). The classification accuracy, sensitivity and specificity of the MIA was compared to that of CA125 levels. Results: Discovery and Training phases - more than 100,000 EV-associated biomarkers were identified from 453 EV samples. The classification performance of these biomarkers was assessed using machine learning algorithms. EV-associated protein and miRNA biomarkers delivered the highest performing classifiers and, therefore, were used in subsequent MIA development and training. During the training phase, multivariate classification algorithms were validated using a 10-fold cross-validation method. The highest performing classifiers for EV-associated protein and miRNA, at specificity of 98%, achieved sensitivities of 90% and 82%, respectively. Validation phase: Locked classification algorithms ( i.e. MIAs) were validated using two independent sample cohorts and reported classification accuracies of 92-98%, significantly outperforming CA-125 (CE = 62%, p<0.001). Automated high-throughput MIA – All stages OC: the best performing automated high-throughput MIA demonstrated an overall sensitivity of 92% (95% CI, 75–96%) and specificity of 93% (95% CI, 86–96%) for all stages of OC, Positive Predictive Value of 95% (CI, 93-96%) and Negative Predictive Value of 80% (CI, 76-89%) at 98% specificity (n=532). Stage I OC: Importantly, the MIA displayed a sensitivity of 90% (95% CI, 76–100%) and specificity of 96% (95% CI, 40%–99%) for stage I OC. While CA125 have an overall sensitivity for all stages of OC of 61% (95% CI, 53–69%), with a sensitivity of 44% for stage I (95% CI, 28–62%). Conclusions: In this study we report the development and validation of an accurate, automated high-throughput EV-based test for early detection of ovarian cancer. The test delivers significant improvements in sensitivity and specificity compared to CA-125, especially in detecting early-stage OC.

Gestational diabetes mellitus (GDM) affects 2–20

The field of extracellular vesicle (EV) signalling has the potential to transform our understanding of maternal-fetal communication and affords new opportunities for non-invasive prenatal testing and therapeutic intervention. EVs have been implicated in implantation, placentation, maternal adaptation to pregnancy and complications of pregnancy, being detectable in maternal circulation as early as 6 weeks of pregnancy. EVs of differing biogenic origin, composition and bioactivity are released by cells to maintain homoeostasis. Induction of EV signalling is associated with aberrant cellular metabolism and manifests as changes in EV concentrations and/or composition. Characterizing such changes affords opportunity to develop more informative diagnostics and efficacious interventions. To develop accurate and reliable EV-based diagnostics requires: identification of disease-associated biomarkers in specific EV subpopulations; and rapid, reproducible and scalable sample processing. Conventional isolation methods face challenges due to co-isolation of particles with similar physicochemical properties. Methods targeting specific vesicle-surface epitopes and compatible with automated platforms show promise. Effective EV therapeutics require precise targeting, achieved through genetic engineering to release EVs expressing cell-targeting ligands and carrying therapeutic payloads. Unlike cell-based therapies, this approach offers advantages including: low immunogenicity; stability; and long-term storage. Although EV diagnostics and therapeutics in reproductive biology are nascent, available technologies can enhance our understanding of EV signalling between mother and fetus, its role in pregnancies and improve outcomes.

Extracellular vesicles (EVs), including exosomes, have significant potential for diagnostic and therapeutic applications. The lack of standardized methods for efficient and high-throughput isolation and analysis of EVs, however, has limited their widespread use in clinical practice. Surface epitope immunoaffinity (SEI) isolation utilizes affinity ligands, including antibodies, aptamers, or lectins, that target specific surface proteins present on EVs. Paramagnetic bead-SEI isolation represents a fit-for-purpose solution for the reproducible, high-throughput isolation of EVs from biofluids and downstream analysis of RNA, protein, and lipid biomarkers that is compatible with clinical laboratory workflows. This study evaluates a new SEI isolation method for enriching subpopulations of EVs. EVs were isolated from human plasma using a bead-based SEI method designed for on-bead and downstream analysis of EV-associated RNA and protein biomarkers. Western blot analysis confirmed the presence of EV markers in the captured nanoparticles. Mass spectrometry analysis of the SEI lysate identified over 1500 proteins, with the top 100 including known EV-associated proteins. microRNA (miRNA) sequencing followed by RT-qPCR analysis identified EV-associated miRNA transcripts. Using SEI, EVs were isolated using automated high-throughput particle moving instruments, demonstrating equal or higher protein and miRNA yield and recovery compared to manual processing. SEI is a rapid, efficient, and high-throughput method for isolating enriched populations of EVs; effectively reducing contamination and enabling the isolation of a specific subpopulation of EVs. In this study, high-throughput EV isolation and RNA extraction have been successfully implemented. This technology holds great promise for advancing the field of EV research and facilitating their application for biomarker discovery and clinical research.

Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care. Machine learning (ML) techniques are invaluable for recognising complex patterns in biomarkers compared to conventional methods, yet they can lack physical insights into diagnosis. eXplainable Artificial Intelligence (XAI) is capable of providing deeper insights into the decision-making of complex ML algorithms increasing their applicability. We aim to introduce best practice for combining ML and XAI techniques for biomarker validation tasks. We focused on classification tasks and a game theoretic approach based on Shapley values to build and evaluate models and visualise results. We described the workflow and apply the pipeline in a case study using the CDAS PLCO Ovarian Biomarkers dataset to demonstrate the potential for accuracy and utility. The case study results demonstrate the efficacy of the ML pipeline, its consistency, and advantages compared to conventional statistical approaches. The resulting guidelines provide a general framework for practical application of XAI in medical research that can inform clinicians and validate and explain cancer biomarkers. Display Omitted • Machine learning and Shapley analysis are suitable for biomarker discovery tasks. • Pipeline achieves consistent result with statistical analysis on feature contribution. • Shapley analysis provides opportunities for accountability via model explainability. • Various challenges in this area persist and need further research.

Aim: Saliva extracellular vesicles (EVs) serve as a significant reservoir of biomarkers that may be of clinical use in disease diagnosis. Saliva, however, contains EVs of both host- and bacterial- origin. Identifying suitable EVs for disease diagnosis involves enriching host EVs and limiting non-host contamination with effective isolation methods. The objectives of this research were: (1) to evaluate the salivary EVs enrichment in 12 periodontally healthy patients by two different methods: size exclusion chromatography (SEC) and bead-based immunoaffinity capture (EXO-NET®); (2) to analyze the variance expression of inflammatory cytokines in EXO-NET-enriched EVs, comparing individuals with periodontitis (n = 20) to non-periodontitis (n = 12). Methods: Whole unstimulated saliva samples were collected from 12 periodontally healthy and 20 periodontitis patients. EVs were isolated from the 12 non-periodontitis patients using SEC (referred to as SEC-EVs) and EXO-NET (referred to as EXO-NET EVs), after which their total protein content, 37 EV surface markers, and bacterial pathogens expression were compared. Subsequently, the inflammatory cytokines expression levels (interleukin-IL-6, IL-1β, IL-8, and IL-10) in EXO-NET EVs were measured for non-periodontitis and periodontitis. Results: EXO-NET EVs contained more EV-specific protein and substantially higher expression of EV surface markers (CD9, CD81, CD63), but less pathogenic DNA was detected compared to that in SEC-EVs. Additionally, EXO-NET EVs from periodontitis patients contained higher amounts of IL-6 and IL-8, and decreased IL-10, compared to those from non-periodontitis patients. Conclusion: The findings suggest that immunoaffinity capture (EXO-NET) is a dependable method for salivary EVs enrichment, resulting in a higher yield of host EVs with reduced bacterial DNA detection compared to SEC. Furthermore, the research proposes that immunoaffinity capture enriched EVs can function as biomarkers for periodontitis, demonstrated by an increased expression of proinflammatory cytokines from periodontitis patients.

OBJECTIVE:Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care. Machine learning (ML) techniques are invaluable for recognising complex patterns in biomarkers compared to conventional methods, yet they can lack physical insights into diagnosis. eXplainable Artificial Intelligence (XAI) is capable of providing deeper insights into the decision-making of complex ML algorithms increasing their applicability. We aim to introduce best practice for combining ML and XAI techniques for biomarker validation tasks.METHODS:We focused on classification tasks and a game theoretic approach based on Shapley values to build and evaluate models and visualise results. We described the workflow and apply the pipeline in a case study using the CDAS PLCO Ovarian Biomarkers dataset to demonstrate the potential for accuracy and utility.RESULTS:The case study results demonstrate the efficacy of the ML pipeline, its consistency, and advantages compared to conventional statistical approaches.CONCLUSION:The resulting guidelines provide a general framework for practical application of XAI in medical research that can inform clinicians and validate and explain cancer biomarkers.

During the last decade, there has been great interest in elucidating the biological role of extracellular vesicles (EVs), particularly, their hormone-like role in cell-to-cell communication. The field of endocrinology is uniquely placed to provide insight into the functions of EVs, which are secreted from all cells into biological fluids and carry endocrine signals to engage in paracellular and distal interactions. EVs are a heterogeneous population of membrane-bound vesicles of varying size, content, and bioactivity. EVs are specifically packaged with signaling molecules, including lipids, proteins, and nucleic acids, and are released via exocytosis into biofluid compartments. EVs regulate the activity of both proximal and distal target cells, including translational activity, metabolism, growth, and development. As such, EVs signaling represents an integral pathway mediating intercellular communication. Moreover, as the content of EVs is cell-type specific, it is a "fingerprint" of the releasing cell and its metabolic status. Recently, changes in the profile of EV and bioactivity have been described in several endocrine-related conditions including diabetes, obesity, cardiovascular diseases, and cancer. The goal of this statement is to highlight relevant aspects of EV research and their potential role in the field of endocrinology.

Early and innovative diagnostic strategies are required to predict the risk of developing pre-eclampsia (PE). The purpose of this study was to evaluate the performance of gingival crevicular fluid (GCF) placental alkaline phosphatase (PLAP) concentrations to correctly classify women at risk of PE. A prospectively collected, retrospectively stratified cohort study was conducted, with 412 pregnant women recruited at 11–14 weeks of gestation. Physical, obstetrical, and periodontal data were recorded. GCF and blood samples were collected for PLAP determination by ELISA assay. A multiple logistic regression classification model was developed, and the classification efficiency of the model was established. Within the study cohort, 4.3% of pregnancies developed PE. GCF-PLAP concentration was 3- to 6-fold higher than in plasma samples. GCF-PLAP concentrations and systolic blood pressure were greater in women who developed PE (p = 0.015 and p < 0.001, respectively). The performance of the multiparametric model that combines GCF-PLAP concentration and the levels of systolic blood pressure (at 11–14 weeks gestation) showed an association of systolic blood pressure and GCF-PLAP concentrations with the likelihood of developing PE (OR:1.07; 95% CI 1.01–1.11; p = 0.004 and OR:1.008, 95% CI 1.000–1.015; p = 0.034, respectively). The model had a sensitivity of 83%, a specificity of 72%, and positive and negative predictive values of 12% and 99%, respectively. The area under the receiver operating characteristic (AUC-ROC) curve was 0.77 and correctly classified 72% of PE pregnancies. In conclusion, the multivariate classification model developed may be of utility as an aid in identifying pre-symptomatic women who subsequently develop PE.

Exposure to an adverse prenatal environment can influence fetal development and result in long-lasting changes in the offspring. However, the association between maternal exposure to stressful events during pregnancy and the achievement of pre-reading skills in the offspring is unknown. Here we examined the association between prenatal exposure to the Chilean high-magnitude earthquake that occurred on February 27th, 2010 and the development of early reading precursors skills (listening comprehension, print knowledge, alphabet knowledge, vocabulary, and phonological awareness) in children at kindergarten age. This multilevel retrospective cohort study including 3280 children, of whom 2415 were unexposed and 865 were prenatally exposed to the earthquake shows substantial evidence that maternal exposure to an unambiguously stressful event resulted in impaired pre-reading skills and that a higher detrimental effect was observed in those children who had been exposed to the earthquake during the first trimester of gestation. In addition, females were more significantly affected by the exposure to the earthquake than their male peers in alphabet knowledge; contrarily, males were more affected than females in print knowledge skills. These findings suggest that early intervention programs for pregnant women and/or children exposed to prenatal stress may be effective strategies to overcome impaired pre-reading skills in children.

Proteomics has gone through tremendous development during recent decades [...].

Objectives: Dandy-Walker malformation (DWM) is a common cerebellar malformation characterized by vermian hypoplasia with upward rotation, cystic dilatation of the fourth ventricle and elevated torcular.DWM can be diagnosed prenatally by fetal sonogram and/or magnetic resonance imaging (MRI).However, its associated neurodevelopmental abnormalities are variable and difficult to predict in prenatal counseling.We hypothesized that DWM have abnormalities beyond cerebellum and the posterior fossa, which may potentially impact neurodevelopmental variations.Methods: We retrospectively reviewed medical record and fetal MRIs of 15 fetuses with DWM.We performed post-acquisition regional volumetric fetal MRI analysis in 11 fetuses with DWM and 12 control fetuses to measure volumes of cortical plate, subcortical parenchyma, cerebellar hemispheres, and vermis.Growth trajectories between 18 and 33 weeks' gestation in each structure were modeled for each group.A logarithmic transform of the volume data was used before fitting the linear regression model.Intergroup measures were compared using ANCOVA. Results:The median (interquartile range) gestational ages of fetal MRI studies in 11 fetuses with DWM and 12 controls were 22.6 (4.3) weeks and 25.1 (9.2) weeks, respectively (p value = 0.31).

Objective: To evaluate the accuracy of concentrations of gingival crevicular fluid (GCF) placental alkaline phosphatase (PLAP) during early pregnancy in identifying women at risk of subsequently developing preeclampsia (PE). Design: Prospective cohort study. Setting: Hospital Sotero del Rio, Santiago, Chile. Population: Pregnant women recruited at 11-14 weeks of gestation. Methods: Maternal obstetric and periodontal histories were obtained. GCF samples were collected for PLAP determination by ELISA assay. Multiple logistic regression models estimated the association between GCF-PLAP concentrations, maternal variables, and PE development. The accuracy performance of the prediction model was established. Results: 460 women were recruited into the study, and 412 completed their pregnancy follow–up visit. 18 (4.3%) women developed PE. GCF-PLAP concentrations and systolic blood pressure measurements were significantly higher in women who developed PE (p=0.015 and p<0.001, respectively). An association between first-trimester systolic blood pressure, GCF-PLAP, and PE were established. The predictive model had a sensitivity of 83%, specificity of 72%, a positive predictive value (PPV) of 12%, and a negative predictive value (NPV) of 99%. The positive and negative likelihood ratios were 2.9 and 0.3, respectively, thus classifying correctly 72% of women who subsequently developed PE. The area under the receiver operating characteristic curve was 0.77 for PE and 0.85 for preterm PE. Conclusions: An algorithm that includes PLAP concentrations in GCF and blood pressure during early pregnancy may aid in the identification of women at risk of developing PE. Funding: FONDEF IDeA: ID16I10452. NICHD/NIH/DHHS: HHSN275201300006C. Keywords: a cohort study, gestation, hypertension, placental biomarkers, risk prediction model.

Objective Amniotic fluid cytokines have been implicated in the mechanisms of preterm labor and birth. Cytokines can be packaged within or on the surface of extracellular vesicles. The main aim of this study was to test whether the protein abundance internal to and on the surface of extracellular vesicles changes in the presence of sterile intra-amniotic inflammation and proven intra-amniotic infection in women with preterm labor as compared to the women with preterm labor without either intra-amniotic inflammation or proven intra-amniotic infection. Study design Women who had an episode of preterm labor and underwent an amniocentesis for the diagnosis of intra-amniotic infection or intra-amniotic inflammation were classified into three groups: 1) preterm labor without either intra-amniotic inflammation or proven intra-amniotic infection, 2) preterm labor with sterile intra-amniotic inflammation, and 3) preterm labor with intra-amniotic infection. The concentrations of 38 proteins were determined on the extracellular vesicle surface, within the vesicles, and in the soluble fraction of amniotic fluid. Results 1) Intra-amniotic inflammation, regardless of detected microbes, was associated with an increased abundance of amniotic fluid cytokines on the extracellular vesicle surface, within vesicles, and in the soluble fraction. These changes were most prominent in women with proven intra-amniotic infection. 2) Cytokine changes on the surface of extracellular vesicles were correlated with those determined in the soluble fraction; yet the magnitude of the increase was significantly different between these compartments. 3) The performance of prediction models of early preterm delivery based on measurements on the extracellular vesicle surface was equivalent to those based on the soluble fraction. Conclusions Differential packaging of amniotic fluid cytokines in extracellular vesicles during preterm labor with sterile intra-amniotic inflammation or proven intra-amniotic infection is reported herein for the first time. The current study provides insights into the biology of the intra-amniotic fluid ad may aid in the development of biomarkers for obstetrical disease.

Small extracellular vesicles (sEVs) released from the extravillous trophoblast (EVT) are known to regulate uterine spiral artery remodeling during early pregnancy. The bioactivity and release of these sEVs differ under differing oxygen tensions and in aberrant pregnancy conditions. Whether the placental cell-derived sEVs released from the hypoxic placenta contribute to the pathophysiology of preeclampsia is not known. We hypothesize that, in response to low oxygen tension, the EVT packages a specific set of proteins in sEVs and that these released sEVs interact with endothelial cells to induce inflammation and increase maternal systemic blood pressure. Using a quantitative MS/MS approach, we identified 507 differentially abundant proteins within sEVs isolated from HTR-8/SVneo cells (a commonly used EVT model) cultured at 1% (hypoxia) compared with 8% (normoxia) oxygen. Among these differentially abundant proteins, 206 were up-regulated and 301 were down-regulated (P < 0.05), and they were mainly implicated in inflammation-related pathways. In vitro incubation of hypoxic sEVs with endothelial cells, significantly increased (P < 0.05) the release of GM-CSF, IL-6, IL-8, and VEGF, when compared with control (i.e. cells without sEVs) and normoxic sEVs. In vivo injection of hypoxic sEVs into pregnant rats significantly increased (P < 0.05) mean arterial pressure with increases in systolic and diastolic blood pressures. We propose that oxygen tension regulates the release and bioactivity of sEVs from EVT and that these sEVs regulate inflammation and maternal systemic blood pressure. This novel oxygen-responsive, sEVs signaling pathway, therefore, may contribute to the physiopathology of preeclampsia.

BACKGROUND Gestational diabetes mellitus (GDM) is increasing worldwide and women with a history of GDM are at risk of developing type 2 diabetes which is a risk factor for periodontitis. AIM To explore the association between the concentrations of matrix metalloproteinase (MMP)-8 and -9 in gingival crevicular fluid (GCF) during early pregnancy with the periodontal diagnosis and the risk of GDM development. MATERIALS AND METHODS A prospective cohort study, including 314 women, enrolled at 11-14 weeks of pregnancy was conducted. A complete maternal/obstetric and periodontal exam was performed, and GCF samples were obtained for the MMP-8 and -9 determination by Multiplex Elisa Assays. Mann-Whitney test; Spearman's correlation and log-binomial regression model estimated the association between MMPs concentration in GCF and GDM. RESULTS 14% of the pregnancies were diagnosed with GDM. An increase in the concentration of MMP-8 and -9 in women with periodontitis stage III and IV compared to periodontitis stage I was observed (99.31 ng/ml [IQR: 85.32] v/s 71.95 ng/ml [IQR: 54.04], and 262.4 ng/ml [IQR: 312.55] v/s 114.1 ng/ml [IQR: 184.94], respectively). Women who developed GDM showed increased concentrations of MMP-8 and -9 in GCF since the beginning of pregnancy (p = 0.0381; p = 0.0302, respectively). MMP-8 concentration in GCF was associated with GDM (RR: 1.19; p = 0.045; CI 95% 1.00-1.40; and RR: 1.20; p = 0.063; CI 95% 0.99-1.45 in the adjusted model). CONCLUSION(S) GCF concentrations of MMP-8 and -9 at early of pregnancy are increased in women with severe periodontitis and associated with the GDM development. This article is protected by copyright. All rights reserved.

Spontaneous abortion is a common complication in early pregnancy, with an incidence of around 20%. Ultrasound scan and measurement of human chorionic gonadotropin are used to identify patients at risk of spontaneous abortion; however, there is a clinical need to find new biomarkers to prospectively identify patients before the onset of clinical symptoms. Here, we aim to investigate potential biomarkers of spontaneous abortion taken in the first clinical appointment of pregnancy. A case–control study was conducted based on a prospectively collected cohort in which cases and controls were retrospectively stratified based on pregnancy outcome: normal healthy pregnancies (controls = 33) and pregnancies that ended in spontaneous abortion (cases = 10). We evaluated extracellular vesicles isolated by precipitation with ExoQuick™ and protein concentrations of tissue plasminogen activator, leptin, and adiponectin measured by ELISA. The extracellular vesicles showed the typical morphology and membrane proteins: CD63, Alix, and Flotilin-1. The size distributions of the isolated extracellular vesicles were 112 ± 27 and 118 ± 28 nm in diameter for controls and spontaneous abortion, respectively, and the total amount of extracellular vesicles did not show any difference between controls and the spontaneous abortion group. The tissue plasminogen activator showed a significant difference (p = 0.0004) between both groups, although neither adiponectin nor leptin revealed significant changes, indicating that women who had spontaneous abortions have significantly higher levels of tissue plasminogen activator than women who had normal pregnancies.