Search

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Introduction Small for gestational age (SGA) may be associated with neonatal morbidity and mortality. Our understanding of the molecular pathways implicated is poor. Objectives Our aim was to determine the metabolic pathways involved in the pathophysiology of SGA and examine their variation between maternal biofluid samples. Methods Plasma (Cork) and urine (Cork, Auckland) samples were collected at 20 weeks’ gestation from nulliparous low-risk pregnant women participating in the SCOPE study. Women who delivered an SGA infant (birthweight Results Lipidomics plasma analysis revealed that 22 out of the 33 significantly altered lipids annotated were glycerophospholipids; all were detected in higher levels in SGA. Metabolomic analysis identified reduced expression of metabolites associated with detoxification (D-Glucuronic acid, Estriol-16-glucuronide), nutrient absorption and transport (Sulfolithocholic acid) pathways. Conclusions This study suggests higher levels of glycerophospholipids, and lower levels of specific urine metabolites are implicated in the pathophysiology of SGA. Further research is needed to confirm these findings in independent samples.

With less than half of patients with major depressive disorder (MDD) correctly diagnosed within the primary care setting, there is a clinical need to develop an objective and readily accessible test to enable earlier and more accurate diagnosis. The aim of this study was to develop diagnostic prediction models to identify MDD patients among individuals presenting with subclinical low mood, based on data from dried blood spot (DBS) proteomics (194 peptides representing 115 proteins) and a novel digital mental health assessment (102 sociodemographic, clinical and personality characteristics). To this end, we investigated 130 low mood controls, 53 currently depressed individuals with an existing MDD diagnosis (established current MDD), 40 currently depressed individuals with a new MDD diagnosis (new current MDD), and 72 currently not depressed individuals with an existing MDD diagnosis (established non-current MDD). A repeated nested cross-validation approach was used to evaluate variation in model selection and ensure model reproducibility. Prediction models that were trained to differentiate between established current MDD patients and low mood controls (AUC = 0.94 ± 0.01) demonstrated a good predictive performance when extrapolated to differentiate between new current MDD patients and low mood controls (AUC = 0.80 ± 0.01), as well as between established non-current MDD patients and low mood controls (AUC = 0.79 ± 0.01). Importantly, we identified DBS proteins A1AG1, A2GL, AL1A1, APOE and CFAH as important predictors of MDD, indicative of immune system dysregulation; as well as poor self-rated mental health, BMI, reduced daily experiences of positive emotions, and tender-mindedness. Despite the need for further validation, our preliminary findings demonstrate the potential of such prediction models to be used as a diagnostic aid for detecting MDD in clinical practice.

Introduction Preterm birth (PTB) is defined as birth occurring before 37 weeks’ gestation, affects 5–9% of all pregnancies in developed countries, and is the leading cause of perinatal mortality. Spontaneous preterm birth (sPTB) accounts for 31–50% of all PTB, but the underlying pathophysiology is poorly understood. Objective This study aimed to decipher the lipidomics pathways involved in pathophysiology of sPTB. Methods Blood samples were taken from SCreening fOr Pregnancy Endpoints (SCOPE), an international study that recruited 5628 nulliparous women, with a singleton low-risk pregnancy. Our analysis focused on plasma from SCOPE in Cork. Discovery profiling of the samples was undertaken using liquid chromatography-mass spectrometry Lipidomics, and features significantly altered between sPTB (n = 16) and Control (n = 32) groups were identified using empirical Bayes testing, adjusting for multiple comparisons. Results Twenty-six lipids showed lower levels in plasma of sPTB compared to controls (adjusted p Conclusions We report reduced levels of plasma phospholipids in sPTB. Phospholipid integrity is linked to biological membrane stability and inflammation, while storage and breakdown of lipids have previously been implicated in pregnancy complications. The contribution of phospholipids to sPTB as a cause or effect is still unclear; however, our results of differential plasma phospholipid expression represent another step in advancing our understanding of the aetiology of sPTB. Further work is needed to validate these findings in independent pregnancy cohorts.

The present study involved both human cohorts and animal experiments to explore the performance of soluble CD146 (sCD146), a marker of endothelial function, as a diagnostic marker of acutely decompensated heart failure (ADHF), to determine the influence of patients' characteristics on that performance and to explore the potential application of CD146 in the pathophysiology of ADHF.NT-proBNP and sCD146 were measured in three hundred ninety-one patients admitted to the emergency department for acute dyspnea. ROC curve analysis demonstrated that AUCs for ADHF diagnosis in dyspneic patients were 0.86 (95% CI: 0.82-0.90) for sCD146 and 0.90 (95% CI: 0.86-0.92) for NT-proBNP. Subgroup analyses demonstrated that adding sCD146 to NT-proBNP improved the diagnostic performance for patients lying in the gray zone of NT-proBNP (p=0.02) and could be especially useful for ruling-out ADHF. An experimental model of ADHF in rats using thoracic aortic constriction suggests that CD146 is expressed in the intima of large arteries and associated with both left ventricular function and organ congestion.sCD146, a marker of endothelial function, seems to be as powerful as NT-proBNP is used to detect the cardiac origin of an acute dyspnea. The combination of sCD146 and NT-proBNP may have better performance than NT-proBNP used alone in particular for patients underlying in the "gray" zone and could therefore be an improved option for ruling-out ADHF. Both experimental and human data suggest that CD146 is related to systolic left ventricular function and to organ congestion.

An overrepresentation of adverse pregnancy outcomes has been observed in pregnancies associated with a male fetus. We investigated the association between fetal gender and candidate biomarkers for preeclampsia. Proteins were quantified in samples taken at 20 weeks from women recruited to the SCreening fOr Pregnancy Endpoints (SCOPE) study (preeclampsia n = 150; no preeclampsia n = 450). In contrast to placental growth factor, soluble endoglin, and insulin-like growth factor acid labile subunit, levels of metallopeptidase domain 12 (ADAM12) at 20 weeks were dependent on fetal gender in pregnancies complicated by preeclampsia, for male (n = 73) fetuses the multiples of the median (MoM; interquartile range [IQR] 1.1-1.5) was 1.3, whereas for female fetuses (n = 75) MoM was 1.1 (1.0-1.3); P < .01. Prediction of preeclampsia using ADAM12 levels was improved for pregnancies associated with a male fetus (area under receiver–operator curve [AUC] 0.73 [95% confidence interval [CI] 0.67-0.80]) than that of a female fetus (AUC 0.62 [0.55-0.70]); P = .03. The data presented here fit a contemporary hypothesis that there is a difference between the genders in response to an adverse maternal environment and suggest that an alteration in ADAM12 may reflect an altered placental response in pregnancies subsequently complicated by preeclampsia.

Background Disease prevalence is rarely explicitly considered in the early stages of the development of novel prognostic tests. Rather, researchers use the area under the receiver operating characteristic (AUROC) as the key metric to gauge and report predictive performance ability. Because this statistic does not account for disease prevalence, proposed tests may not appropriately address clinical requirements. This ultimately impedes the translation of prognostic tests into clinical practice. Methods A method to express positive- and/or negative predictive value criteria (PPV, NPV) within the ROC space is presented. Equations are derived for so-called equi-PPV (and equi-NPV) lines. Herewith it is possible, for any given prevalence, to plot a series of sensitivity-specificity pairs which meet a specified PPV (or NPV) criterion onto the ROC space. This concept is introduced by firstly reviewing the well-established “mechanics”, strengths and limitations of the ROC analysis in the context of developing prognostic models. Then, the use of PPV (and/or) NPV criteria to augment the ROC analysis is elaborated. Additionally, an interactive web tool was also created to enable people to explore the dynamics of lines of equi-predictive value in function of prevalence. The web tool also allows to gauge what ROC curve shapes best meet specific positive and/or negative predictive value criteria (http://d4ta.link/ppvnpv/). Results To illustrate the merits and implications of this concept, an example on the prediction of pre-eclampsia risk in low-risk nulliparous pregnancies is elaborated. Conclusions In risk stratification, the clinical usefulness of a prognostic test can be expressed in positive- and negative predictive value criteria; the development of novel prognostic tests will be facilitated by the possibility to co-visualise such criteria together with ROC curves. To achieve clinically meaningful risk stratification, the development of separate tests to meet either a pre-specified positive value (rule-in) or a negative predictive value (rule-out) criteria should be considered: the characteristics of successful rule-in and rule-out tests may markedly differ. Electronic supplementary material The online version of this article (10.1186/s41512-017-0017-y) contains supplementary material, which is available to authorized users.

The risk stratification in patients presenting with acute dyspnoea remains a challenge. We therefore conducted a prospective, observational cohort study enrolling 292 patients presenting to the emergency department with acute dyspnoea. A proteomic approach for antibody-free targeted protein quantification based on high-end MS was used to measure LTBP2 [latent TGF (transforming growth factor)-binding protein 2] levels. Final diagnosis and death during follow-up were adjudicated blinded to LTBP2 levels. AHF (acute heart failure) was the final diagnosis in 54% of patients. In both AHF (P

Aims Biochemical marker testing has improved the evaluation and management of patients with cardiovascular diseases over the past decade. Natriuretic peptides (NPs), used in clinical practice to assess cardiac dysfunction, exhibit many limitations, however. We used an unbiased proteomics approach for the discovery of novel diagnostic plasma biomarkers of heart failure (HF). Methods and results A proteomics pipeline adapted for very low-abundant plasma proteins was applied to clinical samples from patients admitted with acute decompensated HF (ADHF). Quiescin Q6 (QSOX1), a protein involved in the formation of disulfide bridges, emerged as the best performing marker for ADHF (with an area under the receiver operator characteristic curve of 0.86, 95% confidence interval: 0.79–0.92), and novel isoforms of NPs were also identified. Diagnostic performance of QSOX1 for ADHF was confirmed in 267 prospectively collected subjects of whom 76 had ADHF. Combining QSOX1 to B-type NP (BNP) significantly improved diagnostic accuracy for ADHF by particularly improving specificity. Using thoracic aortic constriction in rats, QSOX1 was specifically induced within both left atria and ventricles at the time of HF onset. Conclusion The novel biomarker QSOX1 accurately identifies ADHF, particularly when combined with BNP. Through both clinical and experimental studies we provide lines of evidence for a link between ADHF and cardiovascular production of QSOX1.

Introduction Screening for pre-eclampsia is a focus of prenatal care and is largely based on the use of clinical risk factors. However, current screening protocols are unfit to determine pre-eclampsia risk in 1st time pregnant women; biomarkers have the potential to address this unmet need. As single biomarkers have insufficient predictive accuracy, unbiased biomarker discoveries have been performed to identify panels of markers which when combined, have the potential pre-eclampsia prediction. Metabolite based solutions using mass spectrometry have gained significant interest as they have the potential to readily translate to clinical practice. Objectives To translate the discovery that blood-borne metabolite biomarkers stratify pregnant women early in pregnancy (∼15 weeks) to their risk of pre-eclampsia [1] into a commercial LC–MS based clinical assay. To pursue fit-for-purpose testing of the first version of the developed LC–MS pipeline followed by independent verification through a case:control study. Methods The analysis pipeline incorporated (1) a single step metabolite extraction, (2) multiplex LC-QqQ-MS assays for 40+ metabolites and (3) a dedicated data processing protocol. Case:control study testing utilised 15 weeks’ plasma samples of from 50 pregnant women who subsequently developed pre-eclampsia and 500 random control pregnancies. All participants are part of the SCOPE study [2] and were recruited in Cork, Ireland. Results For the 40+ metabolite assays: 62% had a %CV £ 15% and 82% had a %CV £ 25%. Univariate analyses using the ROC statistic showed that 7 of the metabolites tested had significant predictive power (lower limit 95% CI ROC-AUC [3] 0.5). Multivariate logistic regression analysis revealed particular combinations of metabolites which identified groups of women either at increased risk or at decreased risk for pre-eclampsia. Conclusion These findings underpin the potential of metabolite-centric multimarker panels to encapsulate a complex syndrome such as pre-eclampsia considerably in advance of any clinical manifestation. Further development steps will include performing additional case:control studies and subsequent clinical validation of this metabolite based test in the large scale European, multicentre phase IIa clinical study IMproved Pregnancy Outcomes by Early Detection (IMPROvED) which is currently recruiting 1st time pregnant women in 5 European countries [3] . Acknowledgment The authors gratefully acknowledge funding from the EU-HEALTH Project IMPROvED (305169) of the Seventh Framework Programme (FP7).

Introduction Currently no test accurately predicts pre-eclampsia (PE) in a healthy nulliparous population. Unbiased protein biomarker discovery has the potential to identify novel markers but multimarker panels are required to achieve clinically relevant prediction of PE. To this purpose, single biomarker performances were obtained and multimarker panels developed in a significant subcohort of the international Screening fOr Pregnancy Endpoints study (SCOPE) study [1]. Objectives To identify and validate novel protein markers for PE prediction using chromatographic and mass spectrometric techniques which enable the identification and quantification of plasma proteins present in plasma at sub ng/ml concentration (Pronota, Belgium). Methods Pre-disease plasma samples (22–26 weeks) from women who subsequently developed PE and those with uncomplicated pregnancies [2] were used to generate 30 plasma proteome profiles using the MASStermind™ pipeline. A set of novel protein candidates were validated using an antibody-free mass spectrometry method using multiple reaction monitoring (MASSterclass™) in a subcohort of the SCOPE study (NZ & Aus) [1]. Relative abundance of 40+ proteins was determined in 20week plasma samples from 100 women who developed PE and 200 women who did not develop PE (included women with other pregnancy complications). Multivariate analyses were performed to identify algorithms with predictive performance using combinations confined to a maximum of 6 parameters (protein markers and clinical parameters) to avoid overfitting. Validation of the prediction panels was performed in an independent subcohort of SCOPE (Europe) comprising 50 PE and 150 no PE. Results From this large scale biomarker discovery effort a number of key results were obtained: a novel protein, i.e., Insulin-like growth factor binding protein, acid labile subunit (IGFALS), was identified. AUC for this marker for the prediction of all PE was 0.71 (CI 0.68–0.75) which was greater than both PlGF and s-Eng (respective AUCs: 0.64 and 0.61). IGFALS was also found to have predictive value for term (AUC 0.70) as well as preterm disease (AUC 0.75). Using multivariate analysis, marker panels were identified that achieved clinically relevant prediction (exemplary panel prediction of all PE cases AUC=0.79; prediction of preterm PE AUC=0.92). These multivariate models were successfully validated in the European SCOPE subcohort. In addition, predictive algorithms based on mass spectrometric read outs were largely invariant to interchanging the IGFALS mass spectrometry quantitation data with IGFALS ELISA data. Conclusion This study demonstrates the capability of high level LC-MS technologies to discover candidate biomarkers and execute large scale multiplex validation to develop a predictive screening test for preeclampsia.