Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression

Importance

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools.

Objective

To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information.

Design, Setting, and Participants

This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023.

Main Outcomes and Measures

Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview.

Results

Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001).

Conclusions and Relevance

This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

Personality, symptom, and demographic correlates of perceived efficacy of selective serotonin reuptake inhibitor monotherapy among current users with low mood: A data-driven approach

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) are often the first-line treatment option for depressive symptoms, however their efficacy varies across patients. Identifying predictors of response to SSRIs could facilitate personalised treatment of depression and improve treatment outcomes. The aim of this study was to develop a data-driven formulation of demographic, personality, and symptom-level factors associated with subjective response to SSRI treatment.

METHODS

Participants were recruited online and data were collected retrospectively through an extensive digital mental health questionnaire. Extreme gradient boosting classification with nested cross-validation was used to identify factors distinguishing between individuals with low (n=37) and high (n=111) perceived benefit from SSRI treatment.

RESULTS

The algorithm demonstrated a good predictive performance (test AUC=.88±.07). Positive affectivity was the strongest predictor of response to SSRIs and a major confounder of the remaining associations. After controlling for positive affectivity, as well as current wellbeing, severity of current depressive symptoms, and multicollinearity, only low positive affectivity, chronic pain, sleep problems, and unemployment remained significantly associated with diminished subjective response to SSRIs.

LIMITATIONS

This was an exploratory analysis of data collected at a single time point, for a study which had a different primary aim. Therefore, the results may not reflect causal relationships, and require validation in future prospective studies. Furthermore, the data were self-reported by internet users, which could affect integrity of the dataset and limit generalisability of the results.

CONCLUSIONS

Our findings suggest that demographic, personality, and symptom data may offer a potential cost-effective and efficient framework for SSRI treatment outcome prediction.

The Delta Study - Prevalence and characteristics of mood disorders in 924 individuals with low mood: Results of the of the World Health Organization Composite International Diagnostic Interview (CIDI)

OBJECTIVES

The Delta Study was undertaken to improve the diagnosis of mood disorders in individuals presenting with low mood. The current study aimed to estimate the prevalence and explore the characteristics of mood disorders in participants of the Delta Study, and discuss their implications for clinical practice.

METHODS

Individuals with low mood (Patients Health Questionnaire-9 score ≥5) and either no previous mood disorder diagnosis (baseline low mood group, n = 429), a recent (≤5 years) clinical diagnosis of MDD (baseline MDD group, n = 441) or a previous clinical diagnosis of BD (established BD group, n = 54), were recruited online. Self-reported demographic and clinical data were collected through an extensive online mental health questionnaire and mood disorder diagnoses were determined with the World Health Organization Composite International Diagnostic Interview (CIDI).

RESULTS

The prevalence of BD and MDD in the baseline low mood group was 24% and 36%, respectively. The prevalence of BD among individuals with a recent diagnosis of MDD was 31%. Participants with BD in both baseline low mood and baseline MDD groups were characterized by a younger age at onset of the first low mood episode, more severe depressive symptoms and lower wellbeing, relative to the MDD or low mood groups. Approximately half the individuals with BD diagnosed as MDD (49%) had experienced (hypo)manic symptoms prior to being diagnosed with MDD.

CONCLUSIONS

The current results confirm high under- and misdiagnosis rates of mood disorders in individuals presenting with low mood, potentially leading to worsening of symptoms and decreased well-being, and indicate the need for improved mental health triage in primary care.

Impact of a Web-Based Psychiatric Assessment on the Mental Health and Well-Being of Individuals Presenting With Depressive Symptoms: Longitudinal Observational Study

BACKGROUND

Web-based assessments of mental health concerns hold great potential for earlier, more cost-effective, and more accurate diagnoses of psychiatric conditions than that achieved with traditional interview-based methods.

OBJECTIVE

The aim of this study was to assess the impact of a comprehensive web-based mental health assessment on the mental health and well-being of over 2000 individuals presenting with symptoms of depression.

METHODS

Individuals presenting with depressive symptoms completed a web-based assessment that screened for mood and other psychiatric conditions. After completing the assessment, the study participants received a report containing their assessment results along with personalized psychoeducation. After 6 and 12 months, participants were asked to rate the usefulness of the web-based assessment on different mental health-related outcomes and to self-report on their recent help-seeking behavior, diagnoses, medication, and lifestyle changes. In addition, general mental well-being was assessed at baseline and both follow-ups using the Warwick-Edinburgh Mental Well-being Scale (WEMWBS).

RESULTS

Data from all participants who completed either the 6-month or the 12-month follow-up (N=2064) were analyzed. The majority of study participants rated the study as useful for their subjective mental well-being. This included talking more openly (1314/1939, 67.77%) and understanding one's mental health problems better (1083/1939, 55.85%). Although most participants (1477/1939, 76.17%) found their assessment results useful, only a small proportion (302/2064, 14.63%) subsequently discussed them with a mental health professional, leading to only a small number of study participants receiving a new diagnosis (110/2064, 5.33%). Among those who were reviewed, new mood disorder diagnoses were predicted by the digital algorithm with high sensitivity (above 70%), and nearly half of the participants with new diagnoses also had a corresponding change in medication. Furthermore, participants' subjective well-being significantly improved over 12 months (baseline WEMWBS score: mean 35.24, SD 8.11; 12-month WEMWBS score: mean 41.19, SD 10.59). Significant positive predictors of follow-up subjective well-being included talking more openly, exercising more, and having been reviewed by a psychiatrist.

CONCLUSIONS

Our results suggest that completing a web-based mental health assessment and receiving personalized psychoeducation are associated with subjective mental health improvements, facilitated by increased self-awareness and subsequent use of self-help interventions. Integrating web-based mental health assessments within primary and/or secondary care services could benefit patients further and expedite earlier diagnosis and effective treatment.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/18453.

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

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.

Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood

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.

Exploring the neuropsychiatric spectrum using high-content functional analysis of single-cell signaling networks

Neuropsychiatric disorders overlap in symptoms and share genetic risk factors, challenging their current classification into distinct diagnostic categories. Novel cross-disorder approaches are needed to improve our understanding of the heterogeneous nature of neuropsychiatric diseases and overcome existing bottlenecks in their diagnosis and treatment. Here we employ high-content multi-parameter phospho-specific flow cytometry, fluorescent cell barcoding and automated sample preparation to characterize ex vivo signaling network responses (n = 1764) measured at the single-cell level in B and T lymphocytes across patients diagnosed with four major neuropsychiatric disorders: autism spectrum condition (ASC), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ; n = 25 each), alongside matched healthy controls (n = 100). We identified 25 nodes (individual cell subtype-epitope-ligand combinations) significantly altered relative to the control group, with variable overlap between different neuropsychiatric diseases and heterogeneously expressed at the level of each individual patient. Reconstruction of the diagnostic categories from the altered nodes revealed an overlapping neuropsychiatric spectrum extending from MDD on one end, through BD and SCZ, to ASC on the other end. Network analysis showed that although the pathway structure of the epitopes was broadly preserved across the clinical groups, there were multiple discrete alterations in network connectivity, such as disconnections within the antigen/integrin receptor pathway and increased negative regulation within the Akt1 pathway in CD4⁺ T cells from ASC and SCZ patients, in addition to increased correlation of Stat1 (pY701) and Stat5 (pY694) responses in B cells from BD and MDD patients. Our results support the "dimensional" approach to neuropsychiatric disease classification and suggest potential novel drug targets along the neuropsychiatric spectrum.

Spinal manifestations of CLN1 disease start during the early postnatal period

Aim

To understand the progression of CLN1 disease and develop effective therapies we need to characterize early sites of pathology. Therefore, we performed a comprehensive evaluation of the nature and timing of early CLN1 disease pathology in the spinal cord, which appears especially vulnerable, and how this may affect behaviour.

Methods

We measured the spinal volume and neuronal number, and quantified glial activation, lymphocyte infiltration and oligodendrocyte maturation, as well as cytokine profile analysis during the early stages of pathology in Ppt1‐deficient (Ppt1^−/−) mouse spinal cords. We then performed quantitative gait analysis and open‐field behaviour tests to investigate the behavioural correlates during this period.

Results

We detected significant microglial activation in Ppt1^−/− spinal cords at 1 month. This was followed by astrocytosis, selective interneuron loss, altered spinal volumes and oligodendrocyte maturation at 2 months, before significant storage material accumulation and lymphocyte infiltration at 3 months. The same time course was apparent for inflammatory cytokine expression that was altered as early as one month. There was a transient early period at 2 months when Ppt1^−/− mice had a significantly altered gait that resembles the presentation in children with CLN1 disease. This occurred before an anticipated decline in overall locomotor performance across all ages.

Conclusion

These data reveal disease onset 2 months (25% of life‐span) earlier than expected, while spinal maturation is still ongoing. Our multi‐disciplinary data provide new insights into the spatio‐temporal staging of CLN1 pathogenesis during ongoing postnatal maturation, and highlight the need to deliver therapies during the presymptomatic period.

A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study

Background

Mood disorders affect hundreds of millions of people worldwide, imposing a substantial medical and economic burden. Existing diagnostic methods for mood disorders often result in a delay until accurate diagnosis, exacerbating the challenges of these disorders. Advances in digital tools for psychiatry and understanding the biological basis of mood disorders offer the potential for novel diagnostic methods that facilitate early and accurate diagnosis of patients.

Objective

The Delta Trial was launched to develop an algorithm-based diagnostic aid combining symptom data and proteomic biomarkers to reduce the misdiagnosis of bipolar disorder (BD) as a major depressive disorder (MDD) and achieve more accurate and earlier MDD diagnosis.

Methods

Participants for this ethically approved trial were recruited through the internet, mainly through Facebook advertising. Participants were then screened for eligibility, consented to participate, and completed an adaptive digital questionnaire that was designed and created for the trial on a purpose-built digital platform. A subset of these participants was selected to provide dried blood spot (DBS) samples and undertake a World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI). Inclusion and exclusion criteria were chosen to maximize the safety of a trial population that was both relevant to the trial objectives and generalizable. To provide statistical power and validation sets for the primary and secondary objectives, 840 participants were required to complete the digital questionnaire, submit DBS samples, and undertake a CIDI.

Results

The Delta Trial is now complete. More than 3200 participants completed the digital questionnaire, 924 of whom also submitted DBS samples and a CIDI, whereas a total of 1780 participants completed a 6-month follow-up questionnaire and 1542 completed a 12-month follow-up questionnaire. The analysis of the trial data is now underway.

Conclusions

If a diagnostic aid is able to improve the diagnosis of BD and MDD, it may enable earlier treatment for patients with mood disorders.

International Registered Report Identifier (IRRID)

DERR1-10.2196/18453

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals

Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC = 0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals.

Multimodel inference for biomarker development: an application to schizophrenia

In the present study, to improve the predictive performance of a model and its reproducibility when applied to an independent data set, we investigated the use of multimodel inference to predict the probability of having a complex psychiatric disorder. We formed training and test sets using proteomic data (147 peptides from 77 proteins) from two-independent collections of first-onset drug-naive schizophrenia patients and controls. A set of prediction models was produced by applying lasso regression with repeated tenfold cross-validation to the training set. We used feature extraction and model averaging across the set of models to form two prediction models. The resulting models clearly demonstrated the utility of a multimodel based approach to make good (training set AUC > 0.80) and reproducible predictions (test set AUC > 0.80) for the probability of having schizophrenia. Moreover, we identified four proteins (five peptides) whose effect on the probability of having schizophrenia was modified by sex, one of which was a novel potential biomarker of schizophrenia, foetal haemoglobin. The evidence of effect modification suggests that future schizophrenia studies should be conducted in males and females separately. Future biomarker studies should consider adopting a multimodel approach and going beyond the main effects of features.

Desmopressin stimulation testing: Response to intravenous and intranasal forms

INTRODUCTION

Desmopressin is commonly used to reduce bleeding in patients with mucocutaneous bleeding disorders and is available in both intravenous and intranasal forms. Given the variability in response to desmopressin at an individual level, its effectiveness should be assessed with a test dose prior to being advised for use. At this time, no trial has extensively compared the use of intranasal desmopressin to intravenous desmopressin.

AIMS

To determine whether both forms of desmopressin are equally effective in yielding a positive response in laboratory assays in paediatric patients with von Willebrand disease or probable von Willebrand disease.

METHODS

We evaluated medical record data for 58 patients who underwent desmopressin stimulation testing in our haematology clinic during a 1-year period. Data were collected on demographic information and haematologic laboratory assays prior to desmopressin administration and one hour following desmopressin.

RESULTS

There was an absolute increase in von Willebrand antigen to levels appropriate for haemostasis following both forms of desmopressin, although this increase was significantly greater in the intravenous group compared to the intranasal group. There was also a significant absolute increase in Ristocetin Cofactor and Factor VIII levels following desmopressin in both groups.

CONCLUSION

Both intravenous and intranasal forms of desmopressin produce a positive response during desmopressin stimulation testing and can be used to identify patients for whom this medication would be effective.

Schizophrenia-risk and urban birth are associated with proteomic changes in neonatal dried blood spots

In the present study, we tested whether there were proteomic differences in blood between schizophrenia patients after the initial onset of the disorder and controls; and whether those differences were also present at birth among neonates who later developed schizophrenia compared to those without a psychiatric admission. We used multiple reaction monitoring mass spectrometry to quantify 77 proteins (147 peptides) in serum samples from 60 first-onset drug-naive schizophrenia patients and 77 controls, and 96 proteins (152 peptides) in 892 newborn blood-spot (NBS) samples collected between 1975 and 1985. Both serum and NBS studies showed significant alterations in protein levels. Serum results revealed that Haptoglobin and Plasma protease C1 inhibitor were significantly upregulated in first-onset schizophrenia patients (corrected P < 0.05). Alpha-2-antiplasmin, Complement C4-A and Antithrombin-III were increased in first-onset schizophrenia patients (uncorrected P-values 0.041, 0.036 and 0.013, respectively) and also increased in newborn babies who later develop schizophrenia (P-values 0.0058, 0.013 and 0.044, respectively). We also tested whether protein abundance at birth was associated with exposure to an urban environment during pregnancy and found highly significant proteomic differences at birth between urban and rural environments. The prediction model for urbanicity had excellent predictive performance in both discovery (area under the receiver operating characteristic curve (AUC) = 0.90) and validation (AUC = 0.89) sample sets. We hope that future biomarker studies based on stored NBS samples will identify prognostic disease indicators and targets for preventive measures for neurodevelopmental conditions, particularly those with onset during early childhood, such as autism spectrum disorder.

Altered apolipoprotein C expression in association with cognition impairments and hippocampus volume in schizophrenia and bipolar disorder

Proteomic analyses facilitate the interpretation of molecular biomarker probes which are very helpful in diagnosing schizophrenia (SZ). In the current study, we attempt to test whether potential differences in plasma protein expressions in SZ and bipolar disorder (BD) are associated with cognitive deficits and their underlying brain structures. Forty-two plasma proteins of 29 SZ patients, 25 BD patients and 93 non-clinical controls were quantified and analysed using multiple reaction monitoring-based triple quadrupole mass spectrometry approach. We also computed group comparisons of protein expressions between patients and controls, and between SZ and BD patients, as well. Potential associations of protein levels with cognitive functioning (psychomotor speed, executive functioning, crystallised intelligence) as well as underlying brain volume in the hippocampus were explored, using bivariate correlation analyses. The main finding of this study was that apolipoprotein expression differed between patients and controls and that these alterations in both disease groups were putatively related to cognitive impairments as well as to hippocampus volumes. However, none of the protein level differences were related to clinical symptom severity. In summary, altered apolipoprotein expression in BD and SZ was linked to cognitive decline and underlying morphological changes in both disorders. Our results suggest that the detection of molecular patterns in association with cognitive performance and its underlying brain morphology is of great importance for understanding of the pathological mechanisms of SZ and BD, as well as for supporting the diagnosis and treatment of both disorders.

Towards reproducible MRM based biomarker discovery using dried blood spots

There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

Blood-based immune-endocrine biomarkers of treatment response in depression

Antidepressant treatment for major depressive disorder remains suboptimal with response rates of just over 50%. Although treatment guidelines, algorithms and clinical keys are available to assist the clinician, the process of finding an effective pharmacotherapy to maximise benefit for the individual patient is largely by "trial and error" and remains challenging. This highlights a clear need to identify biomarkers of treatment response to help guide personalised treatment strategies. We have carried out the largest multiplex immunoassay based longitudinal study to date, examining up to 258 serum markers involved in immune, endocrine and metabolic processes as potential biomarkers associated with treatment response in 332 depression patients recruited from four independent clinical centres. We demonstrated for the first time that circulating Apolipoprotein A-IV, Endoglin, Intercellular Adhesion Molecule 1, Tissue Inhibitor of Metalloproteinases 1, Plasminogen Activator Inhibitor 1, Thrombopoietin, Complement C3, Hepatocyte Growth Factor and Insulin-like Growth Factor-Binding Protein 2 were associated with response to different antidepressants. In addition, we showed that specific sets of immune-endocrine proteins were associated with response to Venlafaxine (serotonin-norepinephrine reuptake inhibitor), Imipramine (tricyclic antidepressant) and other antidepressant drugs. However, we were not able to reproduce the literature findings on BDNF and TNF-α, two of the most commonly reported candidate treatment response markers. Despite the need for extensive validation studies, our preliminary findings suggest that a pre-treatment immune-endocrine profile may help to determine a patient's likelihood to respond to specific antidepressant and/or alternative treatments such as anti-inflammatory drugs, providing hope for future personalised treatment approaches.

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology

Pharmacological and genetic rodent models of schizophrenia play an important role in the drug discovery pipeline, but quantifying the molecular similarity of such models with the underlying human pathophysiology has proved difficult. We developed a novel systems biology methodology for the direct comparison of anterior prefrontal cortex tissue from four established glutamatergic rodent models and schizophrenia patients, enabling the evaluation of which model displays the greatest similarity to schizophrenia across different pathophysiological characteristics of the disease. Liquid chromatography coupled tandem mass spectrometry (LC-MS^E) proteomic profiling was applied comparing healthy and "disease state" in human post-mortem samples and rodent brain tissue samples derived from models based on acute and chronic phencyclidine (PCP) treatment, ketamine treatment or NMDA receptor knockdown. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of five functional domains of the disease such as "development and differentiation", which were represented across all four rodent models and were thus subsequently used for cross-species comparison. Kernel-based machine learning techniques quantified that the chronic PCP model represented schizophrenia brain changes most closely for four of these functional domains. This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of schizophrenia most closely, providing an indication of face validity as well as potential guidance in the refinement of construct and predictive validity. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating functional dimensions of behaviour with distinct biological processes.

Molecular serum signature of treatment resistant depression

RATIONALE

A substantial number of patients suffering from major depressive disorder (MDD) do not respond to multiple trials of anti-depressants, develop a chronic course of disease and become treatment resistant. Most of the studies investigating molecular changes in treatment-resistant depression (TRD) have only examined a limited number of molecules and genes. Consequently, biomarkers associated with TRD are still lacking.

OBJECTIVES

This study aimed to use recently advanced high-throughput proteomic platforms to identify peripheral biomarkers of TRD defined by two staging models, the Thase and Rush staging model (TRM) and the Maudsley Staging Model (MSM).

METHODS

Serum collected from an inpatient cohort of 65 individuals suffering from MDD was analysed using two different mass spectrometric-based platforms, label-free liquid chromatography mass spectrometry (LC-MS(E)) and selective reaction monitoring (SRM), as well as a multiplex bead based assay.

RESULTS

In the LC-MS(E) analysis, proteins involved in the acute phase response and complement activation and coagulation were significantly different between the staging groups in both models. In the multiplex bead-based assay analysis TNF-α levels (log(odds) = -4.95, p = 0.045) were significantly different in the TRM comparison. Using SRM, significant changes of three apolipoproteins A-I (β = 0.029, p = 0.035), M (β = -0.017, p = 0.009) and F (β = -0.031, p = 0.024) were associated with the TRM but not the MSM.

CONCLUSION

Overall, our findings suggest that proteins, which are involved in immune and complement activation, may represent potential biomarkers that could be used by clinicians to identify high-risk patients. Nevertheless, given that the molecular changes between the staging groups were subtle, the results need to be interpreted cautiously.

Sex Differences in Serum Markers of Major Depressive Disorder in the Netherlands Study of Depression and Anxiety (NESDA)

Women have a consistently higher prevalence of major depressive disorder (MDD) than men. Hypotheses implicating hypothalamic-pituitary -adrenal, -gonadal, and -thyroid axes, immune response, genetic factors, and neurotransmitters have emerged to explain this difference. However, more evidence for these hypotheses is needed and new explanations must be explored. Here, we investigated sex differences in MDD markers using multiplex immunoassay measurements of 171 serum molecules in individuals enrolled in the Netherlands Study of Depression and Anxiety (N_MDD = 231; N_control = 365). We found 28 sex-dependent markers of MDD, as quantified by a significant interaction between sex and log₂-transformed analyte concentration in a logistic regression with diagnosis (MDD/control) as the outcome variable (p<0.05; q<0.30). Among these were a number of male-specific associations between MDD and elevated levels of proteins involved in immune response, including C-reactive protein, trefoil factor 3, cystatin-C, fetuin-A, β2-microglobulin, CD5L, FASLG receptor, and tumor necrosis factor receptor 2. Furthermore, only male MDD could be classified with an accuracy greater than chance using the measured serum analytes (area under the ROC curve = 0.63). These findings may have consequences for the generalization of inflammatory hypotheses of depression to males and females and have important implications for the development of diagnostic biomarker tests for MDD. More studies are needed to validate these results, investigate a broader range of biological pathways, and integrate this data with brain imaging, genetic, and other relevant data.

Serum biomarkers predictive of depressive episodes in panic disorder

Panic disorder with or without comorbid agoraphobia (PD/PDA) has been linked to an increased risk to develop subsequent depressive episodes, yet the underlying pathophysiology of these disorders remains poorly understood. We aimed to identify a biomarker panel predictive for the development of a depressive disorder (major depressive disorder and/or dysthymia) within a 2-year-follow-up period. Blood serum concentrations of 165 analytes were evaluated in 120 PD/PDA patients without depressive disorder baseline diagnosis (6-month-recency) in the Netherlands Study of Depression and Anxiety (NESDA). We assessed the predictive performance of serum biomarkers, clinical, and self-report variables using receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC). False-discovery-rate corrected logistic regression model selection of serum analytes and covariates identified an optimal predictive panel comprised of tetranectin and creatine kinase MB along with patient gender and scores from the Inventory of Depressive Symptomatology (IDS) rating scale. Combined, an AUC of 0.87 was reached for identifying the PD/PDA patients who developed a depressive disorder within 2 years (n = 44). The addition of biomarkers represented a significant (p = 0.010) improvement over using gender and IDS alone as predictors (AUC = 0.78). For the first time, we report on a combination of biological serum markers, clinical variables and self-report inventories that can detect PD/PDA patients at increased risk of developing subsequent depressive disorders with good predictive performance in a naturalistic cohort design. After an independent validation our proposed biomarkers could prove useful in the detection of at-risk PD/PDA patients, allowing for early therapeutic interventions and improving clinical outcome.

Towards a blood-based diagnostic panel for bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD.

METHODS AND FINDINGS

We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies. We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC)⩾0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel.

CONCLUSIONS

An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.

Identification of an Immune-Neuroendocrine Biomarker Panel for Detection of Depression: A Joint Effects Statistical Approach

BACKGROUND/AIMS

Less than half of depression patients are correctly diagnosed within the primary care setting. Previous proteomic studies have identified numerous immune and neuroendocrine changes in patients. However, few studies have considered the joint effects of biological molecules and their diagnostic potential. Our aim was to develop and validate a diagnostic serum biomarker panel identified through joint effects analysis of multiplex immunoassay profiling data from 1,007 clinical samples.

METHODS

In stage 1, we conducted a meta-analysis of two independent cohorts of 78 first-/recent-onset drug-naive/drug-free depression patients and 156 controls and applied the 10-fold cross-validation with least absolute shrinkage and selection operator regression to identify an optimal diagnostic prediction model (biomarker panel). In stage 2, we tested the discriminatory performance of this biomarker panel using the naturalistic Netherlands Study of Depression and Anxiety (NESDA) cohort of 468 depression patients and 305 controls.

RESULTS

An optimal panel of 33 immune-neuroendocrine biomarkers and gender was selected in the meta-analysis. Testing this biomarker-gender panel using the NESDA cohort resulted in a moderate to good performance to differentiate patients from controls (0.69 < AUC < 0.86), particularly the first-episode patients free of chronic non-psychiatric diseases or medications and following incorporation of sociodemographic covariates (0.76 < AUC < 0.92).

CONCLUSION

Despite the need for additional validation studies, we demonstrated that a blood-based biomarker-sociodemographic panel can detect depression in naturalistic healthcare settings with good discriminatory power. Further refinements of blood biomarker panels aiding in the diagnosis of depression may provide a cost-effective means to increase accuracy of clinical diagnosis within the primary care setting.

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention.

Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r² ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4⁺ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

Genetic association studies have identified many DNA sequence variants that associate with disease risk. By exploiting the known correlation that exists between neighbouring variants in the genome, inference can be extended beyond those individual variants tested to identify sets within which a causal variant is likely to reside. However, this correlation, particularly in the presence of multiple disease causing variants in relative proximity, makes disentangling the specific causal variants difficult. Statistical approaches to this fine mapping problem have traditionally taken a stepwise search approach, beginning with the most associated variant in a region, then iteratively attempting to find additional associated variants. We adapted a stochastic search approach that avoids this stepwise process and is explicitly designed for dealing with highly correlated predictors to the fine mapping problem. We showed in simulated data that it outperforms its stepwise counterpart and other variable selection strategies such as the lasso. We applied our approach to understand the association of two immune-mediated diseases to a region on chromosome 10p15. We identified a model for multiple sclerosis containing two variants, neither of which was found through a stepwise search, and functionally linked both of these to the neighbouring candidate gene, IL2RA, in independent data. Our approach can be used to aid fine mapping of other disease-associated regions, which is critical for design of functional follow-up studies required to understand the mechanisms through which genetic variants influence disease.

Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers

Genetic studies of type 1 diabetes (T1D) have identified 50 susceptibility regions^1,2 (www.T1DBase.org) revealing major pathways contributing to risk³, with some loci shared across immune disorders^4–6. In order to make genetic comparisons across autoimmune disorders as informative as possible a dense genotyping array, the ImmunoChip, was developed, from which four novel T1D regions were identified (P < 5 × 10⁻⁸). A comparative analysis with 15 immune diseases (www.ImmunoBase.org) revealed that T1D is more similar genetically to other autoantibody-positive diseases, most significantly to juvenile idiopathic arthritis and least to ulcerative colitis, and provided support for three additional novel T1D loci. Using a Bayesian approach, we defined credible sets for the T1D SNPs. These T1D SNPs localized to enhancer sequences active in thymus, T and B cells, and CD34+ stem cells. Enhancer-promoter interactions can now be analyzed in these cell types to identify which particular genes and regulatory sequences are causal.

Detection and correction of artefacts in estimation of rare copy number variants and analysis of rare deletions in type 1 diabetes

Copy number variants (CNVs) have been proposed as a possible source of ‘missing heritability’ in complex human diseases. Two studies of type 1 diabetes (T1D) found null associations with common copy number polymorphisms, but CNVs of low frequency and high penetrance could still play a role. We used the Log-R-ratio intensity data from a dense single nucleotide polymorphism (SNP) array, ImmunoChip, to detect rare CNV deletions (rDELs) and duplications (rDUPs) in 6808 T1D cases, 9954 controls and 2206 families with T1D-affected offspring. Initial analyses detected CNV associations. However, these were shown to be false-positive findings, failing replication with polymerase chain reaction. We developed a pipeline of quality control (QC) tests that were calibrated using systematic testing of sensitivity and specificity. The case–control odds ratios (OR) of CNV burden on T1D risk resulting from this QC pipeline converged on unity, suggesting no global frequency difference in rDELs or rDUPs. There was evidence that deletions could impact T1D risk for a small minority of cases, with enrichment for rDELs longer than 400 kb (OR = 1.57, P = 0.005). There were also 18 de novo rDELs detected in affected offspring but none for unaffected siblings (P = 0.03). No specific CNV regions showed robust evidence for association with T1D, although frequencies were lower than expected (most less than 0.1%), substantially reducing statistical power, which was examined in detail. We present an R-package, plumbCNV, which provides an automated approach for QC and detection of rare CNVs that can facilitate equivalent analyses of large-scale SNP array datasets.

Plasma concentrations of soluble IL-2 receptor α (CD25) are increased in type 1 diabetes and associated with reduced C-peptide levels in young patients

AIMS/HYPOTHESIS

Type 1 diabetes is a common autoimmune disease that has genetic and environmental determinants. Variations within the IL2 and IL2RA (also known as CD25) gene regions are associated with disease risk, and variation in expression or function of these proteins is likely to be causal. We aimed to investigate if circulating concentrations of the soluble form of CD25, sCD25, an established marker of immune activation and inflammation, were increased in individuals with type 1 diabetes and if this was associated with the concentration of C-peptide, a measure of insulin production that reflects the degree of autoimmune destruction of the insulin-producing beta cells.

METHODS

We used immunoassays to measure sCD25 and C-peptide in peripheral blood plasma from patient and control samples.

RESULTS

We identified that sCD25 was increased in patients with type 1 diabetes compared with controls and replicated this result in an independent set of 86 adult patient and 80 age-matched control samples (p = 1.17 × 10(-3)). In 230 patients under 20 years of age, with median duration-of-disease of 6.1 years, concentrations of sCD25 were negatively associated with C-peptide concentrations (p = 4.8 × 10(-3)).

CONCLUSIONS/INTERPRETATION

The 25% increase in sCD25 in patients, alongside the inverse association between sCD25 and C-peptide, probably reflect the adverse effects of an on-going, actively autoimmune and inflammatory immune system on beta cell function in patients.

Serum proteomic analysis identifies sex-specific differences in lipid metabolism and inflammation profiles in adults diagnosed with Asperger syndrome

Background

The higher prevalence of Asperger Syndrome (AS) and other autism spectrum conditions in males has been known for many years. However, recent multiplex immunoassay profiling studies have shown that males and females with AS have distinct proteomic changes in serum.

Methods

Here, we analysed sera from adults diagnosed with AS (males = 14, females = 16) and controls (males = 13, females = 16) not on medication at the time of sample collection, using a combination of multiplex immunoassay and shotgun label-free liquid chromatography mass spectrometry (LC-MS^E). The main objective was to identify sex-specific serum protein changes associated with AS.

Results

Multiplex immunoassay profiling led to identification of 16 proteins that were significantly altered in AS individuals in a sex-specific manner. Three of these proteins were altered in females (ADIPO, IgA, APOA1), seven were changed in males (BMP6, CTGF, ICAM1, IL-12p70, IL-16, TF, TNF-alpha) and six were changed in both sexes but in opposite directions (CHGA, EPO, IL-3, TENA, PAP, SHBG). Shotgun LC-MS^E profiling led to identification of 13 serum proteins which had significant sex-specific changes in the AS group and, of these, 12 were altered in females (APOC2, APOE, ARMC3, CLC4K, FETUB, GLCE, MRRP1, PTPA, RN149, TLE1, TRIPB, ZC3HE) and one protein was altered in males (RGPD4). The free androgen index in females with AS showed an increased ratio of 1.63 compared to controls.

Conclusion

Taken together, the serum multiplex immunoassay and shotgun LC-MS^E profiling results indicate that adult females with AS had alterations in proteins involved mostly in lipid transport and metabolism pathways, while adult males with AS showed changes predominantly in inflammation signalling. These results provide further evidence that the search for biomarkers or novel drug targets in AS may require stratification into male and female subgroups, and could lead to the development of novel targeted treatment approaches.

Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts

BACKGROUND

Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.

METHODS AND FINDINGS

We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects. Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m(2) higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10⁻²⁷). The BMI allele score was associated both with BMI (p = 6.30×10⁻⁶²) and 25(OH)D (-0.06% [95% CI -0.10 to -0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10⁻⁵⁷ for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: -4.2 [95% CI -7.1 to -1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores).

CONCLUSIONS

On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.

Evidence of gene-gene interaction and age-at-diagnosis effects in type 1 diabetes

The common genetic loci that independently influence the risk of type 1 diabetes have largely been determined. Their interactions with age-at-diagnosis of type 1 diabetes, sex, or the major susceptibility locus, HLA class II, remain mostly unexplored. A large collection of more than 14,866 type 1 diabetes samples (6,750 British diabetic individuals and 8,116 affected family samples of European descent) were genotyped at 38 confirmed type 1 diabetes-associated non-HLA regions and used to test for interaction of association with age-at-diagnosis, sex, and HLA class II genotypes using regression models. The alleles that confer susceptibility to type 1 diabetes at interleukin-2 (IL-2), IL2/4q27 (rs2069763) and renalase, FAD-dependent amine oxidase (RNLS)/10q23.31 (rs10509540), were associated with a lower age-at-diagnosis (P = 4.6 × 10⁻⁶ and 2.5 × 10⁻⁵, respectively). For both loci, individuals carrying the susceptible homozygous genotype were, on average, 7.2 months younger at diagnosis than those carrying the protective homozygous genotypes. In addition to protein tyrosine phosphatase nonreceptor type 22 (PTPN22), evidence of statistical interaction between HLA class II genotypes and rs3087243 at cytotoxic T-lymphocyte antigen 4 (CTLA4)/2q33.2 was obtained (P = 7.90 × 10⁻⁵). No evidence of differential risk by sex was obtained at any loci (P ≥ 0.01). Statistical interaction effects can be detected in type 1 diabetes although they provide a relatively small contribution to our understanding of the familial clustering of the disease.

Seven newly identified loci for autoimmune thyroid disease

Autoimmune thyroid disease (AITD), including Graves' disease (GD) and Hashimoto's thyroiditis (HT), is one of the most common of the immune-mediated diseases. To further investigate the genetic determinants of AITD, we conducted an association study using a custom-made single-nucleotide polymorphism (SNP) array, the ImmunoChip. The SNP array contains all known and genotype-able SNPs across 186 distinct susceptibility loci associated with one or more immune-mediated diseases. After stringent quality control, we analysed 103 875 common SNPs (minor allele frequency >0.05) in 2285 GD and 462 HT patients and 9364 controls. We found evidence for seven new AITD risk loci (P < 1.12 × 10⁻⁶; a permutation test derived significance threshold), five at locations previously associated and two at locations awaiting confirmation, with other immune-mediated diseases.

Confirmation of novel type 1 diabetes risk loci in families

AIMS/HYPOTHESIS

Over 50 regions of the genome have been associated with type 1 diabetes risk, mainly using large case/control collections. In a recent genome-wide association (GWA) study, 18 novel susceptibility loci were identified and replicated, including replication evidence from 2,319 families. Here, we, the Type 1 Diabetes Genetics Consortium (T1DGC), aimed to exclude the possibility that any of the 18 loci were false-positives due to population stratification by significantly increasing the statistical power of our family study.

METHODS

We genotyped the most disease-predicting single-nucleotide polymorphisms at the 18 susceptibility loci in 3,108 families and used existing genotype data for 2,319 families from the original study, providing 7,013 parent-child trios for analysis. We tested for association using the transmission disequilibrium test.

RESULTS

Seventeen of the 18 susceptibility loci reached nominal levels of significance (p < 0.05) in the expanded family collection, with 14q24.1 just falling short (p = 0.055). When we allowed for multiple testing, ten of the 17 nominally significant loci reached the required level of significance (p < 2.8 × 10(-3)). All susceptibility loci had consistent direction of effects with the original study.

CONCLUSIONS/INTERPRETATION

The results for the novel GWA study-identified loci are genuine and not due to population stratification. The next step, namely correlation of the most disease-associated genotypes with phenotypes, such as RNA and protein expression analyses for the candidate genes within or near each of the susceptibility regions, can now proceed.

Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes

One mechanism by which disease-associated DNA variation can alter disease risk is altering gene expression. However, linkage disequilibrium (LD) between variants, mostly single-nucleotide polymorphisms (SNPs), means it is not sufficient to show that a particular variant associates with both disease and expression, as there could be two distinct causal variants in LD. Here, we describe a formal statistical test of colocalization and apply it to type 1 diabetes (T1D)-associated regions identified mostly through genome-wide association studies and expression quantitative trait loci (eQTLs) discovered in a recently determined large monocyte expression data set from the Gutenberg Health Study (1370 individuals), with confirmation sought in an additional data set from the Cardiogenics Transcriptome Study (558 individuals). We excluded 39 out of 60 overlapping eQTLs in 49 T1D regions from possible colocalization and identified 21 coincident eQTLs, representing 21 genes in 14 distinct T1D regions. Our results reflect the importance of monocyte (and their derivatives, macrophage and dendritic cell) gene expression in human T1D and support the candidacy of several genes as causal factors in autoimmune pancreatic beta-cell destruction, including AFF3, CD226, CLECL1, DEXI, FKRP, PRKD2, RNLS, SMARCE1 and SUOX, in addition to the recently described GPR183 (EBI2) gene.

FUT2 nonsecretor status links type 1 diabetes susceptibility and resistance to infection

OBJECTIVE

FUT2 encodes the α(1,2) fucosyltransferase that determines blood group secretor status. Homozygotes (A/A) for the common nonsense mutation rs601338A>G (W143X) are nonsecretors and are unable to express histo-blood group antigens in secretions and on mucosal surfaces. This mutation has been reported to provide resistance to Norovirus and susceptibility to Crohn's disease, and hence we aimed to determine if it also affects risk of type 1 diabetes.

RESEARCH DESIGN AND METHODS

rs601338A>G was genotyped in 8,344 patients with type 1 diabetes, 10,008 control subjects, and 3,360 type 1 diabetic families. Logistic regression models were used to analyze the case-control collection, and conditional logistic regression was used to analyze the family collection. RESULTS The nonsecretor A/A genotype of rs601338A>G was found to confer susceptibility to type 1 diabetes in both the case-control and family collections (odds ratio for AA 1.29 [95% CI 1.20-1.37] and relative risk for AA 1.22 [95% CI = 1.12-1.32]; combined P = 4.3 × 10(-18)), based on a recessive effects model.

CONCLUSIONS

Our findings linking FUT2 and type 1 diabetes highlight the intriguing relationship between host resistance to infections and susceptibility to autoimmune disease.

Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene

The chromosome 16p13 region has been associated with several autoimmune diseases, including type 1 diabetes (T1D) and multiple sclerosis (MS). CLEC16A has been reported as the most likely candidate gene in the region, since it contains the most disease-associated single-nucleotide polymorphisms (SNPs), as well as an imunoreceptor tyrosine-based activation motif. However, here we report that intron 19 of CLEC16A, containing the most autoimmune disease-associated SNPs, appears to behave as a regulatory sequence, affecting the expression of a neighbouring gene, DEXI. The CLEC16A alleles that are protective from T1D and MS are associated with increased expression of DEXI, and no other genes in the region, in two independent monocyte gene expression data sets. Critically, using chromosome conformation capture (3C), we identified physical proximity between the DEXI promoter region and intron 19 of CLEC16A, separated by a loop of >150 kb. In reciprocal experiments, a 20 kb fragment of intron 19 of CLEC16A, containing SNPs associated with T1D and MS, as well as with DEXI expression, interacted with the promotor region of DEXI but not with candidate DNA fragments containing other potential causal genes in the region, including CLEC16A. Intron 19 of CLEC16A is highly enriched for transcription-factor-binding events and markers associated with enhancer activity. Taken together, these data indicate that although the causal variants in the 16p13 region lie within CLEC16A, DEXI is an unappreciated autoimmune disease candidate gene, and illustrate the power of the 3C approach in progressing from genome-wide association studies results to candidate causal genes.

Bronchoscopic lung-volume reduction with Exhale airway stents for emphysema (EASE trial): randomised, sham-controlled, multicentre trial

BACKGROUND

Airway bypass is a bronchoscopic lung-volume reduction procedure for emphysema whereby transbronchial passages into the lung are created to release trapped air, supported with paclitaxel-coated stents to ease the mechanics of breathing. The aim of the EASE (Exhale airway stents for emphysema) trial was to evaluate safety and efficacy of airway bypass in people with severe homogeneous emphysema.

METHODS

We undertook a randomised, double-blind, sham-controlled study in 38 specialist respiratory centres worldwide. We recruited 315 patients who had severe hyperinflation (ratio of residual volume [RV] to total lung capacity of ≥0·65). By computer using a random number generator, we randomly allocated participants (in a 2:1 ratio) to either airway bypass (n=208) or sham control (107). We divided investigators into team A (masked), who completed pre-procedure and post-procedure assessments, and team B (unmasked), who only did bronchoscopies without further interaction with patients. Participants were followed up for 12 months. The 6-month co-primary efficacy endpoint required 12% or greater improvement in forced vital capacity (FVC) and 1 point or greater decrease in the modified Medical Research Council dyspnoea score from baseline. The composite primary safety endpoint incorporated five severe adverse events. We did Bayesian analysis to show the posterior probability that airway bypass was superior to sham control (success threshold, 0·965). Analysis was by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00391612.

FINDINGS

All recruited patients were included in the analysis. At 6 months, no difference between treatment arms was noted with respect to the co-primary efficacy endpoint (30 of 208 for airway bypass vs 12 of 107 for sham control; posterior probability 0·749, below the Bayesian success threshold of 0·965). The 6-month composite primary safety endpoint was 14·4% (30 of 208) for airway bypass versus 11·2% (12 of 107) for sham control (judged non-inferior, with a posterior probability of 1·00 [Bayesian success threshold >0·95]).

INTERPRETATION

Although our findings showed safety and transient improvements, no sustainable benefit was recorded with airway bypass in patients with severe homogeneous emphysema.

FUNDING

Broncus Technologies.

Inherited variation in vitamin D genes is associated with predisposition to autoimmune disease type 1 diabetes

OBJECTIVE

Vitamin D deficiency (25-hydroxyvitamin D [25(OH)D] <50 nmol/L) is commonly reported in both children and adults worldwide, and growing evidence indicates that vitamin D deficiency is associated with many extraskeletal chronic disorders, including the autoimmune diseases type 1 diabetes and multiple sclerosis.

RESEARCH DESIGN AND METHODS

We measured 25(OH)D concentrations in 720 case and 2,610 control plasma samples and genotyped single nucleotide polymorphisms from seven vitamin D metabolism genes in 8,517 case, 10,438 control, and 1,933 family samples. We tested genetic variants influencing 25(OH)D metabolism for an association with both circulating 25(OH)D concentrations and disease status.

RESULTS

Type 1 diabetic patients have lower circulating levels of 25(OH)D than similarly aged subjects from the British population. Only 4.3 and 18.6% of type 1 diabetic patients reached optimal levels (≥75 nmol/L) of 25(OH)D for bone health in the winter and summer, respectively. We replicated the associations of four vitamin D metabolism genes (GC, DHCR7, CYP2R1, and CYP24A1) with 25(OH)D in control subjects. In addition to the previously reported association between type 1 diabetes and CYP27B1 (P = 1.4 × 10(-4)), we obtained consistent evidence of type 1 diabetes being associated with DHCR7 (P = 1.2 × 10(-3)) and CYP2R1 (P = 3.0 × 10(-3)).

CONCLUSIONS

Circulating levels of 25(OH)D in children and adolescents with type 1 diabetes vary seasonally and are under the same genetic control as in the general population but are much lower. Three key 25(OH)D metabolism genes show consistent evidence of association with type 1 diabetes risk, indicating a genetic etiological role for vitamin D deficiency in type 1 diabetes.

Common genetic determinants of vitamin D insufficiency: a genome-wide association study

BACKGROUND

Vitamin D is crucial for maintenance of musculoskeletal health, and might also have a role in extraskeletal tissues. Determinants of circulating 25-hydroxyvitamin D concentrations include sun exposure and diet, but high heritability suggests that genetic factors could also play a part. We aimed to identify common genetic variants affecting vitamin D concentrations and risk of insufficiency.

METHODS

We undertook a genome-wide association study of 25-hydroxyvitamin D concentrations in 33 996 individuals of European descent from 15 cohorts. Five epidemiological cohorts were designated as discovery cohorts (n=16 125), five as in-silico replication cohorts (n=9367), and five as de-novo replication cohorts (n=8504). 25-hydroxyvitamin D concentrations were measured by radioimmunoassay, chemiluminescent assay, ELISA, or mass spectrometry. Vitamin D insufficiency was defined as concentrations lower than 75 nmol/L or 50 nmol/L. We combined results of genome-wide analyses across cohorts using Z-score-weighted meta-analysis. Genotype scores were constructed for confirmed variants.

FINDINGS

Variants at three loci reached genome-wide significance in discovery cohorts for association with 25-hydroxyvitamin D concentrations, and were confirmed in replication cohorts: 4p12 (overall p=1.9x10(-109) for rs2282679, in GC); 11q12 (p=2.1x10(-27) for rs12785878, near DHCR7); and 11p15 (p=3.3x10(-20) for rs10741657, near CYP2R1). Variants at an additional locus (20q13, CYP24A1) were genome-wide significant in the pooled sample (p=6.0x10(-10) for rs6013897). Participants with a genotype score (combining the three confirmed variants) in the highest quartile were at increased risk of having 25-hydroxyvitamin D concentrations lower than 75 nmol/L (OR 2.47, 95% CI 2.20-2.78, p=2.3x10(-48)) or lower than 50 nmol/L (1.92, 1.70-2.16, p=1.0x10(-26)) compared with those in the lowest quartile.

INTERPRETATION

Variants near genes involved in cholesterol synthesis, hydroxylation, and vitamin D transport affect vitamin D status. Genetic variation at these loci identifies individuals who have substantially raised risk of vitamin D insufficiency.

FUNDING

Full funding sources listed at end of paper (see Acknowledgments).

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to play an important role in genetic susceptibility to common disease. To address this we undertook a large direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed ~19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated ~50% of all common CNVs larger than 500bp. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell-lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease, IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis, and type 1 diabetes, and TSPAN8 for type 2 diabetes, though in each case the locus had previously been identified in SNP-based studies, reflecting our observation that the majority of common CNVs which are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs which can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

International albumin use: 1995 to 2006

Over the last ten years more reliable information regarding the risks and benefits of the use of albumin for fluid resuscitation has emerged. To determine what influence this has had on clinical practice, we sought to document albumin use (from mass of albumin supplied to hospitals) in 16 industrialised countries between 1995 and 2006. Data on national albumin and synthetic colloid use was sought from independent intensive care researchers and albumin issuers. The mass of albumin supplied per 10,000 persons on an annual basis by country and aggregated across the study countries was calculated. Volumes of synthetic colloid supplied per 10,000 persons were calculated. Data were obtained for 15 countries. Albumin use varied significantly between countries and throughout the observation period. Overall, aggregate albumin use decreased from a peak of 2.54 kg per 10,000 persons in 1995 to 1.40 kg per 10,000 persons in 1999; use has remained relatively constant since. Data on supply of synthetic colloids was available in only three countries and varied from 11.7 litres per 10,000 persons in Canada in 1995, to 231.8 litres per 10,000 persons in Denmark in 2004. Between 1995 and 1999 albumin use decreased and has been materially constant since; where data were available, use of synthetic colloids increased. Whether these practice changes have resulted in a net health gain or in harm requires further research.

Analysis of 55 autoimmune disease and type II diabetes loci: further confirmation of chromosomes 4q27, 12q13.2 and 12q24.13 as type I diabetes loci, and support for a new locus, 12q13.3-q14.1

A candidate gene study was conducted on 10 established type II diabetes genes and 45 genes associated with autoimmune diseases, including type I diabetes (T1D), in a maximum of 1410 affected sib-pair families assembled by the Type I Diabetes Genetics Consortium. Associations at P values <10(-3) were found for three known T1D regions at chromosomes 4q27, 12q13.2 and 12q24.13 (http://www.T1DBase.org). Support was obtained for a newly identified T1D candidate locus on chromosome 12q13.3-12q14.1 (rs1678536/KIF5A: P=8.1 x 10(-3); relative risk (RR) for minor allele=0.89, 95% CI=0.82-0.97), which has a separate association from the previously reported T1D candidate locus ERBB3/12q13.2-q13.3. Our new evidence adds to that previously published for the same gene region in a T1D case-control study (rs1678542; P=3.0 x 10(-4); odds ratio (OR)=0.92, 95% CI=0.88-0.96). This region, which contains many genes, has also been associated with rheumatoid arthritis.

Ambulatory blood pressure measurements are related to albumin excretion and are predictive for risk of microalbuminuria in young people with type 1 diabetes

AIMS/HYPOTHESIS

The relationship between BP and microalbuminuria in young people with type 1 diabetes is not completely clear. As microalbuminuria is preceded by a gradual rise in albumin excretion within the normal range, we hypothesised that ambulatory BP (ABP) may be closely related to albumin excretion and progression to microalbuminuria.

METHODS

ABP monitoring (ABPM) was performed in 509 young people with type 1 diabetes (age median [range]: 15.7 [10.7-22.6] years) followed with annual assessments of three early morning urinary albumin:creatinine ratios (ACRs) and HbA(1c). Systolic BP (SBP) and diastolic BP (DBP) and the nocturnal fall in BP were analysed in relation to ACR.

RESULTS

All ABPM variables were significantly related to baseline log(10) ACR (p < 0.001). After the ABPM evaluation, 287 patients were followed for a median of 2.2 (1.0-5.5) years. ABP at baseline was independently related to mean ACR during follow-up. Nineteen initially normoalbuminuric patients developed microalbuminuria after 2.0 (0.2-4.0) years and their baseline daytime DBP was higher than in normoalbuminuric patients (p < 0.001). After adjusting for baseline ACR and HbA(1c), there was an 11% increased risk of microalbuminuria for each 1 mmHg increase in daytime DBP. Forty-eight per cent of patients were non-dippers for SBP and 60% for DBP; however, ACR was not different between dippers and non-dippers and there were no differences in the nocturnal fall in BP between normoalbuminuric and future microalbuminuric patients.

CONCLUSIONS/INTERPRETATION

In this cohort of young people with type 1 diabetes, ABP was significantly related to ACR, and daytime DBP was independently associated with progression to microalbuminuria. Increasing albumin excretion, even in the normal range, may be associated with parallel rises in BP.

Prevalence of abnormal lipid profiles and the relationship with the development of microalbuminuria in adolescents with type 1 diabetes

OBJECTIVE

To explore the prevalence of lipid abnormalities and their relationship with albumin excretion and microalbuminuria in adolescents with type 1 diabetes.

RESEARCH DESIGN AND METHODS

The study population comprised 895 young subjects with type 1 diabetes (490 males); median age at the baseline assessment was 14.5 years (range 10-21.1), and median diabetes duration was 4.8 years (0.2-17). A total of 2,194 nonfasting blood samples were collected longitudinally for determination of total cholesterol, LDL cholesterol, HDL cholesterol, TG, and non-HDL cholesterol. Additional annually collected data on anthropometric parameters, A1C, and albumin-to-creatinine ratio (ACR) were available.

RESULTS

Total cholesterol, LDL cholesterol, HDL cholesterol, and non-HDL cholesterol were higher in females than in males (all P < 0.001). A significant proportion of subjects presented sustained lipid abnormalities during follow-up: total cholesterol >5.2 mmol/l (18.6%), non-HDL cholesterol >3.4 mmol/l (25.9%), TG >1.7 mmol/l (20.1%), and LDL cholesterol >3.4 mmol/l (9.6%). Age and duration were significantly related to all lipid parameters (P < 0.001); A1C was independently related to all parameters (P < 0.001) except HDL cholesterol, whereas BMI SD scores were related to all parameters (P < 0.05) except total cholesterol. Total cholesterol and non-HDL cholesterol were independently related to longitudinal changes in ACR (B coefficient +/- SE): 0.03 +/- 0.01/1 mmol/l, P = 0.009, and 0.32 +/- 0.014/1 mmol/l, P = 0.02, respectively. Overall mean total cholesterol and non-HDL cholesterol were higher in microalbuminuria positive (n = 115) than in normoalbuminuric subjects (n = 780): total cholesterol 4.7 +/- 1.2 vs. 4.5 +/- 0.8 mmol/l (P = 0.04) and non-HDL cholesterol 3.2 +/- 1.2 vs. 2.9 +/- 0.8 mmol/l (P = 0.03).

CONCLUSIONS

In this longitudinal study of adolescents with type 1 diabetes, sustained lipid abnormalities were related to age, duration, BMI, and A1C. Furthermore, ACR was related to both total cholesterol and non-HDL cholesterol, indicating a potential role in the pathogenesis of diabetic nephropathy.

Efficient gene delivery to the adult and fetal CNS using pseudotyped non-integrating lentiviral vectors

Non-integrating lentiviral vectors show considerable promise for gene therapy applications as they persist as long-term episomes in non-dividing cells and diminish risks of insertional mutagenesis. In this study, non-integrating lentiviral vectors were evaluated for their use in the adult and fetal central nervous system of rodents. Vectors differentially pseudotyped with vesicular stomatitis virus, rabies and baculoviral envelope proteins allowed targeting of varied cell populations. Efficient gene delivery to discrete areas of the brain and spinal cord was observed following stereotactic administration. Furthermore, after direct in utero administration (E14), sustained and strong expression was observed 4 months into adulthood. Quantification of transduction and viral copy number was comparable when using non-integrating lentivirus and conventional integrating vector. These data support the use of non-integrating lentiviral vectors as an effective alternative to their integrating counterparts in gene therapy applications, and highlight their potential for treatment of inherited and acquired neurological disorders.

Shared and distinct genetic variants in type 1 diabetes and celiac disease

BACKGROUND

Two inflammatory disorders, type 1 diabetes and celiac disease, cosegregate in populations, suggesting a common genetic origin. Since both diseases are associated with the HLA class II genes on chromosome 6p21, we tested whether non-HLA loci are shared.

METHODS

We evaluated the association between type 1 diabetes and eight loci related to the risk of celiac disease by genotyping and statistical analyses of DNA samples from 8064 patients with type 1 diabetes, 9339 control subjects, and 2828 families providing 3064 parent-child trios (consisting of an affected child and both biologic parents). We also investigated 18 loci associated with type 1 diabetes in 2560 patients with celiac disease and 9339 control subjects.

RESULTS

Three celiac disease loci--RGS1 on chromosome 1q31, IL18RAP on chromosome 2q12, and TAGAP on chromosome 6q25--were associated with type 1 diabetes (P<1.00x10(-4)). The 32-bp insertion-deletion variant on chromosome 3p21 was newly identified as a type 1 diabetes locus (P=1.81x10(-8)) and was also associated with celiac disease, along with PTPN2 on chromosome 18p11 and CTLA4 on chromosome 2q33, bringing the total number of loci with evidence of a shared association to seven, including SH2B3 on chromosome 12q24. The effects of the IL18RAP and TAGAP alleles confer protection in type 1 diabetes and susceptibility in celiac disease. Loci with distinct effects in the two diseases included INS on chromosome 11p15, IL2RA on chromosome 10p15, and PTPN22 on chromosome 1p13 in type 1 diabetes and IL12A on 3q25 and LPP on 3q28 in celiac disease.

CONCLUSIONS

A genetic susceptibility to both type 1 diabetes and celiac disease shares common alleles. These data suggest that common biologic mechanisms, such as autoimmunity-related tissue damage and intolerance to dietary antigens, may be etiologic features of both diseases.

Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci

We carried out a meta-analysis of data from three genome-wide association (GWA) studies of type 1 diabetes (T1D), testing 305,090 SNPs in 3,561 T1D cases and 4,646 controls of European ancestry. We obtained further support for 4q27 (IL2-IL21, P = 1.9 x 10(-8)) and, after genotyping an additional 6,225 cases, 6,946 controls and 2,828 families, convincing evidence for four previously unknown and distinct risk loci in chromosome regions 6q15 (BACH2, P = 4.7 x 10(-12)), 10p15 (PRKCQ, P = 3.7 x 10(-9)), 15q24 (CTSH, P = 3.2 x 10(-15)) and 22q13 (C1QTNF6, P = 2.0 x 10(-8)).