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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 (NMDD = 231; Ncontrol = 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.

Type 1 diabetes and celiac disease, both of which are associated with HLA class II genes, cosegregate in populations, suggesting a common genetic origin. In this article, the authors tested whether any non-HLA loci are shared. They report susceptibility alleles shared by both diseases, indicating that common biologic mechanisms underlie these immune-mediated disorders. Type 1 diabetes and celiac disease cosegregate in populations, suggesting a common genetic origin. These authors report susceptibility alleles shared by both diseases, indicating that common biologic mechanisms underlie these immune-mediated disorders. Type 1 diabetes is caused by autoimmune destruction of the insulin-producing beta cells in the pancreatic islets. The disease affects approximately 0.4% of persons of European origin and is strongly clustered in families. The major susceptibility genes — the HLA class II loci, HLA-DQB1 and HLA-DRB1 on chromosome 6p21 — act in combination with many other non-HLA loci across the genome, 1 , 2 with unknown environmental factors playing a major role. 3 – 6 Celiac disease, which results from an immune, inflammatory reaction in the small intestine to proteins in ingested barley, wheat, and rye gluten, occurs in approximately 0.1% of persons of . . .

Jason Cooper and colleagues identify four new risk loci for type 1 diabetes through a meta-analysis of data from three genome-wide association studies, with replication in additional case-control and family-based samples, providing further insights into the genetic risk factors underlying this disease. 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 × 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 × 10 −12 ), 10p15 ( PRKCQ , P = 3.7 × 10 −9 ), 15q24 ( CTSH , P = 3.2 × 10 −15 ) and 22q13 ( C1QTNF6 , P = 2.0 × 10 −8 ).

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.

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.

In this study we report convincing statistical support for a sixth type 1 diabetes (T1D) locus in the innate immunity viral RNA receptor gene region IFIH1 (also known as mda-5 or Helicard ) on chromosome 2q24.3. We found the association in an interim analysis of a genome-wide nonsynonymous SNP (nsSNP) scan, and we validated it in a case-control collection and replicated it in an independent family collection. In 4,253 cases, 5,842 controls and 2,134 parent-child trio genotypes, the risk ratio for the minor allele of the nsSNP rs1990760 A → G (A946T) was 0.86 (95% confidence interval = 0.82–0.90) at P = 1.42 × 10 −10 .

Genome-wide association studies are now identifying disease-associated chromosome regions. However, even after convincing replication, the localization of the causal variant(s) requires comprehensive resequencing, extensive genotyping and statistical analyses in large sample sets leading to targeted functional studies. Here, we have localized the type 1 diabetes (T1D) association in the interleukin 2 receptor alpha ( IL2RA ) gene region to two independent groups of SNPs, spanning overlapping regions of 14 and 40 kb, encompassing IL2RA intron 1 and the 5′ regions of IL2RA and RBM17 (odds ratio = 2.04, 95% confidence interval = 1.70–2.45; P = 1.92 × 10 −28 ; control frequency = 0.635). Furthermore, we have associated IL2RA T1D susceptibility genotypes with lower circulating levels of the biomarker, soluble IL-2RA ( P = 6.28 × 10 −28 ), suggesting that an inherited lower immune responsiveness predisposes to T1D.

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

In a previous paper we have shown that, when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping, scoring inaccuracies can lead to differential misclassification and, consequently, to increased false-positive rates. Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets. Here, we describe methodological improvements to minimise such biases. These fall into two categories: improvements to the basic clustering methods for identifying genotypes from fluorescence intensities, and use of \"fuzzy\" calls in association tests in order to make appropriate allowance for call uncertainty. We find that the main improvement is a modification of the calling algorithm that links the clustering of cases and controls while allowing for different DNA sourcing. We also find that, in the presence of different DNA sourcing, biases associated with missing data can increase the false-positive rate. Therefore, we propose the use of \"fuzzy\" calls to deal with uncertain genotypes that would otherwise be labeled as missing.

Few serum biomarker tests are implemented in clinical practice and recent reports raise concerns about poor reproducibility of biomarker studies. Here, we investigated the potential role of sex and female hormonal status in this widespread irreproducibility. We examined 171 serum proteins and small molecules measured in 1,676 participants from the Netherlands Study of Depression and Anxiety. Concentrations of 96 molecules varied with sex and 66 molecules varied between oral contraceptive pill users, postmenopausal females and females in the follicular and luteal phases of the menstrual cycle (FDR-adjusted p -value <0.05). Simulations of biomarker studies yielded up to 40% false discoveries when patient and control groups were not matched for sex and up to 41% false discoveries when premenopausal females were not matched for oral contraceptive pill use. High accuracy (over 90%) classification tools were developed to label samples with sex and female hormonal status where this information was not collected.