Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress.

Severe stress exposure increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and behavioral changes. Current markerless pose estimation tools allow for more complex and naturalistic behavioral tests. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. Applying this tool to chronic social defeat in male mice, the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter and fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. Moreover, DeepOF aims to facilitate reproducibility and unification of behavioral classification by providing an open-source tool, which can advance the study of rodent individual and social behavior, thereby enabling biological insights and, for example, subsequent drug development for psychiatric disorders.

Validity of a two-component imaging-derived disease activity score for improved assessment of synovitis in early rheumatoid arthritis.

OBJECTIVES: Imaging of joint inflammation provides a standard against which to derive an updated DAS for RA. Our objectives were to develop and validate a DAS based on reweighting the DAS28 components to maximize association with US-assessed synovitis. METHODS: Early RA patients from two observational cohorts (n = 434 and n = 117) and a clinical trial (n = 59) were assessed at intervals up to 104 weeks from baseline; all US scans were within 1 week of clinical exam. There were 899, 163 and 183 visits in each cohort. Associations of combined US grey scale and power Doppler scores (GSPD) with 28 tender joint count and 28 swollen joint count (SJC28), CRP, ESR and general health visual analogue scale were examined in linear mixed model regressions. Cross-validation evaluated model predictive ability. Coefficients learned from training data defined a re-weighted DAS28 that was validated against radiographic progression in independent data (3037 observations; 717 patients). RESULTS: Of the conventional DAS28 components only SJC28 and CRP were associated with GSPD in all three development cohorts. A two-component model including SJC28 and CRP outperformed a four-component model (R2 = 0.235, 0.392, 0.380 vs 0.232, 0.380, 0.375, respectively). The re-weighted two-component DAS28CRP outperformed conventional DAS28 definitions in predicting GSPD (Δtest log-likelihood <-2.6, P < 0.01), Larsen score and presence of erosions. CONCLUSION: A score based on SJC28 and CRP alone demonstrated stronger associations with synovitis and radiographic progression than the original DAS28 and should be considered in research on pathophysiological manifestations of early RA. Implications for clinical management of RA remain to be established.

Serum kidney injury molecule 1 and ß2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes.

AIMS/HYPOTHESIS: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. METHODS: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30-75 ml min-1 [1.73 m]-2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. RESULTS: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and ß2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets. CONCLUSIONS/INTERPRETATION: Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30-75 ml min-1 [1.73 m]-2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.

Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data.

BACKGROUND: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of chronic obstructive pulmonary disease (COPD) exacerbations, with a view to instituting timely treatment. However, algorithms to identify exacerbations result in frequent false-positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving prediction quality. OBJECTIVE: Our objectives were to (1) establish whether machine learning techniques applied to telemonitoring datasets improve prediction of hospital admissions and decisions to start corticosteroids, and (2) determine whether the addition of weather data further improves such predictions. METHODS: We used daily symptoms, physiological measures, and medication data, with baseline demography, COPD severity, quality of life, and hospital admissions from a pilot and large randomized controlled trial of telemonitoring in COPD. We linked weather data from the United Kingdom meteorological service. We used feature selection and extraction techniques for time series to construct up to 153 predictive patterns (features) from symptom, medication, and physiological measurements. We used the resulting variables to construct predictive models fitted to training sets of patients and compared them with common symptom-counting algorithms. RESULTS: We had a mean 363 days of telemonitoring data from 135 patients. The two most practical traditional score-counting algorithms, restricted to cases with complete data, resulted in area under the receiver operating characteristic curve (AUC) estimates of 0.60 (95% CI 0.51-0.69) and 0.58 (95% CI 0.50-0.67) for predicting admissions based on a single day's readings. However, in a real-world scenario allowing for missing data, with greater numbers of patient daily data and hospitalizations (N=57,150, N+=55, respectively), the performance of all the traditional algorithms fell, including those based on 2 days' data. One of the most frequently used algorithms performed no better than chance. All considered machine learning models demonstrated significant improvements; the best machine learning algorithm based on 57,150 episodes resulted in an aggregated AUC of 0.74 (95% CI 0.67-0.80). Adding weather data measurements did not improve the predictive performance of the best model (AUC 0.74, 95% CI 0.69-0.79). To achieve an 80% true-positive rate (sensitivity), the traditional algorithms were associated with an 80% false-positive rate: our algorithm halved this rate to approximately 40% (specificity approximately 60%). The machine learning algorithm was moderately superior to the best symptom-counting algorithm (AUC 0.77, 95% CI 0.74-0.79 vs AUC 0.66, 95% CI 0.63-0.68) at predicting the need for corticosteroids. CONCLUSIONS: Early detection and management of COPD remains an important goal given its huge personal and economic costs. Machine learning approaches, which can be tailored to an individual's baseline profile and can learn from experience of the individual patient, are superior to existing predictive algorithms and show promise in achieving this goal. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number ISRCTN96634935; http://www.isrctn.com/ISRCTN96634935 (Archived by WebCite at http://www.webcitation.org/722YkuhAz).

Plasma N-glycans in colorectal cancer risk.

Aberrant glycosylation has been associated with a number of diseases including cancer. Our aim was to elucidate changes in whole plasma N-glycosylation between colorectal cancer (CRC) cases and controls in one of the largest cohorts of its kind. A set of 633 CRC patients and 478 age and gender matched controls was analysed. Additionally, patients were stratified into four CRC stages. Moreover, N-glycan analysis was carried out in plasma of 40 patients collected prior to the initial diagnosis of CRC. Statistically significant differences were observed in the plasma N-glycome at all stages of CRC, this included a highly significant decrease in relation to the core fucosylated bi-antennary glycans F(6)A2G2 and F(6)A2G2S(6)1 (P < 0.0009). Stage 1 showed a unique biomarker signature compared to stages 2, 3 and 4. There were indications that at risk groups could be identified from the glycome (retrospective AUC = 0.77 and prospective AUC = 0.65). N-glycome biomarkers related to the pathogenic progress of the disease would be a considerable asset in a clinical setting and it could enable novel therapeutics to be developed to target the disease in patients at risk of progression.

Apolipoprotein CIII and N-terminal prohormone b-type natriuretic peptide as independent predictors for cardiovascular disease in type 2 diabetes.

BACKGROUND AND AIMS: Developing sparse panels of biomarkers for cardiovascular disease in type 2 diabetes would enable risk stratification for clinical decision making and selection into clinical trials. We examined the individual and joint performance of five candidate biomarkers for incident cardiovascular disease (CVD) in type 2 diabetes that an earlier discovery study had yielded. METHODS: Apolipoprotein CIII (apoCIII), N-terminal prohormone B-type natriuretic peptide (NT-proBNP), high sensitivity Troponin T (hsTnT), Interleukin-6, and Interleukin-15 were measured in baseline serum samples from the Collaborative Atorvastatin Diabetes trial (CARDS) of atorvastatin versus placebo. Among 2105 persons with type 2 diabetes and median age of 62.9 years (range 39.2-77.3), there were 144 incident CVD (acute coronary heart disease or stroke) cases during the maximum 5-year follow up. We used Cox Proportional Hazards models to identify biomarkers associated with incident CVD and the area under the receiver operating characteristic curves (AUROC) to assess overall model prediction. RESULTS: Three of the biomarkers were singly associated with incident CVD independently of other risk factors; NT-proBNP (Hazard Ratio per standardised unit 2.02, 95% Confidence Interval [CI] 1.63, 2.50), apoCIII (1.34, 95% CI 1.12, 1.60) and hsTnT (1.40, 95% CI 1.16, 1.69). When combined in a single model, only NT-proBNP and apoCIII were independent predictors of CVD, together increasing the AUROC using Framingham risk variables from 0.661 to 0.745. CONCLUSIONS: The biomarkers NT-proBNP and apoCIII substantially increment the prediction of CVD in type 2 diabetes beyond that obtained with the variables used in the Framingham risk score.

Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients.

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10-7 for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.

N-Glycan Profile and Kidney Disease in Type 1 Diabetes.

OBJECTIVE: Poorer glycemic control in type 1 diabetes may alter N-glycosylation patterns on circulating glycoproteins, and these alterations may be linked with diabetic kidney disease (DKD). We investigated associations between N-glycans and glycemic control and renal function in type 1 diabetes. RESEARCH DESIGN AND METHODS: Using serum samples from 818 adults who were considered to have extreme annual loss in estimated glomerular filtration rate (eGFR; i.e., slope) based on retrospective clinical records, from among 6,127 adults in the Scottish Diabetes Research Network Type 1 Bioresource Study, we measured total and IgG-specific N-glycan profiles. This yielded a relative abundance of 39 total (GP) and 24 IgG (IGP) N-glycans. Linear regression models were used to investigate associations between N-glycan structures and HbA1c, albumin-to-creatinine ratio (ACR), and eGFR slope. Models were adjusted for age, sex, duration of type 1 diabetes, and total serum IgG. RESULTS: Higher HbA1c was associated with a lower relative abundance of simple biantennary N-glycans and a higher relative abundance of more complex structures with more branching, galactosylation, and sialylation (GP12, 26, 31, 32, and 34, and IGP19 and 23; all P < 3.79 × 10-4). Similar patterns were seen for ACR and greater mean annual loss of eGFR, which were also associated with fewer of the simpler N-glycans (all P < 3.79 × 10-4). CONCLUSIONS: Higher HbA1c in type 1 diabetes is associated with changes in the serum N-glycome that have elsewhere been shown to regulate the epidermal growth factor receptor and transforming growth factor-ß pathways that are implicated in DKD. Furthermore, N-glycans are associated with ACR and eGFR slope. These data suggest that the role of altered N-glycans in DKD warrants further investigation.

IgG glycan patterns are associated with type 2 diabetes in independent European populations.

BACKGROUND: Type 2 diabetes results from interplay between genetic and acquired factors. Glycans on proteins reflect genetic, metabolic and environmental factors. However, associations of IgG glycans with type 2 diabetes have not been described. We compared IgG N-glycan patterns in type 2 diabetes with healthy subjects. METHODS: In the DiaGene study, a population-based case-control study, (1886 cases and 854 controls) 58 IgG glycan traits were analyzed. Findings were replicated in the population-based CROATIA-Korcula-CROATIA-Vis-ORCADES studies (162 cases and 3162 controls), and meta-analyzed. AUCs of ROC-curves were calculated using 10-fold cross-validation for clinical characteristics, IgG glycans and their combination. RESULTS: After correction for extensive clinical covariates, 5 IgG glycans and 13 derived traits significantly associated with type 2 diabetes in meta-analysis (after Bonferroni correction). Adding IgG glycans to age and sex increased the AUC from 0.542 to 0.734. Adding them to the extensive model did not substantially improve the AUC. The AUC for IgG glycans alone was 0.729. CONCLUSIONS: Several IgG glycans and traits firmly associate with type 2 diabetes, reflecting a pro-inflammatory and biologically-aged state. IgG glycans showed limited improvement of AUCs. However, IgG glycans showed good prediction alone, indicating they may capture information of combined covariates. The associations found may yield insights in type 2 diabetes pathophysiology. GENERAL SIGNIFICANCE: This work shows that IgG glycomic changes have biomarker potential and may yield important insights into pathophysiology of complex public health diseases, illustrated here for the first time in type 2 diabetes.

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies.

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.

GeneImp: Fast Imputation to Large Reference Panels Using Genotype Likelihoods from Ultralow Coverage Sequencing.

We address the task of genotype imputation to a dense reference panel given genotype likelihoods computed from ultralow coverage sequencing as inputs. In this setting, the data have a high-level of missingness or uncertainty, and are thus more amenable to a probabilistic representation. Most existing imputation algorithms are not well suited for this situation, as they rely on prephasing for computational efficiency, and, without definite genotype calls, the prephasing task becomes computationally expensive. We describe GeneImp, a program for genotype imputation that does not require prephasing and is computationally tractable for whole-genome imputation. GeneImp does not explicitly model recombination, instead it capitalizes on the existence of large reference panels-comprising thousands of reference haplotypes-and assumes that the reference haplotypes can adequately represent the target haplotypes over short regions unaltered. We validate GeneImp based on data from ultralow coverage sequencing (0.5×), and compare its performance to the most recent version of BEAGLE that can perform this task. We show that GeneImp achieves imputation quality very close to that of BEAGLE, using one to two orders of magnitude less time, without an increase in memory complexity. Therefore, GeneImp is the first practical choice for whole-genome imputation to a dense reference panel when prephasing cannot be applied, for instance, in datasets produced via ultralow coverage sequencing. A related future application for GeneImp is whole-genome imputation based on the off-target reads from deep whole-exome sequencing.

Automated Integration of a UPLC Glycomic Profile.

Ultra-performance liquid chromatography (UPLC) is the established technology for accurate analysis of IgG Fc N-glycosylation due to its superior sensitivity, resolution, speed, and its capability to provide branch-specific information of glycan species. Correct and cost-efficient preprocessing of chromatographic data is the major prerequisite for subsequent analyses ranging from inference of structural isomers to biomarker discovery and prediction of humoral immune response from characterized changes in glycosylation. The complexity of glycomic chromatograms poses a number of challenges for developing automated data annotation and quantitation algorithms, which frequently necessitated manual or semi-manual approaches to preprocessing, most notably to peak detection and integration. Such procedures are meticulous and time-consuming, and may be a source of confounding due to their dependence on human labelers. Although liquid chromatography is a mature field and a number of methods have been developed for automatic peak detection outside the area of glycomics analysis, we found that hardly any of them are suitable for automatic integration of UPLC glycomic profiles without substantial modifications. In this chapter, we illustrate practical challenges of automatic peak detection of UPLC glycomics chromatograms. We outline a robust, semi-supervised method ACE (Automatic Chromatogram Extraction) for automated alignment and detection of glycan peaks in chromatograms, developed by Pharmatics Limited (UK) in collaboration with Genos Limited (Croatia). Application of the tool requires minimal human interference, which results in a significant reduction in the time and cost of IgG glycomics signal integration using Waters Acquity UPLC instrument (Milford, MA, USA) in several human cohorts with blind technical replicas.

Glycosylation of plasma IgG in colorectal cancer prognosis.

In this study we demonstrate the potential value of Immunoglobulin G (IgG) glycosylation as a novel prognostic biomarker of colorectal cancer (CRC). We analysed plasma IgG glycans in 1229 CRC patients and correlated with survival outcomes. We assessed the predictive value of clinical algorithms and compared this to algorithms that also included glycan predictors. Decreased galactosylation, decreased sialylation (of fucosylated IgG glycan structures) and increased bisecting GlcNAc in IgG glycan structures were strongly associated with all-cause (q < 0.01) and CRC mortality (q = 0.04 for galactosylation and sialylation). Clinical algorithms showed good prediction of all-cause and CRC mortality (Harrell's C: 0.73, 0.77; AUC: 0.75, 0.79, IDI: 0.02, 0.04 respectively). The inclusion of IgG glycan data did not lead to any statistically significant improvements overall, but it improved the prediction over clinical models for stage 4 patients with the shortest follow-up time until death, with the median gain in the test AUC of 0.08. These glycan differences are consistent with significantly increased IgG pro-inflammatory activity being associated with poorer CRC prognosis, especially in late stage CRC. In the absence of validated biomarkers to improve upon prognostic information from existing clinicopathological factors, the potential of these novel IgG glycan biomarkers merits further investigation.

Biomarkers of rapid chronic kidney disease progression in type 2 diabetes.

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, ß2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models.

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes.

AIMS/HYPOTHESIS: We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes. METHODS: In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC). RESULTS: Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA1c. CONCLUSIONS/INTERPRETATION: We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.

Model selection approach suggests causal association between 25-hydroxyvitamin D and colorectal cancer.

INTRODUCTION: Vitamin D deficiency has been associated with increased risk of colorectal cancer (CRC), but causal relationship has not yet been confirmed. We investigate the direction of causation between vitamin D and CRC by extending the conventional approaches to allow pleiotropic relationships and by explicitly modelling unmeasured confounders. METHODS: Plasma 25-hydroxyvitamin D (25-OHD), genetic variants associated with 25-OHD and CRC, and other relevant information was available for 2645 individuals (1057 CRC cases and 1588 controls) and included in the model. We investigate whether 25-OHD is likely to be causally associated with CRC, or vice versa, by selecting the best modelling hypothesis according to Bayesian predictive scores. We examine consistency for a range of prior assumptions. RESULTS: Model comparison showed preference for the causal association between low 25-OHD and CRC over the reverse causal hypothesis. This was confirmed for posterior mean deviances obtained for both models (11.5 natural log units in favour of the causal model), and also for deviance information criteria (DIC) computed for a range of prior distributions. Overall, models ignoring hidden confounding or pleiotropy had significantly poorer DIC scores. CONCLUSION: Results suggest causal association between 25-OHD and colorectal cancer, and support the need for randomised clinical trials for further confirmations.

Extending admixture mapping to nuclear pedigrees: application to sarcoidosis.

We describe statistical methods that extend the application of admixture mapping from unrelated individuals to nuclear pedigrees, allowing existing pedigree-based collections to be fully exploited. Computational challenges have been overcome by developing a fast algorithm that exploits the factorial structure of the underlying model of ancestry transitions. This has been implemented as an extension of the program ADMIXMAP. We demonstrate the application of the method to a study of sarcoidosis in African Americans that has previously been analyzed only as an admixture mapping study restricted to unrelated individuals. Although the ancestry signals detected in this pedigree analysis are generally similar to those detected in the earlier analysis of unrelated cases, we are able to extract more information and this yields a much sharper exclusion map; using the classical criterion of an LOD score of minus 2, the pedigree analysis is able to exclude a risk ratio of 2 or more associated with African ancestry over 96% of the genome, compared with only 83% in the earlier analysis of unrelated individuals only. Although the pedigree extension of ADMIXMAP can use ancestry-informative markers only at relatively low density, it can use imputed ancestry states from programs such as WINPOP or HAPMIX that use dense SNP marker genotypes for admixture mapping. This extends both the efficiency and the range of application of this powerful gene mapping method.

The association of dietary intake of purine-rich vegetables, sugar-sweetened beverages and dairy with plasma urate, in a cross-sectional study.

INTRODUCTION: Hyperuricemia is a strong risk factor for gout. The incidence of gout and hyperuricemia has increased recently, which is thought to be, in part, due to changes in diet and lifestyle. Objective of this study was to investigate the association between plasma urate concentration and: a) food items: dairy, sugar-sweetened beverages (SSB) and purine-rich vegetables; b) related nutrients: lactose, calcium and fructose. METHODS: A total of 2,076 healthy participants (44% female) from a population-based case-control study in Scotland (1999-2006) were included in this study. Dietary data was collected using a semi-quantitative food frequency questionnaire (FFQ). Nutrient intake was calculated using FFQ and composition of foods information. Urate concentration was measured in plasma. RESULTS: Mean urate concentration was 283.8±72.1 mmol/dL (females: 260.1±68.9 mmol/dL and males: 302.3±69.2 mmol/dL). Using multivariate regression analysis we found that dairy, calcium and lactose intakes were inversely associated with urate (p = 0.008, p = 0.003, p = 0.0007, respectively). Overall SSB consumption was positively associated with urate (p = 0.008), however, energy-adjusted fructose intake was not associated with urate (p = 0.66). The intake of purine-rich vegetables was not associated to plasma urate (p = 0.38). CONCLUSIONS: Our results suggest that limiting purine-rich vegetables intake for lowering plasma urate may be ineffectual, despite current recommendations. Although a positive association between plasma urate and SSB consumption was found, there was no association with fructose intake, suggesting that fructose is not the causal agent underlying the SSB-urate association. The abundant evidence supporting the inverse association between plasma urate concentration and dairy consumption should be reflected in dietary guidelines for hyperuricemic individuals and gout patients. Further research is needed to establish which nutrients and food products influence plasma urate concentration, to inform the development of evidence-based dietary guidelines.

Abundant pleiotropy in human complex diseases and traits.

We present a systematic review of pleiotropy among SNPs and genes reported to show genome-wide association with common complex diseases and traits. We find abundant evidence of pleiotropy; 233 (16.9%) genes and 77 (4.6%) SNPs show pleiotropic effects. SNP pleiotropic status was associated with gene location (p = 0.024; pleiotropic SNPs more often exonic [14.5% versus 4.9% for nonpleiotropic, trait-associated SNPs] and less often intergenic [15.8% versus 23.6%]), "predicted transcript consequence" (p = 0.001; pleiotropic SNPs more often predicted to be structurally deleterious [5% versus 0.4%] but not more often in regulatory sequences), and certain disease classes. We develop a method to calculate the likelihood that pleiotropic links between traits occurred more often than expected and demonstrate that this approach can identify etiological links that are already known (such as between fetal hemoglobin and malaria risk) and those that are not yet established (e.g., between plasma campesterol levels and gallstones risk; and between immunoglobulin A and juvenile idiopathic arthritis). Examples of pleiotropy will accumulate over time, but it is already clear that pleiotropy is a common property of genes and SNPs associated with disease traits, and this will have implications for identification of molecular targets for drug development, future genetic risk-profiling, and classification of diseases.