Maternal asthma symptoms during pregnancy on child behaviour and executive function: A Bayesian phenomics approach.

OBJECTIVE: Maternal history of inflammatory conditions has been linked to offspring developmental and behavioural outcomes. This phenomenon may be explained by the maternal immune activation (MIA) hypothesis, which posits that dysregulation of the gestational immune environment affects foetal neurodevelopment. The timing of inflammation is critical. We aimed to understand maternal asthma symptoms during pregnancy, in contrast with paternal asthma symptoms during the same period, on child behaviour problems and executive function in a population-based cohort. METHODS: Data were obtained from 844 families from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort. Parent asthma symptoms during the prenatal period were reported. Asthma symptoms in children were reported longitudinally from two to five years old, while behavioural problems and executive functioning were obtained at seven years old. Parent and child measures were compared between mothers with and without prenatal asthma symptoms. Generalized linear and Bayesian phenomics models were used to determine the relation between parent or child asthma symptoms and child outcomes. RESULTS: Children of mothers with prenatal asthma symptoms had greater behavioural and executive problems than controls (Cohen's d: 0.43-0.75; all p < 0.05). This association remained after adjustments for emerging asthma symptoms during the preschool years and fathers' asthma symptoms during the prenatal period. After adjusting for dependence between child outcomes, the Bayesian phenomics model showed that maternal prenatal asthma symptoms were associated with child internalising symptoms and higher-order executive function, while child asthma symptoms were associated with executive function skills. Paternal asthma symptoms during the prenatal period were not associated with child outcomes. CONCLUSIONS: Associations between child outcomes and maternal but not paternal asthma symptoms during the prenatal period suggests a role for MIA. These findings need to be validated in larger samples, and further research may identify behavioural and cognitive profiles of children with exposure to MIA.

Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity.

The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

Bayesian clustering of multiple zero-inflated outcomes.

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Genome-wide association study for antibody response to Mycobacterium avium ssp. paratubeculosis in goats.

Mycobacterium avium ssp. paratuberculosis (MAP), causes Johne's disease (JD), or paratuberculosis, a chronic enteritis of ruminants, which in goats is characterized by ileal lesions. The work described here is a case-control association study using the Illumina Caprine SNP50 BeadChip to unravel the genes involved in susceptibility of goats to JD. Goats in herds with a high occurrence of Johne's disease were classified as healthy or infected based on the level of serum antibodies against MAP, and 331 animals were selected for the association study. Goats belonged to the Jonica (157) and Siriana breeds (174). Whole-genome association analysis identified one region suggestive of significance associated with an antibody response to MAP on chromosome 7 (p-value = 1.23 × 10-5 ). These results provide evidence for genetic loci involved in the antibody response to MAP in goats.

Joint modeling of recurrent events and survival: a Bayesian non-parametric approach.

Heart failure (HF) is one of the main causes of morbidity, hospitalization, and death in the western world, and the economic burden associated with HF management is relevant and expected to increase in the future. We consider hospitalization data for HF in the most populated Italian Region, Lombardia. Data were extracted from the administrative data warehouse of the regional healthcare system. The main clinical outcome of interest is time to death and research focus is on investigating how recurrent hospitalizations affect the time to event. The main contribution of the article is to develop a joint model for gap times between consecutive rehospitalizations and survival time. The probability models for the gap times and for the survival outcome share a common patient specific frailty term. Using a flexible Dirichlet process model for %Bayesian nonparametric prior as the random-effects distribution accounts for patient heterogeneity in recurrent event trajectories. Moreover, the joint model allows for dependent censoring of gap times by death or administrative reasons and for the correlations between different gap times for the same individual. It is straightforward to include covariates in the survival and/or recurrence process through the specification of appropriate regression terms. The main advantages of the proposed methodology are wide applicability, ease of interpretation, and efficient computations. Posterior inference is implemented through Markov chain Monte Carlo methods.

A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study.

Statistical analysis of questionnaire data is often performed employing techniques from item-response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads.

Bayesian splines versus fractional polynomials in network meta-analysis.

BACKGROUND: Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. METHODS: In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. RESULTS: We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. CONCLUSIONS: The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia.

We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete distributions. This process is defined by including a suitable Beta regression framework within a generalized Dirichlet process to induce dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation, and availability of efficient MCMC algorithms. The motivation for this work is the study of the impact of asparginage metabolism on lipid levels in a group of pediatric patients treated for acute lymphoblastic leukemia.

A comparative review of network meta-analysis models in longitudinal randomized controlled trial.

Network meta-analysis (NMA) technique extends the standard meta-analysis methods, allowing pairwise comparison of all treatments in a network in the absence of head-to-head comparisons. Traditional NMA models consider a single endpoint for each trial. However, in many cases, trials in the network have different durations and/or report data at multiple time points. Moreover, these time points are often not the same for all trials. In this work, we review the most relevant methods that incorporate multiple time points and allow indirect comparisons of treatment effects across different longitudinal studies. In particular, we focus on the mixed treatment comparison developed by Dakin et al,[10] on the Bayesian evidence synthesis techniques-integrated two-component prediction developed by Ding et al,[11] and on the more recent method based on fractional polynomials by Jansen et al.[12] We highlight the main features of each model and illustrate them in simulations and in a real data application. Our study shows that methods based on fractional polynomials offer a flexible modeling strategy in most applications.

A Bayesian semiparametric Markov regression model for juvenile dermatomyositis.

Juvenile dermatomyositis (JDM) is a rare autoimmune disease that may lead to serious complications, even to death. We develop a 2-state Markov regression model in a Bayesian framework to characterise disease progression in JDM over time and gain a better understanding of the factors influencing disease risk. The transition probabilities between disease and remission state (and vice versa) are a function of time-homogeneous and time-varying covariates. These latter types of covariates are introduced in the model through a latent health state function, which describes patient-specific health over time and accounts for variability among patients. We assume a nonparametric prior based on the Dirichlet process to model the health state function and the baseline transition intensities between disease and remission state and vice versa. The Dirichlet process induces a clustering of the patients in homogeneous risk groups. To highlight clinical variables that most affect the transition probabilities, we perform variable selection using spike and slab prior distributions. Posterior inference is performed through Markov chain Monte Carlo methods. Data were made available from the UK JDM Cohort and Biomarker Study and Repository, hosted at the UCL Institute of Child Health.

Recalcitrant chronic bladder pain and recurrent cystitis but negative urinalysis: What should we do?

PURPOSE: Lower urinary tract symptoms (LUTS) may be associated with chronic urinary tract infection (UTI) undetected by routine diagnostic tests. Antimicrobial therapy might confer benefit for these patients. MATERIALS AND METHODS: Over 10 years, we treated patients with chronic LUTS. Pyuria was adopted as the principal biomarker of infection. Urinary leucocyte counts were recorded from microscopy of fresh midstream urine (MSU) samples. Antibiotics were prescribed and the prescription adjusted to achieve a measurable clinical response and a reduction in pyuria. RESULTS: We treated 624 women [mean age = 53.4 years; standard deviation (SD) = 18] with chronic LUTS and pyuria. Mean duration of symptoms prior to presentation was 6.5 years. Only 16% of MSU cultures submitted were positive (≥105 cfu ml-1). Mean treatment length was 383 days [SD = 347; 95% confidence interval (CI) = 337-428]. Treatment was associated with a reduction in total LUTS (F = 98; p = 0.0001), 24-h frequency (F = 75; p = 0.0001), urinary urgency (F = 90; p = 0.0001), lower urinary tract pain (F = 108; p = 0.0001), voiding symptoms (F = 10; p = 0.002), and pyuria (F = 15.4; p = 0.0001). Full-dose first-generation antibiotics for UTI, such as cefalexin, nitrofurantoin, or trimethoprim, were combined with methenamine hippurate. We recorded 475 adverse events (AEs) during 273,762 treatment days. There was only one serious adverse event (SAE). We observed no increase in the proportion of resistant bacterial isolates. CONCLUSION: This large case series demonstrates that patients with chronic LUTS and pyuria experience symptom regression and a reduction in urinary tract inflammation associated with antimicrobial therapy. Disease regression was achieved with a low frequency of AEs. These results provide preliminary data to inform a future randomized controlled trial (RCT).

Lower urinary tract symptoms that predict microscopic pyuria.

INTRODUCTION AND HYPOTHESIS: Urinary dipsticks and culture analyses of a mid-stream urine specimen (MSU) at 105 cfu ml-1 of a known urinary pathogen are considered the gold standard investigations for diagnosing urinary tract infection (UTI). However, the reliability of these tests has been much criticised and they may mislead. It is now widely accepted that pyuria (≥1 WBC µl-1) detected by microscopy of a fresh unspun, unstained specimen of urine is the best biological indicator of UTI available. We aimed to scrutinise the greater potential of symptoms analysis in detecting pyuria and UTI. METHODS: Lower urinary tract symptom (LUTS) descriptions were collected from patients with chronic lower urinary tract symptoms referred to a tertiary referral unit. The symptoms informed a 39-question inventory, grouped into storage, voiding, stress incontinence and pain symptoms. All questions sought a binary yes or no response. A bespoke software package was developed to collect the data. The study was powered to a sample of at least 1,990 patients, with sufficient power to analyse 39 symptoms in a linear model with an effect size of Cohen's f2 = 0.02, type 1 error probability = 0.05; and power (1-ß); 95% where ß is the probability of type 2 error). The inventory was administered to 2,050 female patients between August 2004 and November 2011. The data were collated and the following properties assessed: internal consistency, test-retest reliability, inter-observer reliability, internal responsiveness, external responsiveness, construct validity analysis and a comparison with the International Consultation on Incontinence Modular Questionnaire for female lower urinary tract symptoms (ICIQ-FLUTS). The dependent variable used as a surrogate marker of UTI was microscopic pyuria. An MSU sample was sent for routine culture. RESULTS: The symptoms proved reliable predictors of microscopic pyuria. In particular, voiding symptoms correlated well with microscopic pyuria (χ2 = 88, df = 1, p < 0.001). The symptom inventory has significant psychometric characteristics as below: test-retest reliability: Cronbach's alpha was 0.981; inter-observer reliability, Cronbach's alpha was 0.995, internal responsiveness F = 221, p < 0.001, external responsiveness F = 359, df = 5, p < 0.001. The correlation coefficients for the domains of the ICIQ-FLUTS were around R = 0.5, p < 0.001. CONCLUSION: This symptoms score performed well on the standard, psychometric validation. The score changed in response to treatment and in a direction appropriate to the changes in microscopic pyuria. It correlated with measures of quality of life. It would seem to make a good candidate for monitoring treatment progress in ordinary clinical practice.

A genome-wide association study and biological pathway analysis of epilepsy prognosis in a prospective cohort of newly treated epilepsy.

We present the analysis of a prospective multicentre study to investigate genetic effects on the prognosis of newly treated epilepsy. Patients with a new clinical diagnosis of epilepsy requiring medication were recruited and followed up prospectively. The clinical outcome was defined as freedom from seizures for a minimum of 12 months in accordance with the consensus statement from the International League Against Epilepsy (ILAE). Genetic effects on remission of seizures after starting treatment were analysed with and without adjustment for significant clinical prognostic factors, and the results from each cohort were combined using a fixed-effects meta-analysis. After quality control (QC), we analysed 889 newly treated epilepsy patients using 472 450 genotyped and 6.9 × 10(6) imputed single-nucleotide polymorphisms. Suggestive evidence for association (defined as Pmeta < 5.0 × 10(-7)) with remission of seizures after starting treatment was observed at three loci: 6p12.2 (rs492146, Pmeta = 2.1 × 10(-7), OR[G] = 0.57), 9p23 (rs72700966, Pmeta = 3.1 × 10(-7), OR[C] = 2.70) and 15q13.2 (rs143536437, Pmeta = 3.2 × 10(-7), OR[C] = 1.92). Genes of biological interest at these loci include PTPRD and ARHGAP11B (encoding functions implicated in neuronal development) and GSTA4 (a phase II biotransformation enzyme). Pathway analysis using two independent methods implicated a number of pathways in the prognosis of epilepsy, including KEGG categories 'calcium signaling pathway' and 'phosphatidylinositol signaling pathway'. Through a series of power curves, we conclude that it is unlikely any single common variant explains >4.4% of the variation in the outcome of newly treated epilepsy.

Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events.

Risk prediction models are used to predict a clinical outcome for patients using a set of predictors. We focus on predicting low-dimensional binary outcomes typically arising in epidemiology, health services and public health research where logistic regression is commonly used. When the number of events is small compared with the number of regression coefficients, model overfitting can be a serious problem. An overfitted model tends to demonstrate poor predictive accuracy when applied to new data. We review frequentist and Bayesian shrinkage methods that may alleviate overfitting by shrinking the regression coefficients towards zero (some methods can also provide more parsimonious models by omitting some predictors). We evaluated their predictive performance in comparison with maximum likelihood estimation using real and simulated data. The simulation study showed that maximum likelihood estimation tends to produce overfitted models with poor predictive performance in scenarios with few events, and penalised methods can offer improvement. Ridge regression performed well, except in scenarios with many noise predictors. Lasso performed better than ridge in scenarios with many noise predictors and worse in the presence of correlated predictors. Elastic net, a hybrid of the two, performed well in all scenarios. Adaptive lasso and smoothly clipped absolute deviation performed best in scenarios with many noise predictors; in other scenarios, their performance was inferior to that of ridge and lasso. Bayesian approaches performed well when the hyperparameters for the priors were chosen carefully. Their use may aid variable selection, and they can be easily extended to clustered-data settings and to incorporate external information.

Integrating dynamic mixed-effect modelling and penalized regression to explore genetic association with pharmacokinetics.

CONTEXT: In a previous work, we have shown that penalized regression approaches can allow many genetic variants to be incorporated into sophisticated pharmacokinetic (PK) models in a way that is both computationally and statistically efficient. The phenotypes were the individual model parameter estimates, obtained a posteriori of the model fit and known to be sensitive to the study design. OBJECTIVE: The aim of this study was to propose an integrated approach in which genetic effect sizes are estimated simultaneously with the PK model parameters, which should improve the estimate precision and reduce sensitivity to study design. METHODS: A total of 200 data sets were simulated under the null and each of the following three alternative scenarios: (i) a phase II study with N=300 participants and n=6 sampling times, wherein six unobserved causal variants affect the drug elimination clearance; (ii) the addition of participants with a residual concentration collected in clinical routine (N=300, n=6 plus N=700, n=1); and (iii) a phase II study (N=300, n=6) in which four unobserved causal variants affect two different model parameters. RESULTS: In all scenarios the integrated approach detected fewer false positives. In scenario (i), true-positive rates were low and the stepwise procedure outperformed the integrated approach. In scenario (ii), approaches performed similarly and rates were higher. In scenario (iii), the integrated approach outperformed the stepwise procedure. CONCLUSION: A PK phase II study with N=300 lacks the power to detect genetic effects on PK using genetic arrays. Our approach can simultaneously analyse phase II and clinical routine data and identify when genetic variants affect multiple PK parameters.

Urinary ATP as an indicator of infection and inflammation of the urinary tract in patients with lower urinary tract symptoms.

BACKGROUND: Adenosine-5'-triphosphate (ATP) is a neurotransmitter and inflammatory cytokine implicated in the pathophysiology of lower urinary tract disease. ATP additionally reflects microbial biomass thus has potential as a surrogate marker of urinary tract infection (UTI). The optimum clinical sampling method for ATP urinalysis has not been established. We tested the potential of urinary ATP in the assessment of lower urinary tract symptoms, infection and inflammation, and validated sampling methods for clinical practice. METHODS: A prospective, blinded, cross-sectional observational study of adult patients presenting with lower urinary tract symptoms (LUTS) and asymptomatic controls, was conducted between October 2009 and October 2012. Urinary ATP was assayed by a luciferin-luciferase method, pyuria counted by microscopy of fresh unspun urine and symptoms assessed using validated questionnaires. The sample collection, storage and processing methods were also validated. RESULTS: 75 controls and 340 patients with LUTS were grouped as without pyuria (n = 100), pyuria 1-9 wbc µl(-1) (n = 120) and pyuria ≥10 wbc µl(-1) (n = 120). Urinary ATP was higher in association with female gender, voiding symptoms, pyuria greater than 10 wbc µl(-1) and negative MSU culture. ROC curve analysis showed no evidence of diagnostic test potential. The urinary ATP signal decayed with storage at 23°C but was prevented by immediate freezing at ≤ -20°C, without boric acid preservative and without the need to centrifuge urine prior to freezing. CONCLUSIONS: Urinary ATP may have a role as a research tool but is unconvincing as a surrogate, clinical diagnostic marker.

Ridge regression in prediction problems: automatic choice of the ridge parameter.

To date, numerous genetic variants have been identified as associated with diverse phenotypic traits. However, identified associations generally explain only a small proportion of trait heritability and the predictive power of models incorporating only known-associated variants has been small. Multiple regression is a popular framework in which to consider the joint effect of many genetic variants simultaneously. Ordinary multiple regression is seldom appropriate in the context of genetic data, due to the high dimensionality of the data and the correlation structure among the predictors. There has been a resurgence of interest in the use of penalised regression techniques to circumvent these difficulties. In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems. One challenge in the application of ridge regression is the choice of the ridge parameter that controls the amount of shrinkage of the regression coefficients. We present a method to determine the ridge parameter based on the data, with the aim of good performance in high-dimensional prediction problems. We establish a theoretical justification for our approach, and demonstrate its performance on simulated genetic data and on a real data example. Fitting a ridge regression model to hundreds of thousands to millions of genetic variants simultaneously presents computational challenges. We have developed an R package, ridge, which addresses these issues. Ridge implements the automatic choice of ridge parameter presented in this paper, and is freely available from CRAN.

Dynamics of the risk of smoking-induced lung cancer: a compartmental hidden Markov model for longitudinal analysis.

BACKGROUND: To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer. METHODS: The model was fitted on a case-control study nested in the European Prospective Investigation into Cancer and Nutrition study, including 757 incident cases and 1524 matched controls. Estimation was done through a Markov Chain Monte Carlo algorithm, and simulations based on the posterior estimates of the parameters were used to provide measures of model fit. We performed sensitivity analyses to assess robustness of our findings. RESULTS: After adjusting for its impact on exposure duration, age was not found to independently drive the risk of lung carcinogenesis, whereas age at starting smoking in ever-smokers and time since cessation in former smokers were found to be influential. Our data did not support an age-dependent time to diagnosis. The estimated time between onset of malignancy and clinical diagnosis ranged from 2 to 4 years. Our approach yielded good performance in reconstructing individual trajectories in both cases (sensitivity >90%) and controls (sensitivity >80%). CONCLUSION: Our compartmental model enabled us to identify time-varying predictors of risk and provided us with insights into the dynamics of smoking-induced lung carcinogenesis. Its flexible and general formulation enables the future incorporation of disease states, as measured by intermediate markers, into the modeling of the natural history of cancer, suggesting a large range of applications in chronic disease epidemiology.

Human metabolic phenotype diversity and its association with diet and blood pressure.

Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.

Variability and Number of Circulating Complementary Sex Determiner (Csd) Alleles in a Breeding Population of Italian Honeybees under Controlled Mating.

In Apis mellifera, csd is the primary gene involved in sex determination: haploid hemizygous eggs develop as drones, while females develop from eggs heterozygous for the csd gene. If diploid eggs are homozygous for the csd gene, diploid drones will develop, but will be eaten by worker bees before they are born. Therefore, high csd allelic diversity is a priority for colony survival and breeding. This study aims to investigate the variability of the hypervariable region (HVR) of the csd gene in bees sampled in an apiary under a selection scheme. To this end, an existing dataset of 100 whole-genome sequences was analyzed with a validated pipeline based on de novo assembly of sequences within the HVR region. In total, 102 allelic sequences were reconstructed and translated into amino acid sequences. Among these, 47 different alleles were identified, 44 of which had previously been observed, while 3 are novel alleles. The results show a high variability in the csd region in this breeding population of honeybees.