Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Phenotype forecasting with SNPs data through gene-based Bayesian networks

<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p

Evaluation of physical activity before and after respiratory rehabilitation in normal weight individuals with asthma: a feasibility study

Background: Individuals with asthma spend less time engaging in physical activity compared to the general population. Increasing physical activity has become a patient-centered goal for the treatment of treatable traits of individuals with asthma. There are data showing the possible effects of a pulmonary rehabilitation program on physical activity in obese individuals with asthma but not in normal-weight asthmatics. The objective of this feasibility study is to estimate the number of daily steps and time spent on activity in normal-weight individuals with asthma, measured before and after a pulmonary rehabilitation program. Methods: Normal-weight individuals with moderate to severe asthma were evaluated. The individuals measured their daily steps with an accelerometer for 5 days before and after a pulmonary rehabilitation program. The study was registered on ClinicalTrials.gov: NCT05486689. Results: In total, 17 participants were enrolled; one dropout and data on the time in activity of two individuals are missing due to a software error during the download. Data from 16 patients were analyzed. The median number of steps/day at baseline was 5,578 (25th, 75th percentiles = 4,874, 9,685) while the median activity time was 214 min (25th, 75th percentiles = 165, 239). After the rehabilitation program, the number of daily steps increased by a median value of 472 (p-value = 0.561) and the time in activity reduced by 17 min (p-value = 0.357). We also found a significant difference in quality of life, muscle strength, and exercise capacity. Conclusions: The results of this study make it possible to calculate the sample size of future studies whose main outcome is daily steps in normal-weight individuals with asthma. The difficulties encountered in downloading time in activity data do not allow the same for this outcome

Clusters of individuals recovering from an exacerbation of chronic obstructive pulmonary disease and response to in-hospital pulmonary rehabilitation

Introduction and objectives: Due to the present low availability of pulmonary rehabilitation (PR) for individuals recovering from a COPD exacerbation (ECOPD), we need admission priority criteria. We tested the hypothesis that these individuals might be clustered according to baseline characteristics to identify subpopulations with different responses to PR. Methods: Multicentric retrospective analysis of individuals undergone in-hospital PR. Baseline characteristics and outcome measures (six-minute walking test - 6MWT, Medical Research Council scale for dyspnoea -MRC, COPD assessment test -CAT) were used for clustering analysis. Results: Data analysis of 1159 individuals showed that after program, the proportion of individuals reaching the minimal clinically important difference (MCID) was 85.0%, 86.3%, and 65.6% for CAT, MRC, and 6MWT respectively. Three clusters were found (C1-severe: 10.9%; C2-intermediate: 74.4%; C3-mild: 14.7% of cases respectively). Cluster C1-severe showed the worst conditions with the largest post PR improvements in outcome measures; C3-mild showed the least severe baseline conditions, but the smallest improvements. The proportion of participants reaching the MCID in ALL three outcome measures was significantly different among clusters, with C1-severe having the highest proportion of full success (69.0%) as compared to C2-intermediate (48.3%) and C3-mild (37.4%). Participants in C2-intermediate and C1-severe had 1.7- and 4.6-fold increases in the probability to reach the MCID in all three outcomes as compared to those in C3-mild (OR&nbsp;=&nbsp;1.72, 95% confidence interval [95% CI]&nbsp;=&nbsp;1.2 - 2.49, p&nbsp;=&nbsp;0.0035 and OR&nbsp;=&nbsp;4.57, 95% CI&nbsp;=&nbsp;2.68 - 7.91, p &lt; 0.0001 respectively). Conclusions: Clustering analysis can identify subpopulations of individuals recovering from ECOPD associated with different responses to PR. Our results may help in defining priority criteria based on the probability of success of PR

Minimal clinically important difference in barthel index dyspnea in patients with COPD

Background: The Barthel Index dyspnea (BId) is responsive to physiological changes and pulmonary rehabilitation in patients with chronic obstructive pulmonary disease (COPD). However, the minimum clinically important difference (MCID) has not been established yet. Aim: To identify the MCID of BId in patients with COPD stratified according to the presence of chronic respiratory failure (CRF) or not. Materials and Methods: Using the Medical Research Council (MRC) score as an anchor, receiver operating characteristic curves and quantile regression were retrospectively evaluated before and after pulmonary rehabilitation in 2327 patients with COPD (1151 of them with CRF). Results: The median post-rehabilitation changes in BId for all patients were −10 (interquartile range = −17 to −3, p&lt;0.001), correlating significantly with changes in MRC (r = 0.57, 95% CI = 0.53 to 0.59, p&lt;0.001). Comparing different methods of assessment, the MCID ranged from −6.5 to −9 points for patients without and −7.5 to −12 points for patients with CRF. Conclusion: The most conservative estimate of the MCID is −9 points in patients with COPD, without and −12 in those with CRF. This estimate may be useful in the clinical interpretation of data, particularly in response to intervention studies

P132 Uncovering blood biomarkers of Inflammatory Bowel Diseases by Raman spectroscopy and FAP dosage: toward a noninvasive triage of patients in first care diagnostic

Abstract Background Currently, a major point of concern in the management of Inflammatory Bowel Diseases (IBD) is the absence of accurate and specific circulating biomarkers able to drive diagnosis in a timely and noninvasive manner. Aim of the present study was to explore blood biomarkers of IBD by coupling the targeted detection of circulating fibroblast activation protein (FAP), a recognized valuable marker of bowel lesion in IBD, and Raman spectroscopy (RS), a quick and label-free metabolomic technique that provides a real-time biochemical characterization of plasma samples without any previously known target. Methods Blood samples were collected from over 140 patients with IBD and 170 control subjects matched for gender and age. Isolated plasma was analysed by enzyme-linked immunosorbent assay for quantitative detection of circulating form of FAP. RS was performed on dry droplets of plasma, with the aim to decipher specific fingerprint of IBD in peripheral blood. A predictive model was built on FAP and Raman data separately, to determine specificity, sensitivity and accuracy of the two approaches in patients classification. Supervised multivariate model was applied on a subset of 203 patients to discriminate IBD and control subjects based on combined datasets. Results FAP levels were reduced in patients with IBD as compared to controls (p&lt;0.0001). The sensitivity and specificity of FAP were 70% and 84% based on the optimal cutoff (57.6 ng mL-1, AUC=0.78). Raman spectra of IBD plasma revealed significant differences in peaks corresponding to carotenoids, proteins with β-sheet secondary structure, lipids and aromatic amino-acids. A machine learning model was applied on a subset of patients reaching an accuracy of 85% in classifying IBD and control subjects. No statistically significant differences were observed so far between the discriminative performance of the sole RS or the combination of RS and FAP. Conclusion RS and FAP dosage enable new discoveries in the biological fingerprint of IBD plasma and provide novel candidate biomarkers of IBD. Our preliminary results strongly suggest that novel blood-based approaches could represent a fast noninvasive way to triage patents with suspected IBD in first care diagnostic, to be applied prior to further specific evaluation

Lack of replication of genetic associations with human longevity

The exceptional longevity of centenarians is due in part to inherited genetic factors, as deduced from data that show that first degree relatives of centenarians live longer and have reduced overall mortality. In recent years, a number of groups have performed genetic association studies on long-living individuals (LLI) and young controls to identify alleles that are either positively or negatively selected in the centenarian population as consequence of a demographic pressure. Many of the reported studies have shown genetic loci associated with longevity. Of these, with the exception of APOE, none have been convincingly reproduced. We validated our populations by typing the APOE locus. In addition, we used 749 American Caucasian LLI, organized in two independent tiers and 355 American Caucasian controls in the attempt to replicate previously published findings. We tested Klotho (KL)-VS variant (rs952706), Cholesteryl Ester Transfer Protein (CETP) I405V (rs5882), Paraoxonase 1 (PON1) Q192R (rs662), Apolipoprotein C-III (APOC3) -641C/A (rs2542052), Microsomal Transfer Protein (MTP) -493G/T (rs2866164) and apolipoprotein E (APOE) epsilon2 and epsilon4 isoforms, (rs7412 and rs429358) haplotypes respectively. Our results show that, at present, except for APOE, none of the selected genes show association with longevity if carefully tested in a large cohort of LLI and their controls, pointing to the need of larger populations for case-control studies in extreme longevity

BPIFB4 and its longevity-associated haplotype protect from cardiac ischemia in humans and mice

Long-living individuals (LLIs) escape age-related cardiovascular complications until the very last stage of life. Previous studies have shown that a Longevity-Associated Variant (LAV) of the BPI Fold Containing Family B Member 4 (BPIFB4) gene correlates with an extraordinarily prolonged life span. Moreover, delivery of the LAV-BPIFB4 gene exerted therapeutic action in murine models of atherosclerosis, limb ischemia, diabetic cardiomyopathy, and aging. We hypothesize that downregulation of BPIFB4 expression marks the severity of coronary artery disease (CAD) in human subjects, and supplementation of the LAV-BPIFB4 protects the heart from ischemia. In an elderly cohort with acute myocardial infarction (MI), patients with three-vessel CAD were characterized by lower levels of the natural logarithm (Ln) of peripheral blood BPIFB4 (p = 0.0077). The inverse association between Ln BPIFB4 and three-vessel CAD was confirmed by logistic regression adjusting for confounders (Odds Ratio = 0.81, p = 0.0054). Moreover, in infarcted mice, a single administration of LAV-BPIFB4 rescued cardiac function and vascularization. In vitro studies showed that LAV-BPIFB4 protein supplementation exerted chronotropic and inotropic actions on induced pluripotent stem cell (iPSC)-derived cardiomyocytes. In addition, LAV-BPIFB4 inhibited the pro-fibrotic phenotype in human cardiac fibroblasts. These findings provide a strong rationale and proof of concept evidence for treating CAD with the longevity BPIFB4 gene/protein

Association of FOXO3A locus with extreme longevity in the Southern Italian Centenarian Study

A number of potential candidate genes in a variety of biological pathways have been associated with longevity in model organisms. Many of these genes have human homologs and thus have the potential to provide insights into human longevity. Recently, several studies suggested that FOXO3A functions as a key bridge for various signaling pathways that influence aging and longevity. Interestingly, Willcox and colleagues identified several variants that displayed significant genotype-gender interaction in male human longevity. In particular, a nested case-control study was performed in an ethnic Japanese population in Hawaii, and five candidate longevity genes were chosen based on links to the insulin-insulin-like growth factor-1 (IGF-1) signaling pathway. In the Willcox study, the investigated genetic variations (rs2802292, rs2764264, and rs13217795) within the FOXO3A gene were significantly associated with longevity in male centenarians. We validated the association of FOXO3A polymorphisms with extreme longevity in males from the Southern Italian Centenarian Study. Particularly, rs2802288, a proxy of rs2802292, showed the best allelic association-minor allele frequency (MAF) = 0.49; p = 0.003; odds ratio (OR) = 1.51; 95% confidence interval (CI), 1.15-1.98). Furthermore, we undertook a meta-analysis to explore the significance of rs2802292 association with longevity by combining the association results of the current study and the findings coming from the Willcox et al. investigation. Our data point to a key role of FOXO3A in human longevity and confirm the feasibility of the identification of such genes with centenarian-controls studies. Moreover, we hypothesize the susceptibility to the longevity phenotype may well be the result of complex interactions involving genes and environmental factors but also gender