Metabolomics-Based Discovery of Diagnostic Biomarkers for Onchocerciasis

Background

Development of robust, sensitive, and reproducible diagnostic tests for understanding the epidemiology of neglected tropical diseases is an integral aspect of the success of worldwide control and elimination programs. In the treatment of onchocerciasis, clinical diagnostics that can function in an elimination scenario are non-existent and desperately needed. Due to its sensitivity and quantitative reproducibility, liquid chromatography-mass spectrometry (LC-MS) based metabolomics is a powerful approach to this problem.

Methodology/Principal Findings

Analysis of an African sample set comprised of 73 serum and plasma samples revealed a set of 14 biomarkers that showed excellent discrimination between Onchocerca volvulus–positive and negative individuals by multivariate statistical analysis. Application of this biomarker set to an additional sample set from onchocerciasis endemic areas where long-term ivermectin treatment has been successful revealed that the biomarker set may also distinguish individuals with worms of compromised viability from those with active infection. Machine learning extended the utility of the biomarker set from a complex multivariate analysis to a binary format applicable for adaptation to a field-based diagnostic, validating the use of complex data mining tools applied to infectious disease biomarker discovery and diagnostic development.

Conclusions/Significance

An LC-MS metabolomics-based diagnostic has the potential to monitor the progression of onchocerciasis in both endemic and non-endemic geographic areas, as well as provide an essential tool to multinational programs in the ongoing fight against this neglected tropical disease. Ultimately this technology can be expanded for the diagnosis of other filarial and/or neglected tropical diseases.     

Serum metabolomics for the diagnosis and classification of myasthenia gravis

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease with few reliable diagnostic measures. Therefore, it is great important to explore novel tools for the diagnosis of MG. In this study, a serum metabolomic approach based on LC?CMS in combination with multivariate statistical analyses was used to identify and classify patients with various grades of MG. Serum samples from 42 MG patients and 16 healthy volunteers were analyzed by liquid chromatography Fourier transform mass spectrometry (LC-FTMS). MG patients were clearly distinguished from healthy subjects based on their global serum metabolic profiles by using orthogonal partial least squares (OPLS) analysis. Moreover, different changes in metabolic profiles were observed between early- and late-stages MG patients. Nine biomarkers, including gamma-aminobutyric acid and sphingosine 1-phosphate were identified. In addition, 92.8% sensitivity, 83.3% specificity and 90% accuracy were obtained from the OPLS discriminant analysis (OPLS-DA) class prediction model in detecting MG. The results presented here illustrate that serum metabolomics exhibits great potential in the detecting and grading of MG, and it is potentially applicable as a new diagnostic approach for MG.     

Misdiagnosis-related harm quantification through mixture models and harm measures

Investigating and monitoring misdiagnosis-related harm is crucial for improving health care. However, this effort has traditionally focused on the chart review process, which is labor intensive, potentially unstable, and does not scale well. To monitor medical institutes' diagnostic performance and identify areas for improvement in a timely fashion, researchers proposed to leverage the relationship between symptoms and diseases based on electronic health records or claim data. Specifically, the elevated disease risk following a false-negative diagnosis can be used to signal potential harm. However, off-the-shelf statistical methods do not fully accommodate the data structure of a well-hypothesized risk pattern and thus fail to address the unique challenges adequately. To fill these gaps, we proposed a mixture regression model and its associated goodness-of-fit testing. We further proposed harm measures and profiling analysis procedures to quantify, evaluate, and compare misdiagnosis-related harm across institutes with potentially different patient population compositions. We studied the performance of the proposed methods through simulation studies. We then illustrated the methods through data analyses on stroke occurrence data from the Taiwan Longitudinal Health Insurance Database. From the analyses, we quantitatively evaluated risk factors for being harmed due to misdiagnosis, which unveiled some insights for health care quality research. We also compared general and special care hospitals in Taiwan and observed better diagnostic performance in special care hospitals using various new evaluation measures.     

Novel, objective, multivariate biomarkers composed of plasma amino acid profiles for the diagnosis and assessment of inflammatory bowel disease

Background

Inflammatory bowel disease (IBD) is a chronic intestinal disorder that is associated with a limited number of clinical biomarkers. In order to facilitate the diagnosis of IBD and assess its disease activity, we investigated the potential of novel multivariate indexes using statistical modeling of plasma amino acid concentrations (aminogram).

Methodology and Principal Findings

We measured fasting plasma aminograms in 387 IBD patients (Crohn''s disease (CD), n = 165; ulcerative colitis (UC), n = 222) and 210 healthy controls. Based on Fisher linear classifiers, multivariate indexes were developed from the aminogram in discovery samples (CD, n = 102; UC, n = 102; age and sex-matched healthy controls, n = 102) and internally validated. The indexes were used to discriminate between CD or UC patients and healthy controls, as well as between patients with active disease and those in remission. We assessed index performances using the area under the curve of the receiver operating characteristic (ROC AUC). We observed significant alterations to the plasma aminogram, including histidine and tryptophan. The multivariate indexes established from plasma aminograms were able to distinguish CD or UC patients from healthy controls with ROC AUCs of 0.940 (95% confidence interval (CI): 0.898–0.983) and 0.894 (95%CI: 0.853–0.935), respectively in validation samples (CD, n = 63; UC, n = 120; healthy controls, n = 108). In addition, other indexes appeared to be a measure of disease activity. These indexes distinguished active CD or UC patients from each remission patients with ROC AUCs of 0.894 (95%CI: 0.853–0.935) and 0.849 (95%CI: 0.770–0.928), and correlated with clinical disease activity indexes for CD (r_s = 0.592, 95%CI: 0.385–0.742, p<0.001) or UC (r_s = 0.598, 95%CI: 0.452–0.713, p<0.001), respectively.

Conclusions and Significance

In this study, we demonstrated that established multivariate indexes composed of plasma amino acid profiles can serve as novel, non-invasive, objective biomarkers for the diagnosis and monitoring of IBD, providing us with new insights into the pathophysiology of the disease.     

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.     

Towards efficiency in rare disease research: what is distinctive and important?

Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million worldwide.Over the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing(NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused.     

Role of genetic and environmental influences on heart rate variability in middle-aged men

Our aim was to estimate causal relationships of genetic factors and different specific environmental factors in determination of the level of cardiac autonomic modulation, i.e., heart rate variability (HRV), in healthy male twins and male twins with chronic diseases. The subjects were 208 monozygotic (MZ, 104 healthy) and 296 dizygotic (DZ, 173 healthy) male twins. A structured interview was used to obtain data on lifetime exposures of occupational loading, regularly performed leisure-time sport activities, coffee consumption, smoking history, and chronic diseases from 12 yr of age through the present. A 5-min ECG at supine rest was recorded for the HRV analyses. In univariate statistical analyses based on genetic models with additive genetic, dominance genetic, and unique environmental effects, genetic effects accounted for 31-57% of HRV variance. In multivariate statistical analysis, body mass index, percent body fat, coffee consumption, smoking, medication, and chronic diseases were associated with different HRV variables, accounting for 1-11% of their variance. Occupational physical loading and leisure-time sport activities did not account for variation in any HRV variable. However, in the subgroup analysis of healthy and diseased twins, occupational loading explained 4% of the variability in heart periods. Otherwise, the interaction between health status and genetic effects was significant for only two HRV variables. In conclusion, genetic factors accounted for a major portion of the interindividual differences in HRV, with no remarkable effect of health status. No single behavioral determinant appeared to have a major influence on HRV. The effects of medication and diseases may mask the minimal effect of occupational loading on HRV.     

Essential diagnostics--the role of the Department of Biomedical Research of the Royal Tropical Institute in the 21st century

In the light of emerging and overlooked infectious diseases and widespread drug resistance, diagnostics have become increasingly important in supporting surveillance, disease control and outbreak management programs. In many low-income countries the diagnostic service has been a neglected part of health care, often lacking quantity and quality or even non-existing at all. High-income countries have exploited few of their advanced technical abilities for the much-needed development of low-cost, rapid diagnostic tests to improve the accuracy of diagnosis and accelerate the start of appropriate treatment. As is now also recognized by World Health Organization, investment in the development of affordable diagnostic tools is urgently needed to further our ability to control a variety of diseases that form a major threat to humanity. The Royal Tropical Institute's Department of Biomedical Research aims to contribute to the health of people living in the tropics. To this end, its multidisciplinary group of experts focuses on the diagnosis of diseases that are major health problems in low-income countries. In partnership we develop, improve and evaluate simple and cheap diagnostic tests, and perform epidemiological studies. Moreover, we advice and support others--especially those in developing countries--in their efforts to diagnose infectious diseases.     

Rheumatic Heart Disease-Attributable Mortality at Ages 5–69 Years in Fiji: A Five-Year,National, Population-Based Record-Linkage Cohort Study

BackgroundRheumatic heart disease (RHD) is considered a major public health problem in developing countries, although scarce data are available to substantiate this. Here we quantify mortality from RHD in Fiji during 2008–2012 in people aged 5–69 years.ConclusionsRheumatic heart disease is a leading cause of premature death as well as an important economic burden in this setting. Age-standardised death rates are more than twice those reported in current global estimates. Linkage of routine data provides an efficient tool to better define the epidemiology of neglected diseases.     

代谢生物标志物关联分析抑郁症与心理亚健康的差异性

分析抑郁症和心理亚健康的关联代谢生物标志物,为二者的临床诊断识别以及早期药物防治提供参考。 筛选18例抑郁症患者和23例心理亚健康受试者,空腹采集其静脉血。采用¹H-NMR代谢组学技术并结合单变量、多元统计、相关性以及倍数变化（Fold Change,FC）等分析方法,筛选二者内源性差异代谢物,并作为候选的关联代谢生物标志物,再以受试者工作曲线（receiver operating characteristic curve, ROC）对其诊断识别能力进行评估。选择1r1>0.6、FC>1.5为临界指标,对候选的代谢生物标志物进行筛选,候选的差异代谢物在两组受试者之间存在显著差异（P<0.05,0.01）。主要有3-OH-丁酸盐、醋酸盐、丙氨酸、甜菜碱和肉碱等。受试者工作曲线下面积（AUC）结果显示,肉碱、胆碱、组氨酸和脂质(AUC > 0.85)对于关联抑郁症和心理亚健康,具有较高的诊断价值以及预测能力,为提高抑郁症临床诊断准确度和可信度开辟新途径,并为心理亚健康受试者的早期识别和防治,阻止其发展成为精神类疾病提供参考。     

Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interactions Underlying Multiple Complex Traits

The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s). In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.     

各国孤儿药政策对比分析

据统计，医学上明确诊断的罕见病有5 000~8 000 种。虽然因患病人数少而得名罕见病，但考虑到疾病种类之多，罕见病仍是 各国不可忽视的公共卫生挑战。协助和激励医疗行业开发治疗罕见病的孤儿药，是各国政府一项重要的公共卫生政策。重点介绍了美国、 日本和欧盟的孤儿药立法，激励政策以及对病患、医药行业的积极影响。     

Evaluating the improvement in diagnostic utility from adding new predictors

Multiple diagnostic tests and risk factors are commonly available for many diseases. This information can be either redundant or complimentary. Combining them may improve the diagnostic/predictive accuracy, but also unnecessarily increase complexity, risks, and/or costs. The improved accuracy gained by including additional variables can be evaluated by the increment of the area under (AUC) the receiver‐operating characteristic curves with and without the new variable(s). In this study, we derive a new test statistic to accurately and efficiently determine the statistical significance of this incremental AUC under a multivariate normality assumption. Our test links AUC difference to a quadratic form of a standardized mean shift in a unit of the inverse covariance matrix through a properly linear transformation of all diagnostic variables. The distribution of the quadratic estimator is related to the multivariate Behrens–Fisher problem. We provide explicit mathematical solutions of the estimator and its approximate non‐central F‐distribution, type I error rate, and sample size formula. We use simulation studies to prove that our new test maintains prespecified type I error rates as well as reasonable statistical power under practical sample sizes. We use data from the Study of Osteoporotic Fractures as an application example to illustrate our method.     

“首届中国孤儿药研发论坛”专家观点

对2015 年4 月25 日召开的“首届中国孤儿药研发论坛”的专家报告内容进行归纳总结,旨在为从事罕见病诊断和孤儿药研发工 作的人士提供信息参考。报告内容涉及国内外孤儿药研发现状和前景、中国孤儿药政策和审评状况、罕见病诊断以及中国孤儿药研发的机 会等。     

Model-Based Assessment of Estuary Ecosystem Health Using the Latent Health Factor Index,with Application to the Richibucto Estuary

The ability to quantitatively assess ecological health is of great interest to those tasked with monitoring and conserving ecosystems. For decades, biomonitoring research and policies have relied on multimetric health indices of various forms. Although indices are numbers, many are constructed based on qualitative procedures, thus limiting the quantitative rigor of the practical interpretations of such indices. The statistical modeling approach to construct the latent health factor index (LHFI) was recently developed. With ecological data that otherwise are used to construct conventional multimetric indices, the LHFI framework expresses such data in a rigorous quantitative model, integrating qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modeling approach allows unified statistical inference of health for observed sites (along with prediction of health for partially observed sites, if desired) and of the relevance of ecological drivers, all accompanied by formal uncertainty statements from a single, integrated analysis. Thus far, the LHFI approach has been demonstrated and validated in a freshwater context. We adapt this approach to modeling estuarine health, and illustrate it on the previously unassessed system in Richibucto in New Brunswick, Canada, where active oyster farming is a potential stressor through its effects on sediment properties. Field data correspond to health metrics that constitute the popular AZTI marine biotic index and the infaunal trophic index, as well as abiotic predictors preconceived to influence biota. Our paper is the first to construct a scientifically sensible model that rigorously identifies the collective explanatory capacity of salinity, distance downstream, channel depth, and silt–clay content–all regarded a priori as qualitatively important abiotic drivers–towards site health in the Richibucto ecosystem. This suggests the potential effectiveness of the LHFI approach for assessing not only freshwater systems but aquatic ecosystems in general.     

NMR-based metabolomic study of type 1 diabetes

The genetic homogeneity of the people of Sardinia makes it an ideal place to study genetic related diseases such as type 1 diabetes, which in this island has one of the highest incidence worldwide. The principal objective of this study was to use ¹H high-resolution NMR spectroscopy and supervised methods of multivariate data analysis to highlight the importance of the variation of low concentration metabolites between healthy and diabetic Sardinian children. To achieve this goal, statistical analyses were performed after removal of the prevailing signals of sugars and citrate (related to carbohydrate metabolism) and of hippurate (a metabolite of bacterial origins) whose presence overwhelmed all the other compounds effects on classification. The variable influence in the statistical model showed that other metabolites deriving from gut microbial metabolism (p-cresol sulphate and phenylacetylglycine) were heavily involved in classification. This suggests the importance of changes in gut microbiota composition associated with type 1 diabetes in children.     

miRNA Signatures in Sera of Patients with Active Pulmonary Tuberculosis

Several studies showed that assessing levels of specific circulating microRNAs (miRNAs) is a non-invasive, rapid, and accurate method for diagnosing diseases or detecting alterations in physiological conditions. We aimed to identify a serum miRNA signature to be used for the diagnosis of tuberculosis (TB). To account for variations due to the genetic makeup, we enrolled adults from two study settings in Europe and Africa. The following categories of subjects were considered: healthy (H), active pulmonary TB (PTB), active pulmonary TB, HIV co-infected (PTB/HIV), latent TB infection (LTBI), other pulmonary infections (OPI), and active extra-pulmonary TB (EPTB). Sera from 10 subjects of the same category were pooled and, after total RNA extraction, screened for miRNA levels by TaqMan low-density arrays. After identification of “relevant miRNAs”, we refined the serum miRNA signature discriminating between H and PTB on individual subjects. Signatures were analyzed for their diagnostic performances using a multivariate logistic model and a Relevance Vector Machine (RVM) model. A leave-one-out-cross-validation (LOOCV) approach was adopted for assessing how both models could perform in practice. The analysis on pooled specimens identified selected miRNAs as discriminatory for the categories analyzed. On individual serum samples, we showed that 15 miRNAs serve as signature for H and PTB categories with a diagnostic accuracy of 82% (CI 70.2–90.0), and 77% (CI 64.2–85.9) in a RVM and a logistic classification model, respectively. Considering the different ethnicity, by selecting the specific signature for the European group (10 miRNAs) the diagnostic accuracy increased up to 83% (CI 68.1–92.1), and 81% (65.0–90.3), respectively. The African-specific signature (12 miRNAs) increased the diagnostic accuracy up to 95% (CI 76.4–99.1), and 100% (83.9–100.0), respectively. Serum miRNA signatures represent an interesting source of biomarkers for TB disease with the potential to discriminate between PTB and LTBI, but also among the other categories.     

Respiratory sound analysis in healthy and pathological subjects: A wavelet approach

In this paper we describe the application of a wavelet analysis-based method, to characterize the frequency power distribution of the unsteady respiratory sound signals in order to better discriminate the healthy state of a given subject. To evaluate the methodology, both normal tracheal sounds as well as adventitious respiratory sounds were investigated. In particular, our analysis shows the possibility to extract useful statistical information on the energy content and its mean frequency distribution giving us a quantitative characteristic hallmark of the respiratory pattern. The presence of sound anomalies can be pointed out through some specific patterns of the wavelet mean power spectra and thus the localization of the related quartiles which can be used as simple and efficient diagnostic indices. In this study the method has been applied in healthy subjects and patients with different respiratory diseases. Results show that different power spectra patterns characterize health from disease. Some preliminary results indicate also that pathological patterns can change as result of therapeutical interventions like mechanical ventilation.     

A functional genomics approach using metabolomics and in silico pathway analysis

In the field of functional genomics increasing effort is being undertaken to analyze the function of orphan genes using metabolome data. Improved analytical equipment allows screening simultaneously for a high number of metabolites. Such metabolite profiles are analyzed using multivariate data analysis techniques and changes in the genotype will in many cases lead to different metabolite profiles. Here, a theoretical framework that may be applied to identify the function of orphan genes is presented. The approach is based on a combination of metabolome analysis combined with in silico pathway analysis. Pathway analysis may be carried out using convex analysis and a change in the active pathway structure of deletion mutants expressed in a different metabolite profile may disclose the function or the functional class of an orphan gene. The concept is illustrated using a simplified model for growth of Saccharomyces cerevisiae.