NMR-Based Screening for Inborn Errors of Metabolism: Initial Results from a Study on Turkish Neonates

Hyperpolarization-Enhanced NMR Spectroscopy of Unaltered Biofluids Using Photo-CIDNP

The direct and unambiguous detection and identification of individual metabolite molecules present in complex biological mixtures constitute a major challenge in (bio)analytical research. In this context, nuclear magnetic resonance (NMR) spectroscopy has proven to be particularly powerful owing to its ability to provide both qualitative and quantitative atomic-level information on multiple analytes simultaneously in a noninvasive manner. Nevertheless, NMR suffers from a low inherent sensitivity and, moreover, lacks selectivity regarding the number of individual analytes to be studied in a mixture of a myriad of structurally and chemically very different molecules, e.g., metabolites in a biofluid. Here, we describe a method that circumvents these shortcomings via performing selective, photochemically induced dynamic nuclear polarization (photo-CIDNP) enhanced NMR spectroscopy on unmodified complex biological mixtures, i.e., human urine and serum, which yields a single, background-free one-dimensional NMR spectrum. In doing this, we demonstrate that photo-CIDNP experiments on unmodified complex mixtures of biological origin are feasible, can be performed straightforwardly in the native aqueous medium at physiological metabolite concentrations, and act as a spectral filter, facilitating the analysis of NMR spectra of complex biofluids. Due to its noninvasive nature, the method is fully compatible with state-of-the-art metabolomic protocols providing direct spectroscopic information on a small, carefully selected subset of clinically relevant metabolites. We anticipate that this approach, which, in addition, can be combined with existing high-throughput/high-sensitivity NMR methodology, holds great promise for further in-depth studies and development for use in metabolomics and many other areas of analytical research.

Establishment of age- and -gender specific reference intervals for amino acids and acylcarnitines by tandem mass spectrometry in Turkish paediatric population

Introduction

We determined age- and gender-specific reference intervals (RIs) for acylcarnitines and amino acids by tandem mass spectrometry (MS/MS) in the Turkish paediatric population by using laboratory information system (LIS) data.

Materials and methods

A total of 9156 MS/MS results of children between 0-18 years of age, were downloaded from the LIS. Premature infants and newborns followed in the intensive care unit were excluded and only the first result of each patient attending outpatient clinics was included. Children with a known or suspected diagnosis of metabolic disease, malignancy, epilepsy, mental retardation, or genetic disorder were excluded. Laboratory results were evaluated and children with any pathological laboratory finding were excluded, resulting in a final sample size of 3357 (2029 boys and 1328 girls). Blood was collected by capillary puncture and spotted on Whatman 903 filter paper cards and analysed by MS/MS (Shimadzu LCMS-8050, Shimadzu Corporation, Kyoto, Japan). Data were evaluated for age and gender differences and age partitioning was performed according to the literature and visual evaluation of the data. Age subgroups were: ≤ 1 month, 2 months-1 year, 2-5 years, 6-10 years, and 11-18 years.

Results

There were significant age-related differences for the majority of amino acids and acylcarnitines thus age dependent RIs were established. Gender-specific RIs were established for tyrosine, leucine-isoleucine, isovalerylcarnitine (C5) and hexadecanoylcarnitine (C16).

Conclusions

Establishing age-related RIs can enhance the quality of medical care by facilitating early diagnosis and therapy, especially in certain metabolic disorders presenting with mild biochemical abnormalities and subtle clinical manifestations.

Ex vivo proton spectroscopy (1 H-NMR) analysis of inborn errors of metabolism: Automatic and computer-assisted analyses

There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (¹ H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with ¹ H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same ¹ H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because ¹ H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.

Metabolic impact of infant formulas in young infants. An outlook from the urine metabolome

Introduction

Although breast milk is the ideal food source for newborns during the first six months of life, a high percentage of children receive infant formulas. There is evidence that specific diet habits may influence individual metabolic profile. Therefore, in newborns, such profile can be influenced by the use of infantile formulas given the composition differences that display compared to human milk. Up to now, there are no reports in the literature that address this issue.

Objectives

this work aims to compare the metabolic profile of full-term newborns that were feed with either breast milk (n = 32) or infantile formulas (n = 21). Methods: Metabolic profile was established based on urine analysis through gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (H-NMR).

Results

our results evidenced a more gluconeogenic profile in breast-fed infants characterized by elevation of Kreb's cycle intermediaries like fumaric, succinic and ketoglutaric acids compared to infants receiving infant formula. In addition, infant formula fed infants presented urinary excretion of metabolites derived from specific compounds present in this type of diet that were not observed in breast-fed infants, for instance D-glucitol, and 4-deoxytetronic. Moreover, in infant formula fed infants there was excretion of basal levels of metabolites of clinical relevance like 3-hydroxy-3-methyl-glutaric, 2-methyl-3-keto-valeric and 3,4-dihydroxybutyric.

Conclusion

These results show the importance of understanding the metabolic impact of diet in newborn population in normal and pathological contexts.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

1H-Nuclear Magnetic Resonance Analysis of Urine as Diagnostic Tool for Organic Acidemias and Aminoacidopathies

The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC-MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.

Strain-Specific Liver Metabolite Profiles in Medaka

The relationship between genetic variation and phenotypic traits is often poorly understood since specific genotypes do not always easily translate into associated phenotypes, especially for complex disorders. The genetic background has been shown to affect metabolic pathways and thus contribute to variations in the metabolome. Here, we tested the suitability of NMR metabolomics for comparative analysis of fish lines as a first step towards phenotype-genotype association studies. The Japanese rice fish, medaka (Oryzias latipes), is a widely used genetic vertebrate model with several isogenic inbred laboratory strains. We used liver extracts of medaka iCab and HO5 strains as a paradigm to test the feasibility of distinguishing the metabolome of two different inbred strains. Fifteen metabolites could be detected in uni- and multivariate analyses that showed strain-specific levels. Differences could be assigned to specific metabolic pathways. Our results show that NMR spectroscopy is a suitable method to detect variance of the metabolome caused by subtle genetic differences. Thus, it has the potential to address genotype–phenotype associations in medaka, providing an additional level of phenotypic analysis.

Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism

Nuclear Magnetic Resonance (NMR) spectroscopy has been applied in many fields of science and is increasingly being considered as a tool in the clinical setting. This review examines its application for diagnosis of inborn errors of metabolism (IEMs). IEMs, whether involving deficiency in the synthesis and degradation of metabolites, or in lipoprotein metabolism, affect nearly 3% of the global population. NMR is a preferred method for comprehensive evaluation of complex biofluids such as blood or urine, as it can provide a relatively unbiased overview of all compounds that are present and does not destroy or otherwise chemically alter the sample. While current newborn screening programs take advantage of other more sensitive methods, such as mass spectrometry, NMR has advantages especially for urine analysis with respect to ease of sample preparation and the reproducibility of results. NMR spectroscopy is particularly compatible with analysis of lipoproteins because it provides information about their size and density, not easily attained by other methods, that can help the clinician to better manage patients with dyslipidemia. We believe that NMR holds great potential for expanding clinical diagnosis in the future, in the field of IEMs and beyond.

Monitoring Methylmalonic Aciduria by NMR Urinomics

The paper reports on monitoring methylmalonic aciduria (MMA)-specific and non-specific metabolites via NMR urinomics. Five patients have been monitored over periods of time; things involved were diet, medication and occasional episodes of failing to comply with prescribed diets. An extended dataset of targeted metabolites is presented, and correlations with the type of MMA are underlined. A survey of previous NMR studies on MMA is also presented.

Metabolic changes in early neonatal life: NMR analysis of the neonatal metabolic profile to monitor postnatal metabolic adaptations

BACKGROUND

A major challenge from the moment a child is delivered is the adaptation to the extrauterine life, where rapid metabolic changes take place. The study of these changes during the first days of human life may assist in the understanding of the metabolic processes that occur at this critical period, which is likely to provide significant clinical insights. To date, metabolomics has become a powerful field, ideal for the monitoring of such dynamic variations, since it offers the possibility to identify alterations in metabolic profiles, even on daily basis.

METHODS

The study included 253 healthy newborns (GA 35 to 40 weeks) from the region of Western Greece. Urine samples were collected immediately after birth and at the third day of life. NMR-based metabolomics was used to compare the metabolic urinary profiles of newborns from the first and third day of their life, assessing the impact of six perinatal factors; delivery mode, prematurity, maternal smoking, gender, nutrition and neonatal jaundice.

RESULTS

Analysis of urine metabolic fingerprint from the first and third day of life, coupled with multivariate statistics, provides insights into the details of early life metabolic profile differentiation. Αt the third day of life metabolic adaptations are evident, as many differences were noted in urine of healthy neonates within the first 72 h postpartum. Trends in differentiation of metabolites levels between the two groups, late preterm and term newborns, have been also observed.

CONCLUSIONS

Newborn's urine metabolic profiles confirmed the rapid changes in their metabolism after birth. Further, ongoing research will enable us to develop one reference model of urinary metabolomics in healthy newborns during the period of adaptation to the extra-uterine life.

Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis

Objectives

The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.

Methods

Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.

Results

Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.

Conclusions

PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

NMR-based newborn urine screening for optimized detection of inherited errors of metabolism

Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized ¹H- NMR-based analysis.

Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism

Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.

New tools and approaches to newborn screening: ready to open Pandora's box?

The landscape of newborn screening (NBS) is changing as new tools are developed. We must acknowledge that NBS is a very important and extraordinarily positive initiative especially for rare and serious inherited disorders; however, lessons learned from current NBS should guide the future of NBS as we enter the era of “omics” that will expand NBS for many other genetic disorders. In this article, I will first discuss new tools such as genomics and metabolomics for NBS. I will then turn to assessing how best to take advantage of new technical developments while considering the best interests of patients and the success of newborn screening.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Demographic and clinical characteristics of the children with aminoacidopathy in Isfahan Province, Central Iran in 2007-2015

CONTEXT

Aminoacidopathies refer to defects in protein synthesis pathways which result in a range of biochemical disorders and clinical presentations. The enzyme defects in intermediate metabolic pathways lead to accumulation of one or more amino acids or metabolites. Despite higher prevalence rates, screening infants for inherited metabolic disorders is not run in many Middle East countries.

AIM

This research is part of a larger study of inherited metabolic disorders to characterize and measure the prevalence of aminoacidopathies.

SETTINGS AND DESIGN

Cross-sectional study in the population aged 0-17 years old in Isfahan province of Iran, 2007-2015.

SUBJECTS AND METHODS

Demographic characteristics, history of disease, development of clinical condition and socioeconomic status were obtained from interviews as well as patient records of pediatric tertiary referral hospitals and metabolic disorders centers.

STATISTICAL ANALYSIS USED

SPSS qualitative and quantitative analysis.

RESULTS

The incidence rate of aminoacidopathies was derived to be 9/100,000 live births. The frequency of consanguineous marriages in this group of the patients was 89.2%. Of the patients with aminoacidopathies, 76.6% required hospitalization with tyrosinemia having the highest rate overall (>10 times). The most prevalent symptoms in this group of patients were developmental disorders and convulsions while half presented with growth disorders during follow-up. Of the 35.5% patients, who died at various ages, one-third was in the maple syrup urine disease subgroup.

CONCLUSION

Although metabolic disorders are identified as rare diseases, they are more prevalent in the studied population of Isfahan.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.

Urinary NMR-based metabolomic analysis of rats possessing variable sperm count following orally administered Eurycoma longifolia extracts of different quassinoid levels

ETHNOPHARMACOLOGICAL RELEVANCE

Eurycoma longifolia (Tongkat Ali, TA) roots have been ethnically used as a remedy to boost male sexual desire, libido, energy and fertility.

AIM OF THE STUDY

The study evaluated the effect of TA extracts with different quassinoid levels on rats sperm count and examined corresponding post-treatment urinary metabolic changes.

MATERIALS AND METHODS

Twenty-four male Sprague-Dawley rats, categorized into 4 groups of 6 rats each, were orally administered for 48 days with water for the control (group 1), 125mg/kg of TA water extract (TAW, group 2), 125mg/kg of TA quassinoid-poor extract (TAQP, group 3) and 21mg/kg of TA quassinoid-rich extract (TAQR, group 4). Upon completion of the 48-day treatment, the urine samples were analyzed by NMR and the animals were subsequently sacrificed for sperm count analysis. The urine profiles were categorized according to sperm count level.

RESULTS

The results showed that the sperm count in TAW- and TAQR-treated groups was significantly higher compared to the TAQP-administered and control groups. The orthogonal partial least squares discriminant analysis (OPLS-DA) model indicated a clear separation among the urine profiles with respect to sperm count level. Urine (1)H-NMR profiles of the high-sperm count group contained higher concentrations of trigonelline, alanine, benzoic acid and higher intensity of a signal at 3.42ppm, while ethanol was at higher concentration in the normal-sperm count group.

CONCLUSIONS

The results proved the efficacy of quassinoids on sperm count increase in rats and provided quantitative markers in urine suitable for analysis of sperm profile and male fertility status.

Parent Coping and the Behavioural and Social Outcomes of Children Diagnosed with Inherited Metabolic Disorders

OBJECTIVE

To explore the level of coping and management of parents of children with inherited metabolic disorders (IMD) and the relationship with children's cognitive, behavioural and social functioning.

METHODS

Parents of children (n = 22) with confirmed IMD (glutaric aciduria type I, methylmalonic aciduria, propionic aciduria, isovaleric aciduria, glycogen storage disease, maple syrup urine disease, ornithine transcarbamylase or very long-chain acyl-CoA dehydrogenase deficiency) completed standardised questionnaires regarding psychological distress, coping and family management. Children completed cognitive assessments and parents rated their behavioural and social functioning on standardised questionnaires. Scores were compared with normative data.

RESULTS

Most parents were coping well; 4/22 reported high levels of psychological distress. Exploratory analysis found that parent coping variables were correlated to the child's internalising symptoms, whereas family management was related to children's externalising behaviours and social skills. No relationship was found between parent variables and cognitive functioning.

CONCLUSIONS

Parental coping and family management impact on the child's internalising symptoms and externalising behaviours, respectively. Early identification of issues in these domains may enhance referral for therapeutic interventions and family support programmes.

Demographic and Clinical Findings in Pediatric Patients Affected by Organic Acidemia

OBJECTIVE

Metabolic disorders, which involve many different organs, can be ascribed to enzyme deficiency or dysfunction and manifest with a wide range of clinical symptoms. This study evaluated some of the demographic and clinical findings in pediatric patients affected by organic acidemia.

MATERIALS & METHODS

This cross-sectional study was part of a larger study conducted in patients with metabolic disorders during a period of 7 years from 2007 to 2014 in Isfahan Province, Iran. Our study covered a wide range of cases from newborn infants (one-week old) to adolescents (children up to the age of 17 years). This study evaluated patients' demographic information, history of disease, developmental and educational status, clinical and general conditions. Phone and in-person interviews were used to gather information.

RESULTS

Out of 5100 patients screened in this study, 392 patients were affected by one of the different metabolic disorders and 167 individuals were diagnosed as organic acidemia. Propionic acidemia/methyl malonic acidemia (PA/MMA) was the most prevalent form of this metabolic disorder. The frequency of consanguinity was 84.7% in the group of patients. The mortality rate was 18.8% in patients with organic academia.

CONCLUSION

Each of the metabolic diseases, as a separate entity, is rare; nevertheless, in aggregate they have a somewhat high overall prevalence. These diseases result in mental and developmental disorders in the absence of quick diagnosis and initiation of treatment. Furthermore, more mutations should be identified in societies affected by consanguinity. Further research should also be conducted to determine worthwhile and more-efficient screening methods as well as long term neurological prognosis.

Metabolomics in Newborns

Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction.