Daniela Baumgartner

Impaired Elastic Properties of the Ascending Aorta in Newborns Before and Early After Successful Coarctation Repair Proof of a Systemic Vascular Disease of the Prestenotic Arteries

Background—Despite successful surgical correction, morbidity of patients with coarctation of the aorta is increased. It is well known that these patients have impaired elastic properties of the prestenotic arteries. To find out whether these abnormalities are primarily present or develop later, we studied 17 newborns before and early after surgical repair. Methods and Results—Aortic wall stiffness index and distensibility were calculated using ascending and abdominal aortic diameters determined by M-mode echocardiography and noninvasive estimation of aortic pulse pressure in the right arm and leg. Seventeen patients with aortic coarctation (mean age, 20±26 days) were compared with 17 normal neonates (mean age, 13±7 days) preoperatively and postoperatively (10±6 days after surgery). Ascending aortic distensibility in patients was significantly reduced preoperatively (79±58 versus 105±36; P=0.03) and postoperatively (65±24 versus 105±36; P<0.005). Preoperative and postoperative ascending aortic stiffness index was higher in patients (preoperative, 5.2±4.4 versus 2.7±0.9; P=0.04; postoperative, 4.0±1.6 versus 2.7±0.9; P<0.005). Elastic properties of the descending aorta did not differ preoperatively or postoperatively compared with those in normal subjects. Conclusions—Elastic properties of the prestenotic aorta of patients with coarctation seem to be impaired primarily, even in neonates, and remain unchanged early after successful operation. Surgical correction does not resolve inborn pathology of the prestenotic aortic vascular bed.

Bioinformatic-driven search for metabolic biomarkers in disease

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.

Supervised machine learning techniques for the classification of metabolic disorders in newborns

MOTIVATION During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for constructing classification rules with high discriminatory power. RESULTS Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity >96.8%, specificity >99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism.

Dural ectasia in children with marfan syndrome : A prospective, multicenter, patient-control study

The clinical diagnosis of Marfan syndrome in childhood is difficult, because symptoms may not have developed to their full expression until adulthood. The Ghent nosology for the diagnosis of Marfan syndrome classifies dural ectasia as a major diagnostic criterion. More than two thirds of adult patients with Marfan syndrome show dural ectasia, while the frequency in childhood is unknown. This prospective multicenter observational patient‐control study was performed to identify pathologic changes of the lumbosacral spine in young patients with Marfan syndrome. Design: Prospective clinical trial, multicentric, cross‐sectional. Setting: MRI of the lumbosacral spine. Patients: Twenty patients with proven Marfan syndrome, 20 patients suspicious for Marfan syndrome and 38 healthy controls. Outcome measures: Vertebral body diameter (VBD) from L1 to S1, dural sac diameter (DSD) from L1 to S1, dural sac ratio (DSR), qualitative assessment of the lumbosacral spine. Results: DSD and VBD in different age groups were higher in patients with proven or suspected Marfan syndrome than in healthy controls (DSD: L1, 6–8 years, P < 0.05). VBD related to body height showed a similar growth related increase in patients with proven or suspected Marfan syndrome and controls. DSD related to body height was elevated in patients with proven or suspected Marfan syndrome at different levels of the lumbar spine. DSD at levels L1, L5, and S1, and DSR at levels L5 and S1 of patients with proven Marfan syndrome were significantly higher (P < 0.05) than in controls. Conclusion: Even during childhood pathologic changes inside the lumbosacral spine of patients with Marfan syndrome can be observed. Dural ectasia, which occurs at different levels of the lumbar spine, can be detected at levels L5 and S1 in up to 40% of patients with Marfan syndrome.

Biomarker Discovery, Disease Classification, and Similarity Query Processing on High-Throughput MS/MS Data of Inborn Errors of Metabolism

In newborn errors of metabolism, biomarkers are urgently needed for disease screening, diagnosis, and monitoring of therapeutic interventions. This article describes a 2-step approach to discovermetabolic markers, which involves (1) the identification ofmarker candidates and (2) the prioritization of thembased on expert knowledge of diseasemetabolism. For step 1, the authors developed a new algorithm, the biomarker identifier (BMI), to identifymarkers fromquantified diseased versus normal tandemmass spectrometry data sets. BMI produces a ranked list ofmarker candidates and discards irrelevant metabolites based on a quality measure, taking into account the discriminatory performance, discriminatory space, and variance ofmetabolites’ concentrations at the state of disease. To determine the ability of identified markers to classify subjects, the authors compared the discriminatory performance of several machine-learning paradigms and described a retrieval technique that searches and classifies abnormal metabolic profiles from a screening database. Seven inborn errors of metabolism— phenylketonuria (PKU), glutaric acidemia type I (GA-I), 3-methylcrotonylglycinemia deficiency (3-MCCD), methylmalonic acidemia (MMA), propionic acidemia (PA), medium-chain acylCoAdehydrogenase deficiency (MCADD), and 3-OH longchain acyl CoA dehydrogenase deficiency (LCHADD)—were investigated. All primarily prioritized marker candidates could be confirmed by literature. Somenovel secondary candidateswere identified (i.e., C16:1 andC4DCfor PKU, C4DCfor GA-I, and C18:1 forMCADD), which require further validation to confirmtheir biochemical role during health and disease.

Quantitative sequence analysis of FBN1 premature termination codons provides evidence for incomplete NMD in leukocytes

We improved, evaluated, and used Sanger sequencing for quantification of single nucleotide polymorphism (SNP) variants in transcripts and gDNA samples. This improved assay resulted in highly reproducible relative allele frequencies (e.g., for a heterozygous gDNA 50.0±1.4%, and for a missense mutation‐bearing transcript 46.9±3.7%) with a lower detection limit of 3–9%. It provided excellent accuracy and linear correlation between expected and observed relative allele frequencies. This sequencing assay, which can also be used for the quantification of copy number variations (CNVs), methylations, mosaicisms, and DNA pools, enabled us to analyze transcripts of the FBN1 gene in fibroblasts and blood samples of patients with suspected Marfan syndrome not only qualitatively but also quantitatively. We report a total of 18 novel and 19 known FBN1 sequence variants leading to a premature termination codon (PTC), 26 of which we analyzed by quantitative sequencing both at gDNA and cDNA levels. The relative amounts of PTC‐containing FBN1 transcripts in fresh and PAXgene‐stabilized blood samples were significantly higher (33.0±3.9% to 80.0±7.2%) than those detected in affected fibroblasts with inhibition of nonsense‐mediated mRNA decay (NMD) (11.0±2.1% to 25.0±1.8%), whereas in fibroblasts without NMD inhibition no mutant alleles could be detected. These results provide evidence for incomplete NMD in leukocytes and have particular importance for RNA‐based analyses not only in FBN1 but also in other genes. Hum Mutat 30:1–10, 2009.