Pregnancy and newborns disorders followed by urine metabolomics

Chapter 1 introduces the scope of the work by identifying the clinically relevant prenatal disorders and presently available diagnostic methods. The methodology followed in this work is presented, along with a brief account of the principles of the analytical and statistical tools employed. A thorough description of the state of the art of metabolomics in prenatal research concludes the chapter, highlighting the merit of this novel strategy to identify robust disease biomarkers. The scarce use of maternal and newborn urine in previous reports enlightens the relevance of this work. Chapter 2 presents a description of all the experimental details involved in the work performed, comprising sampling, sample collection and preparation issues, data acquisition protocols and data analysis procedures. The proton Nuclear Magnetic Resonance (NMR) characterization of maternal urine composition in healthy pregnancies is presented in Chapter 3. The urinary metabolic profile characteristic of each pregnancy trimester was defined and a 21-metabolite signature found descriptive of the metabolic adaptations occurring throughout pregnancy. 8 metabolites were found, for the first time to our knowledge, to vary in connection to pregnancy, while known metabolic effects were confirmed. This chapter includes a study of the effects of non-fasting (used in this work) as a possible confounder. Chapter 4 describes the metabolomic study of 2nd trimester maternal urine for the diagnosis of fetal disorders and prediction of later-developing complications. This was achieved by applying a novel variable selection method developed in the context of this work. It was found that fetal malformations (FM) (and, specifically those of the central nervous system, CNS) and chromosomal disorders (CD) (and, specifically, trisomy 21, T21) are accompanied by changes in energy, amino acids, lipids and nucleotides metabolic pathways, with CD causing a further deregulation in sugars metabolism, urea cycle and/or creatinine biosynthesis. Multivariate analysis models´ validation revealed classification rates (CR) of 84% for FM (87%, CNS) and 85% for CD (94%, T21). For later-diagnosed preterm delivery (PTD), preeclampsia (PE) and intrauterine growth restriction (IUGR), it is found that urinary NMR profiles have early predictive value, with CRs ranging from 84% for PTD (11-20 gestational weeks, g.w., prior to diagnosis), 94% for PE (18-24 g.w. pre-diagnosis) and 94% for IUGR (2-22 g.w. pre-diagnosis). This chapter includes results obtained for an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) study of pre-PTD samples and correlation with NMR data. One possible marker was detected, although its identification was not possible. Chapter 5 relates to the NMR metabolomic study of gestational diabetes mellitus (GDM), establishing a potentially predictive urinary metabolic profile for GDM, 2-21 g.w. prior to diagnosis (CR 83%). Furthermore, the NMR spectrum was shown to carry information on individual phenotypes, able to predict future insulin treatment requirement (CR 94%). Chapter 6 describes results that demonstrate the impact of delivery mode (CR 88%) and gender (CR 76%) on newborn urinary profile. It was also found that newborn prematurity, respiratory depression, large for gestational age growth and malformations induce relevant metabolic perturbations (CR 82-92%), as well as maternal conditions, namely GDM (CR 82%) and maternal psychiatric disorders (CR 91%). Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the value of maternal or newborn urine metabolomics for pregnancy monitoring and disease prediction, towards the development of new early and non-invasive diagnostic methods.

FTIR, a potential tool to dementia diagnosis trough analysis of plasma

Nowadays it is still difficult to perform an early and accurate diagnosis of dementia, therefore many research focus on the finding of new dementia biomarkers that can aid in that purpose. So scientists try to find a noninvasive, rapid, and relatively inexpensive procedures for early diagnosis purpose. Several studies demonstrated that the utilization of spectroscopic techniques, such as Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy could be an useful and accurate procedure to diagnose dementia. As several biochemical mechanisms related to neurodegeneration and dementia can lead to changes in plasma components and others peripheral body fluids, blood-based samples and spectroscopic analyses can be used as a more simple and less invasive technique. This work is intended to confirm some of the hypotheses of previous studies in which FTIR was used in the study of plasma samples of possible patient with AD and respective controls and verify the reproducibility of this spectroscopic technique in the analysis of such samples. Through the spectroscopic analysis combined with multivariate analysis it is possible to discriminate controls and demented samples and identify key spectroscopic differences between these two groups of samples which allows the identification of metabolites altered in this disease. It can be concluded that there are three spectral regions, 3500-2700 cm -1, 1800-1400 cm-1 and 1200-900 cm-1 where it can be extracted relevant spectroscopic information. In the first region, the main conclusion that is possible to take is that there is an unbalance between the content of saturated and unsaturated lipids. In the 1800-1400 cm-1 region it is possible to see the presence of protein aggregates and the change in protein conformation for highly stable parallel β-sheet. The last region showed the presence of products of lipid peroxidation related to impairment of membranes, and nucleic acids oxidative damage. FTIR technique and the information gathered in this work can be used in the construction of classification models that may be used for the diagnosis of cognitive dysfunction.