Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

Exercit@rt mobile : monitorização da reabilitação respiratória na DPOC

A prevalência estimada da Doença Pulmonar Obstrutiva Crónica (DPOC) em Portugal é de 14,2% para indivíduos com idade superior a 45 anos (cerca de 800.000 indivíduos), sendo mais prevalente no sexo masculino (Observatório Nacional das Doenças Respiratórias, 2014). Com o aparecimento de soluções baseadas em novas modalidades de eHealth e mHealth surgiram novas formas de acompanhamento e monitorização das doenças crónicas, nomeadamente da DPOC. É neste contexto que foi desenvolvida a aplicação mobile Exercit@rt, em parceria com a Escola Superior de Saúde da Universidade de Aveiro e em continuidade com outros estudos do MCMM anteriormente desenvolvidos. A aplicação permite monitorizar, em tempo real, através da utilização de um oxímetro Bluetooth, os níveis de batimento cardíaco e saturação de oxigénio dos pacientes com DPOC. Com esta aplicação os pacientes podem realizar diversos exercícios de fisioterapia respiratória assim como atividades físicas de vida diária que podem ser monitorizadas, georreferenciadas e avaliadas. Para além do desenvolvimento da aplicação mobile, a presente investigação integrou ainda uma etapa de validação que contou com a participação de dez pacientes com doenças do foro respiratório – cinco utilizadores que utilizam/têm smartphone (UTS) e cinco utilizadores não utilizam/não têm smartphone (NUNTS). A cada um destes foram propostas tarefas a realizar na aplicação mobile, estando previsto que a aplicação estivesse apta para qualquer participante. A totalidade dos participantes reconheceu a utilidade da aplicação no controlo da sua doença.

Association between physical activity and cardiorespiratory factors in adolescents: a cross-sectional exploratory study

Background: Currently, under half of the adolescents reach recommended daily levels of physical activity (PA). It is known that higher levels of PA lead to higher levels of cardiorespiratory fitness (CRF) and therefore, a health-related CRF criterion value could contribute to identify the target population for primary cardiovascular disease prevention. Therefore, the aim of this study was to explore the relation between PA levels and CRF factors in healthy adolescents. Methods: A cross-sectional exploratory study with healthy adolescents aged 12-18 years old was conducted. Socio-demographic and body composition data were collected using a questionnaire. PA level was scored with the Physical Activity Index (PAI) and CRF assessment included lung function (LF) measured with spirometry and exercise tolerance measured with Incremental Shuttle Walking Test (ISWT). According to PAI scores the sample was divided in two groups: 1 (sedentary, low and moderately active); 2 (vigorously active (VA)). Descriptive statistics were applied to characterise the sample. Independent sample t-tests assessed differences between groups and simple logistic regressions identified the predictors of being VA. Results: The study included 115 adolescents (14.63±1.70 years old; 56.52% female). Adolescents presented a normal body mass index=21.19±3.14 Kg.m-2) and LF (forced expiratory volume in the first second (FEV1)=105.58±12.73% of the predicted). Significant differences were found between groups in height (G1–163.44±8.01; G2–167±8.65; p=0.024), LF (FEV1/ forced vital capacity (FVC); G1–97.58±10.66; G2–94.04±8.04; p=0.049), ISWT distance (G1– 1089.81±214.04; G2–1173.60±191.86; p=0.038); heart rate (HR) at rest (G1– 84.61±13.68; G2–79.23±13.81; p=0.038), HR at the end of the best ISWT (G1– 124.71±37.57; G2–133.54±33.61; p=0.041) and percentage of the maximal HR achieved during ISWT (G1–63.09±19.03; G2–67.53±17.08; p=0.043). Simple logistic regressions showed that height (OR–1.054; 95%CI 1.006-1.104), ISWT distance (OR–1.002; 95%CI 1.000-1.004) and HR at rest (OR–0.971; 95%CI 0.945-0.999) were predictors of being VA. Conclusions: Results suggest that more physically active adolescents have a better CRF profile. The findings suggest that PA is important to adolescents’ health status and it should be encouraged since childhood. Clinical practice will benefit from the use of PAI, ISWT and HR findings, allowing physiotherapists to use it for prescribing exercise.