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Projeto de investigação
NEUROCLINOMICS - Understanding NEUROdegenerative diseases throught CLINical and OMICS data integration
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Publicações
Integrative biomarker discovery in neurodegenerative diseases
Publication . Carreiro, André V.; De Mendonça, Alexandre; Carvalho, Mamede; Madeira, Sara C.
Data mining has been widely applied in biomarker discovery resulting in significant findings of different clinical and biological biomarkers. With developments in technology, from genomics to proteomics analysis, a deluge of data has become available, as well as standardized data repositories. Nonetheless, researchers are still facing important challenges in analyzing the data, especially when considering the complexity of pathways involved in biological processes and diseases. Data from single sources appear unable to explain complex processes, such as those involved in brain-related disorders, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis, thus raising the need for a more comprehensive perspective. A possible solution relies on data and model integration, where several data types are combined to provide complementary views. This in turn can result in the discovery of previously unknown biomarkers by unraveling otherwise hidden relationships between data from different sources, and/or validate such composite biomarkers in more powerful predictive models.
Prognostic models based on patient snapshots and time windows: predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis
Publication . Carreiro, André V.; Amaral, Pedro; Pinto, Susana; Tomás, Pedro; Carvalho, Mamede; Madeira, Sara C.
Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience.
Significance of subjective memory complaints in the clinical setting
Publication . Silva, Dina; Guerreiro, Manuela; Faria, Catarina; Marôco, João; Schmand, Ben A.; De Mendonça, Alexandre
Objective: The clinical significance of subjective memory complaints in the elderly participants, particularly regarding liability of subsequent progression to dementia, has been controversial. In the present study, we tested the hypothesis that severity or type of subjective memory complaints reported by patients in a clinical setting may predict future conversion to dementia.
Methods: A cohort of nondemented patients with cognitive complaints, followed up for at least 2 years or until conversion to dementia, underwent a neuropsychological evaluation and detailed assessment of memory difficulties with the Subjective Memory Complaints (SMC) Scale.
Results: At baseline, patients who converted to dementia (36.8%) had less years of formal education and generally a worse performance in the neuropsychological assessment. There were no differences in the total SMC score between nonconverters (9.5 ± 4.2) and converters (8.9 ± 4.0, a nonsignificant difference), but nonconverters scored higher in several items of the scale.
Conclusion: For patients with cognitive complaints observed in a memory clinic setting, the severity of subjective memory complaints is not useful to predict future conversion to dementia.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
PTDC/EIA-EIA/111239/2009