Integrative biomarker discovery in neurodegenerative diseases

Carreiro, André V.; Mendonça, Alexandre de; Carvalho, Mamede de; Madeira, Sara C.

doi:10.1002/wsbm.1310

Cited by 4 publications

(3 citation statements)

References 131 publications

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“…Others used neuropsychological tests (NPTs) alone [8, 10, 11, 21–25] or in combination with biological markers [9, 26–31]. The latter strategy seems to achieve better predictive performances than using the markers independently [3, 9, 15, 30–32]. Despite the efforts, to date, no single biomarker to predict conversion from MCI to dementia with high accuracy was yet found [9].…”

Section: Introductionmentioning

confidence: 99%

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Pereira

Lemos

Cardoso

et al. 2017

BMC Med Inform Decis Mak

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BackgroundPredicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion.MethodsIn the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”.ResultsThe proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set.ConclusionsPrognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0497-2) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Pereira

Lemos

Cardoso

et al. 2017

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

show abstract

“…The large discrepancy between sensitivity and specificity attained with small subsets of features strengths the importance of our study: using sophisticated FS approaches and assessing a large number of neuropsychological measures together [46]. In fact, studying the predictive power of single (or small combinations of) NPTs [40] may not be sufficient to describe the complexity of this neurodegenerative process [46]. Moreover, as evidenced in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…NPTs are widely used in clinical practice in alternative to more expensive and often invasive approaches and achieved competitive results in predicting converting patients, when compared to biological biomarkers, such as brain imaging data (MRI and PET) and cerebrospinal fluid (CSF) [7, 9, 43–45]. Machine learning approaches have been shown to be more suitable to uncover hidden synergies between a large number of predictors than traditional statistical methods [46]. Furthermore, finding which NPTs are the most relevant for prognostic prediction would be helpful in clinical practice, enabling clinicians to reduce the number of tests that are performed, saving time, and potentially reducing the number of missing values in the NPTs data (occurring due to limitations of interview duration and patient fatigue), which may compromise the learning task.…”

Section: Introductionmentioning

confidence: 99%

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Pereira

Ferreira

Cardoso

et al. 2018

BMC Med Inform Decis Mak

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BackgroundPredicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models.MethodsWe propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs.ResultsThe best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD.ConclusionsThe FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0710-y) contains supplementary material, which is available to authorized users.

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Potential of Extracellular Vesicles in Neurodegenerative Diseases: Diagnostic and Therapeutic Indications

et al. 2018

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Extracellular vesicles (EVs) are membrane-bound vesicles, including exosomes and microvesicles. EVs are nanometer sized, found in physiological fluids such as urine, blood, cerebro-spinal fluid (CSF), with a capacity of transferring various biological materials such as microRNAs, proteins, and lipids among cells without direct cell-to-cell contact. Many cells in the nervous system have been shown to release EVs. These vesicles are involved in intercellular communication and a variety of biological processes such as modulation of immune response, signal transduction, and transport of genetic materials with low immunogenicity; therefore, they have also been recently investigated for the delivery of therapeutic molecules such as siRNAs and drugs in the treatment of diseases. In addition, since EV components reflect the physiological status of the cells and tissues producing them, they can be utilized as biomarkers for early detection of various diseases. In this review, we summarize EV application, in diagnosis as biomarker sources and as a carrier tool for drug delivery in EV-based therapies in neurodegenerative diseases.

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Integrative biomarker discovery in neurodegenerative diseases

Cited by 4 publications

References 131 publications

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Potential of Extracellular Vesicles in Neurodegenerative Diseases: Diagnostic and Therapeutic Indications

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