Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

Peña-Bautista, Carmen; Oger, Camille; Baquero, Miguel; Vento, Máximo; Cháfer-Pericás, Consuelo

doi:10.1016/j.clinbiochem.2019.07.008

Cited by 13 publications

(6 citation statements)

References 43 publications

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“… References: Martínez-Torteya et al, 2015 [ 94 ], Ficiarà et al 2021 [ 95 ], Jääskeläinen et al, 2020 [ 96 ], Olazarán et al, 2015 [ 97 ], Gray et al, 2013 [ 98 ], Zhao et al, 2020 [ 99 ], Redolfi et al, 2020 [ 90 ], Wang et al, 2018 [ 100 ], Miller et al, 2020 [ 101 ], Hu et al, 2016 [ 102 ], Sh et al, 2021 [ 91 ], Yilmaz et al, 2020 [ 103 ], Peña-Bautista et al, 2019 [ 104 ], Dong et al, 2021 [ 105 ], Chang et al, 2021 [ 106 ], Santangelo et al, 2020 [ 107 ], Abate et al, 2021 [ 108 ], Lin et al, 2021 [ 109 ], Devanarayan et al, 2019 [ 110 ], Barbará-Morales et al, 2020 [ 93 ], Iddi et al, 20119 [ 111 ], Lin et al, 2020 [ 112 ], Mathotaarachchi et al, 2017 [ 113 ], Khatri et al, 2020 [ 92 ], Gupta et al, 2019 [ 114 ], Zhang et al, 2012 [ 115 ], Eke et al, 2021 [ 116 ], Cheng et al, 2015 [ 117 ], Escudero et al, 2013 [ 118 ] …”

Section: Resultsmentioning

confidence: 99%

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease

Blanco,

Salcidua,

Orellana

et al. 2023

Alz Res Therapy

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Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80–90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer’s disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

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Section: Resultsmentioning

confidence: 99%

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease

Blanco,

Salcidua,

Orellana

et al. 2023

Alz Res Therapy

View full text Add to dashboard Cite

show abstract

“…Thus, the obtained sensitivity and specificity were 88.2% and 76.9%, respectively. Similarly, another MCI-AD diagnosis model was developed considering isoprostanoids levels in urine, age, and gender, with satisfactory sensitivity (80.9%) and specificity (76.9%) [ 73 ]. Hence, Peña-Bautista et al .…”

Section: Resultsmentioning

confidence: 99%

Lipids as Early and Minimally Invasive Biomarkers for Alzheimer’s Disease

Casas-Fernández

Peña-Bautista

Baquero

et al. 2022

Self Cite

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: Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by the analysis of cerebrospinal fluid samples or by brain positron emission tomography. These procedures have some disadvantages to become widely used (expensive, invasive). As alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier, could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since brain has a high lipid content and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) showed impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.

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“…ANN is a computer model inspired by the brain's biological neural networks. They are used for various applications, from medical diagnosis to image mining [33,34]. ANN is also increasingly used for prediction tasks.…”

Section: Resultsmentioning

confidence: 99%

A novel data processing approach to detect fraudulent insurance claims for physical damage to cars

YÜCEL

2022

Journal of New Results in Science

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Some automobile insurance companies use computerized auto-detection systems to expedite claims payment decisions for insured vehicles. Claims suspected of fraud are evaluated using empirical data from previously investigated claims. The main objective of this manuscript is to demonstrate a novel data processing system and its potential for use in data classification. The data processing approach was used to develop a machine learning-based sentiment classification model to describe property damage fraud in vehicle accidents and the indicators of fraudulent claims. To this end, Singular Value Decomposition-based components and correlation-based composite variables were created. Machine learning models were then developed, with predictors and composite variables selected based on standard feature selection procedures. Five machine learning models were used: Boosted Trees, Classification and Regression Trees, Random Forests, Artificial Neural Networks, and Support Vector Machines. For all models, the models with composite variables achieved higher accuracy rates, and among these models, the artificial neural network was the model with the highest accuracy performance at 76.56%.

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Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

Cited by 13 publications

References 43 publications

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease

Lipids as Early and Minimally Invasive Biomarkers for Alzheimer’s Disease

A novel data processing approach to detect fraudulent insurance claims for physical damage to cars

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