Biomarkers in Alzheimer’s disease

Janeiro, Manuel H.; Ardanaz, Carlos G.; Sola-Sevilla, Noemi; Dong, Jinya; Cortés-Erice, María; Solas, Maite; Puerta, Elena; Ramı́rez, Marı́a J.

doi:10.1515/almed-2020-0090

Cited by 21 publications

(21 citation statements)

References 69 publications

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“…AD biomarkers have played an integral role in understanding its structure and monitoring its progression to help its early detection and treatment [29], [30]. This section summarizes some types of these biomarkers.…”

Section: Biomarkers Of Alzheimer's Disease Detectionmentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It can cause a massive impact on a patient's memory and mobility. As this disease is irreversible, early diagnosis is crucial for delaying the symptoms and adjusting the patient's lifestyle. Many machine learning (ML) and deep learning (DL) based-approaches have been proposed to accurately predict AD before its symptoms onset. However, finding the most effective approach for AD early prediction is still challenging. This review explored 24 papers published from 2018 until 2021. These papers have proposed different approaches using state of the art machine learning and deep learning algorithms on different biomarkers to early detect AD. The review explored them from different perspectives to derive potential research gaps and draw conclusions and recommendations. It classified these recent approaches in terms of the learning technique used and AD biomarkers. It summarized and compared their findings, and defined their strengths and limitations. It also provided a summary of the common AD biomarkers. From this review, it was found that some approaches strove to increase the prediction accuracy regardless of their complexity such as using heterogeneous datasets, while others sought to find the most practical and affordable ways to predict the disease and yet achieve good accuracy such as using audio data. It was also noticed that DL based-approaches with image biomarkers remarkably surpassed ML based-approaches. However, they achieved poorly with genetic variants data. Despite the great importance of genetic variants biomarkers, their large variance and complexity could lead to a complex approach or poor accuracy. These data are crucial to discover the underlying structure of AD and detect it at early stages. However, an effective pre-processing approach is still needed to refine these data and employ them efficiently using the powerful DL algorithms.

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Section: Biomarkers Of Alzheimer's Disease Detectionmentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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“…Some differences are noticed in clinical tests, such as non-verbal episodic memory or repetition tasks, but the difference is not sufficient to obtain a significant classification [ 10 , 26 ]. Currently, we succeed in precise early diagnosis of PPA subtypes and AD thanks to the association of neuro-anatomic analyses with Cerebrospinal Fluid (CSF) biomarkers [ 27 , 28 , 29 , 30 ]. Indeed, a decrease of β-Amyloid markers and a high tau concentration confirms AD pathology [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Logogenic Primary Progressive Aphasia or Alzheimer Disease: Contribution of Acoustic Markers in Early Differential Diagnosis

Eloïse

Plonka

Arslan

et al. 2022

Life

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The logopenic variant of Primary Progressive Aphasia (lvPPA), a syndromic disorder centered on language impairment, often presents variable underlying neurodegenerative pathologies such as Alzheimer Disease (AD). Actual language assessment tests and lumbar puncture, focused on AD diagnosis, cannot precisely distinguish the symptoms, or predict their progression at onset time. We analyzed acoustic markers, aiming to discriminate lvPPA and AD as well as the influence of AD biomarkers on acoustic profiles at the beginning of the disease. We recruited people with AD (n = 8) and with lvPPA (n = 8), with cerebrospinal fluid biomarker profiles determined by lumbar puncture. The participants performed a sentence repetition task that allows assessing potential lvPPA phonological loop deficits. We found that temporal and prosodic markers significantly differentiate the lvPPA and AD group at an early stage of the disease. Biomarker and acoustic profile comparisons discriminated the two lvPPA subgroups according to their biomarkers. For lvPPA with AD biomarkers, acoustic profile equivalent to an atypical AD form with a specific alteration of the phonological loop is shown. However, lvPPA without AD biomarkers has an acoustic profile approximating the one for DLFT. Therefore, these results allow us to classify lvPPA differentially from AD based on acoustic markers from a sentence repetition task. Furthermore, our results suggest that acoustic analysis would constitute a clinically efficient alternative to refused lumbar punctures. It offers the possibility to facilitate early, specific, and accessible neurodegenerative diagnosis and may ease early care with speech therapy, preventing the progression of symptoms.

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“…A biomarker is a tool that may indicate the presence or absence of a particular disease, future risk of a disease, or severity of the disease. Many factors are currently in research as a biomarker for dementia risk, like the amount of beta-amyloid in the brain, levels of tau protein in cerebrospinal fluid, and positron emission tomography (PET) imaging of the brain showing glucose metabolism [ 5 ]. But from a public health point of view, developing a simple and inexpensive biomarker like a blood test would be of great benefit.…”

Section: Introductionmentioning

confidence: 99%

The Role of High-Density Lipoprotein in Lowering Risk of Dementia in the Elderly: A Review

Anusheel¹,

Avula²,

Joseph³

et al. 2022

Cureus

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Dementia is one of the major causes of disability and hospitalization in the elderly. As far as non-invasive markers of dementia are concerned, we only have age and Apolipoprotein-E (Apo-E) gene, which can be considered as clinically relevant. Modifiable risk factors have been found to be the cause in one-third of the patients who develop dementia. The compatible data supporting the same, in particular for dyslipidemia, is limited, which in turn makes it difficult to devise prevention and interventional methods for both dementia and mild cognitive impairment. Hence, the objective of the review is to summarize the findings on the relation established between the high-density lipoprotein type C( HDL-C) levels and lower the chance of dementia in the elderly, and the possible role of HDL-C as a potential predictive biomarker for cases of dementia in elderly people. Dyslipidemia, a known risk factor for the occurrence of cardiovascular diseases, seems to be linked to Alzheimer’s disease. Elevated levels of serum cholesterol in mid-adult life increases the risk of dementia in older age. But elevated high-density lipoprotein (HDL) level and its principal apolipoprotein A-I (ApoA-I ) equates with a low risk of dementia in the elderly population HDL cholesterol has been found to promote endothelial nitric oxide synthase activity which in turn reduces the neural and vascular inflammation and suppresses vascular adhesion thereby exhibiting its vasoprotective function. It has been believed that all these factors have a role to play in the pathogenesis of dementia. The relation between the higher levels of HDL cholesterol or its key protein component ApoA-I and the lower dementia prevalence in the elderly had been documented in numerous observational studies. Some studies have reported conflicting results. Yet, observational studies measuring the baseline HDL level in middle age found a significant association between HDL level and dementia risk in the elderly, whereas those studies measuring HDL cholesterol level only in old age found no association. Likewise, a significant association between HDL cholesterol and dementia risk has been reported with studies that carry through to 10 years or longer. However, the studies with follow-up of fewer than 10 years had failed to document any such association between HDL cholesterol and dementia. HDL assays may also be used as a predictive biomarker for dementia patients to target the interventions. Although statins do not target HDL directly but can be an area of interest for dementia.

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Biomarkers in Alzheimer’s disease

Cited by 21 publications

References 69 publications

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Logogenic Primary Progressive Aphasia or Alzheimer Disease: Contribution of Acoustic Markers in Early Differential Diagnosis

The Role of High-Density Lipoprotein in Lowering Risk of Dementia in the Elderly: A Review

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