Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis

Murdaca, Giuseppe; Banchero, Sara; Tonacci, Alessandro; Nencioni, Alessio; Monacelli, Fiammetta; Gangemi, Sebastiano

doi:10.3390/diagnostics11060940

Cited by 24 publications

(14 citation statements)

References 42 publications

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“…Hypovitaminosis is implicated in the pathogenesis of various disorders [ 334 , 335 ], and there is increasing evidence that vitamins also play a key role in neurodegenerative diseases, leading to hypotheses concerning their use as disease biomarkers [ 336 , 337 ]. Regarding AD, although preliminary data are certainly encouraging, and the role of vitamins as biomarkers for AD has been widely investigated in literature ( Table 3 ) [ 188 , 297 , 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 ], some limitations in terms of consistency, reproducibility, and specificity remain to be solved, and further research is needed before hypothesizing a clinical application.…”

Section: Resultsmentioning

confidence: 99%

“…Nonetheless, a small study conducted on 48 patients followed up after 7–9 years showed a diminished amount of folate in the blood of MCI patients converting to AD [ 297 ]. When using machine learning approaches, the concomitant findings of reduced blood levels of folic acid and vitamin D were confirmed to be predictive of worse MMSE scores (that is, a more pronounced cognitive impairment), thus suggesting a possible link with disease severity [ 343 ]. Moreover, diminished levels of circulating vitamin B 12 in patients with AD were reported in another recent case-control study [ 344 ].…”

Section: Resultsmentioning

confidence: 99%

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Blood-Based Biomarkers for Alzheimer’s Disease Diagnosis and Progression: An Overview

Varesi

Carrara

Pires

et al. 2022

Cells

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Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Blood-Based Biomarkers for Alzheimer’s Disease Diagnosis and Progression: An Overview

Varesi

Carrara

Pires

et al. 2022

Cells

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“…Vitamin D enhances the immune function and may delay aging; thus, it may play a role in the treatment of AD[ 94 ].…”

Section: Ad and Micronutrientsmentioning

confidence: 99%

Role of micronutrients in Alzheimer's disease: Review of available evidence

Hong-xin¹,

Qian²,

Wu³

et al. 2022

WJCC

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Alzheimer's disease (AD) is one of the most common age-related neurodegenerative disorders that have been studied for more than 100 years. Although an increased level of amyloid precursor protein is considered a key contributor to the development of AD, the exact pathogenic mechanism remains known. Multiple factors are related to AD, such as genetic factors, aging, lifestyle, and nutrients. Both epidemiological and clinical evidence has shown that the levels of micronutrients, such as copper, zinc, and iron, are closely related to the development of AD. In this review, we summarize the roles of eight micronutrients, including copper, zinc, iron, selenium, silicon, manganese, arsenic, and vitamin D in AD based on recently published studies.

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“…ML algorithms can use a large amount of data and extract meaningful results from them. As such, ML can be used in several applications: for classifying patients, predicting future outcomes and even individualize patients' treatment [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

et al. 2021

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Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.

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Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis

Cited by 24 publications

References 42 publications

Blood-Based Biomarkers for Alzheimer’s Disease Diagnosis and Progression: An Overview

Blood-Based Biomarkers for Alzheimer’s Disease Diagnosis and Progression: An Overview

Role of micronutrients in Alzheimer's disease: Review of available evidence

A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

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