A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity

Cordelli, Ermanno; Maulucci, Giuseppe; Spirito, Marco De; Rizzi, Alessandro; Pitocco, Dario; Soda, Paolo

doi:10.1016/j.cmpb.2018.05.025

Cited by 31 publications

(17 citation statements)

References 33 publications

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“…48,[59][60][61][62][63][64] Other diagnostic applications included distinguishing coeliac disease from an at-risk group 65,66 and differentiating those who have complications in T1D. 67,68 Random forests and support vector machine most frequently utilised.…”

Section: Diagnosismentioning

confidence: 99%

“…69 Six models from the evaluated studies returned more than one of the following measures as either 1 or 100%: AUC, accuracy, precision and recall, sensitivity and specificity. 59,67,68,[111][112][113] This perfect performance indicates that a model may not be required, as there exists data that classifies the groups without error. An alternative explanation of apparently optimal performance may reside in poor implementation of cross-validation strategies.…”

Section: Validation and Independent Testingmentioning

confidence: 99%

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A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

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Section: Diagnosismentioning

confidence: 99%

Section: Validation and Independent Testingmentioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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“…The main limit of this work is the small sample size, and the validation of the approach on a larger population is necessary. However, this investigation paves the possibility for the use of the RBC micropolarity assay in diagnostics, in assessing the response to therapies and in fully describing the progression of T1DM and T2DM, complementing HbA1c in defining the quality of long-term management of diabetes [ [10] , [49] ].…”

Section: Discussionmentioning

confidence: 99%

Red blood cells membrane micropolarity as a novel diagnostic indicator of type 1 and type 2 diabetes

Bianchetti

Giacinto

Pitocco

et al. 2019

Analytica Chimica Acta: X

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Classification of the category of diabetes is extremely important for clinicians to diagnose and select the correct treatment plan. Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins, as well as impairment in cholesterol homeostasis, can alter lipid density, packing, and interactions of Red blood cells (RBC) plasma membranes in type 1 and type 2 diabetes, thus varying their membrane micropolarity. This can be estimated, at a submicrometric scale, by determining the membrane relative permittivity, which is the factor by which the electric field between the charges is decreased relative to vacuum. Here, we employed a membrane micropolarity sensitive probe to monitor variations in red blood cells of healthy subjects (n=16) and patients affected by type 1 (T1DM, n=10) and type 2 diabetes mellitus (T2DM, n=24) to provide a cost-effective and supplementary indicator for diabetes classification. We find a less polar membrane microenvironment in T2DM patients, and a more polar membrane microenvironment in T1DM patients compared to control healthy patients. The differences in micropolarity are statistically significant among the three groups (p<0.01). The role of serum cholesterol pool in determining these differences was investigated, and other factors potentially altering the response of the probe were considered in view of developing a clinical assay based on RBC membrane micropolarity. These preliminary data pave the way for the development of an innovative assay which could become a tool for diagnosis and progression monitoring of type 1 and type 2 diabetes.

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“…In this respect, we have already shown how increased membrane fluidity is characteristic of patients with T1DM. [7][8][9][10] The rationale of our study relies on the fact that several processes in T1DM, especially related to diabetic dyslipidaemia, are responsible for altering the physical properties of the erythrocyte membrane. 11,12 The objective of this work is to show how membrane fluidity in T1DM patient can provide more selectively to an index which allows discriminating patients with retinopathy (T1DM + RD), thus allowing to develop a novel marker of the presence of retinopathy in T1DM patients.…”

Section: Introductionmentioning

confidence: 99%

Erythrocyte membrane fluidity as a marker of diabetic retinopathy in type 1 diabetes mellitus

Bianchetti

Viti

Scupola

et al. 2020

Eur J Clin Investigation

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Background: A high level of glycosylated haemoglobin (HbA1c), which is a nonenzymatic glycosylation product, is correlated with an increased risk of developing microangiopathic complications in Diabetes Mellitus (DM). Erythrocyte membrane fluidity could provide a complementary index to monitor the development of complications since it is influenced by several hyperglycaemia-induced pathways and other independent risk factors. Materials and methods: 15 healthy controls and 33 patients with long-duration (≥20 years) type 1 Diabetes Mellitus (T1DM) were recruited. Diabetic subjects were classified into two groups: T1DM, constituted by 14 nonretinopathic patients, and

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A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity

Cited by 31 publications

References 33 publications

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Red blood cells membrane micropolarity as a novel diagnostic indicator of type 1 and type 2 diabetes

Erythrocyte membrane fluidity as a marker of diabetic retinopathy in type 1 diabetes mellitus

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