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Jayaraj RL, Azimullah S, Beiram R. Diabetes as a risk factor for Alzheimer's disease in the Middle East and its shared pathological mediators. Saudi J Biol Sci 2020; 27:736-750. [PMID: 32210695 PMCID: PMC6997863 DOI: 10.1016/j.sjbs.2019.12.028] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 12/14/2019] [Accepted: 12/18/2019] [Indexed: 02/07/2023] Open
Abstract
The incidence of Alzheimer's disease (AD) has risen exponentially worldwide over the past decade. A growing body of research indicates that AD is linked to diabetes mellitus (DM) and suggests that impaired insulin signaling acts as a crucial risk factor in determining the progression of this devastating disease. Many studies suggest people with diabetes, especially type 2 diabetes, are at higher risk of eventually developing Alzheimer's dementia or other dementias. Despite nationwide efforts to increase awareness, the prevalence of Diabetes Mellitus (DM) has risen significantly in the Middle East and North African (MENA) region which might be due to rapid urbanization, lifestyle changes, lack of physical activity and rise in obesity. Growing body of evidence indicates that DM and AD are linked because both conditions involve impaired glucose homeostasis and altered brain function. Current theories and hypothesis clearly implicate that defective insulin signaling in the brain contributes to synaptic dysfunction and cognitive deficits in AD. In the periphery, low-grade chronic inflammation leads to insulin resistance followed by tissue deterioration. Thus insulin resistance acts as a bridge between DM and AD. There is pressing need to understand on how DM increases the risk of AD as well as the underlying mechanisms, due to the projected increase in age related disorders. Here we aim to review the incidence of AD and DM in the Middle East and the possible link between insulin signaling and ApoE carrier status on Aβ aggregation, tau hyperphosphorylation, inflammation, oxidative stress and mitochondrial dysfunction in AD. We also critically reviewed mutation studies in Arab population which might influence DM induced AD. In addition, recent clinical trials and animal studies conducted to evaluate the efficiency of anti-diabetic drugs have been reviewed.
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Key Words
- AAV, Adeno-associated virus
- ABCA1, ATP binding cassette subfamily A member 1
- AD, Alzheimer’s disease
- ADAMTS9, ADAM Metallopeptidase With Thrombospondin Type 1 Motif 9
- AGPAT1, 1-acyl-sn-glycerol-3-phosphate acyltransferase alpha
- Alzheimer’s disease
- Anti-diabetic drugs
- ApoE, Apolipoprotein E
- Arab population
- Aβ, Amyloid-beta
- BACE1, Beta-secretase 1
- BBB, Blood-Brain Barrier
- BMI, Body mass index
- CALR, calreticulin gene
- CIP2A, Cancerous Inhibitor Of Protein Phosphatase 2A
- COX-2, Cyclooxygenase 2
- CSF, Cerebrospinal fluid
- DM, Diabetes mellitus
- DUSP9, Dual Specificity Phosphatase 9
- Diabetes mellitus
- ECE-1, Endotherin converting enzyme 1
- FDG-PET, Fluorodeoxyglucose- positron emission tomography
- FRMD4A, FERM Domain Containing 4A
- FTO, Fat Mass and Obesity Associated Gene
- GLP-1, Glucagon like peptide
- GNPDA2, Glucosamine-6-phosphate deaminase 2
- GSK-3β, Glycogen synthase kinase 3 beta
- IDE, Insulin degrading enzyme
- IGF-1, Insulin-like growth factor 1
- IR, Insulin receptor
- IR, Insulin resistance
- Insulin signaling
- LPA, Lipophosphatidic acid
- MC4R, Melanocortin 4 receptor
- MCI, Myocardial infarction
- MENA, Middle East North African
- MG-H1, Methylglyoxal-hydroimidazolone isomer trifluoroactic acid salt
- MRI, Magnetic resonance imaging
- NDUFS3, NADH:Ubiquinone Oxidoreductase Core Subunit S3
- NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells
- NFT, Neurofibrillary tangles
- NOTCH4, Neurogenic locus notch homolog protein 4
- PI3K, Phosphoinositide-3
- PP2A, Protein phosphatase 2
- PPAR-γ2, Peroxisome proliferator-activated receptor gamma 2
- Pit-PET, Pittsburgh compound B- positron emission tomography
- RAB1A, Ras-related protein 1A
- SORT, Sortilin
- STZ, Streptozotocin
- T1DM, Type 1 Diabetes Mellitus
- T2DM, Type 2 Diabetes Mellitus
- TCF7L2, Transcription Factor 7 Like 2
- TFAP2B, Transcription Factor AP-2 Beta
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Affiliation(s)
| | | | - Rami Beiram
- Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates
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Bianchetti G, Di Giacinto F, Pitocco D, Rizzi A, Rizzo GE, De Leva F, Flex A, di Stasio E, Ciasca G, De Spirito M, Maulucci G. Red blood cells membrane micropolarity as a novel diagnostic indicator of type 1 and type 2 diabetes. Anal Chim Acta X 2019; 3:100030. [PMID: 33117983 PMCID: PMC7587021 DOI: 10.1016/j.acax.2019.100030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 01/06/2023] Open
Abstract
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. Dynamic flux of cholesterol is differentially altered in T1DM and T2DM. Red blood cell senses the dynamic flux of lipids by changing its micropolarity. Laurdan can measure micropolarity in red blood cells membranes. Differences in micropolarity between the three groups are statistically significant. Red blood cell Micropolarity is an innovative assay for diabetes classification.
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Key Words
- DMPC, dimyristoylphosphatidylcholine
- DPPC, dipalmitoilphosphatidylcholine
- Diabetes mellitus
- Fluorescence lifetime microscopy
- HDL, high-density lipoproteins
- HDL-C, high-density lipoprotein cholesterol
- HbA1c, glycated Haemoglobin
- LDL, low-density lipoproteins
- LDL-C, low-density lipoprotein cholesterol
- Membrane micropolarity
- Metabolic imaging
- PC, phosphatydilcholine
- Personalized medicine
- RBC, red blood cells
- Red blood cells
- T1DM, Type 1 Diabetes Mellitus
- T2DM, Type 2 diabetes Mellitus
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Affiliation(s)
- Giada Bianchetti
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Istituto di Fisica, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Flavio Di Giacinto
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Istituto di Fisica, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Dario Pitocco
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Diabetes Care Unit, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Alessandro Rizzi
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Diabetes Care Unit, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Gaetano Emanuele Rizzo
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Diabetes Care Unit, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Francesca De Leva
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Diabetes Care Unit, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Andrea Flex
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Cardiovascular Disease Division, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Enrico di Stasio
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Istituto di Biochimica Clinica, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Gabriele Ciasca
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Istituto di Fisica, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Marco De Spirito
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Istituto di Fisica, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - Giuseppe Maulucci
- Fondazione Policlinico Universitario A, Gemelli IRCSS, Rome, Italy.,Istituto di Fisica, Università Cattolica Del Sacro Cuore, Rome, Italy
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