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      Hyperglycemia Reduces Efficiency of Brain Networks in Subjects with Type 2 Diabetes

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          Abstract

          Previous research has shown that the brain is an important target of diabetic complications. Since brain regions are interconnected to form a large-scale neural network, we investigated whether severe hyperglycemia affects the topology of the brain network in people with type 2 diabetes. Twenty middle-aged (average age: 54 years) individuals with poorly controlled diabetes (HbA1c: 8.9−14.6%, 74−136 mmol/mol) and 20 age-, sex-, and education-matched healthy volunteers were recruited. Graph theoretic network analysis was performed with axonal fiber tractography and tract-based spatial statistics (TBSS) using diffusion tensor imaging. Associations between the blood glucose level and white matter network characteristics were investigated. Individuals with diabetes had lower white matter network efficiency ( P<0.001) and longer white matter path length ( P<0.05) compared to healthy individuals. Higher HbA1c was associated with lower network efficiency ( r = −0.53, P = 0.001) and longer network path length ( r = 0.40, P<0.05). A disruption in local microstructural integrity was found in the multiple white matter regions and associated with higher HbA1c and fasting plasma glucose levels (corrected P<0.05). Poorer glycemic control is associated with lower efficiency and longer connection paths of the global brain network in individuals with diabetes. Chronic hyperglycemia in people with diabetes may disrupt the brain’s topological integration, and lead to mental slowing and cognitive impairment.

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          Most cited references19

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          Efficient Behavior of Small-World Networks

          We introduce the concept of efficiency of a network, measuring how efficiently it exchanges information. By using this simple measure small-world networks are seen as systems that are both globally and locally efficient. This allows to give a clear physical meaning to the concept of small-world, and also to perform a precise quantitative a nalysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.
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            Diabetic nephropathy: diagnosis, prevention, and treatment.

            Diabetic nephropathy is the leading cause of kidney disease in patients starting renal replacement therapy and affects approximately 40% of type 1 and type 2 diabetic patients. It increases the risk of death, mainly from cardiovascular causes, and is defined by increased urinary albumin excretion (UAE) in the absence of other renal diseases. Diabetic nephropathy is categorized into stages: microalbuminuria (UAE >20 microg/min and or =200 microg/min). Hyperglycemia, increased blood pressure levels, and genetic predisposition are the main risk factors for the development of diabetic nephropathy. Elevated serum lipids, smoking habits, and the amount and origin of dietary protein also seem to play a role as risk factors. Screening for microalbuminuria should be performed yearly, starting 5 years after diagnosis in type 1 diabetes or earlier in the presence of puberty or poor metabolic control. In patients with type 2 diabetes, screening should be performed at diagnosis and yearly thereafter. Patients with micro- and macroalbuminuria should undergo an evaluation regarding the presence of comorbid associations, especially retinopathy and macrovascular disease. Achieving the best metabolic control (A1c 1.0 g/24 h and increased serum creatinine), using drugs with blockade effect on the renin-angiotensin-aldosterone system, and treating dyslipidemia (LDL cholesterol <100 mg/dl) are effective strategies for preventing the development of microalbuminuria, in delaying the progression to more advanced stages of nephropathy and in reducing cardiovascular mortality in patients with type 1 and type 2 diabetes.
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              Defining and identifying communities in networks

              The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic, cellular or protein networks) or technological problems (optimization of large infrastructures). Several types of algorithm exist for revealing the community structure in networks, but a general and quantitative definition of community is still lacking, leading to an intrinsic difficulty in the interpretation of the results of the algorithms without any additional non-topological information. In this paper we face this problem by introducing two quantitative definitions of community and by showing how they are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a new local algorithm to detect communities which outperforms the existing algorithms with respect to the computational cost, keeping the same level of reliability. The new algorithm is tested on artificial and real-world graphs. In particular we show the application of the new algorithm to a network of scientific collaborations, which, for its size, can not be attacked with the usual methods. This new class of local algorithms could open the way to applications to large-scale technological and biological applications.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 June 2016
                2016
                : 11
                : 6
                : e0157268
                Affiliations
                [1 ]Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
                [2 ]Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Ansan, Korea
                [3 ]Division of Endocrinology and Metabolism, Department of Internal Medicine, KEPCO Medical Center, Seoul, Korea
                [4 ]Department of Psychiatry, Ulsan University School of Medicine, ASAN Medical Center, Seoul, Korea
                [5 ]Division of Endocrinology and Metabolism, Ulsan University College of Medicine, Seoul, Korea
                [6 ]Diabetes Center, ASAN Medical Center, Seoul, Korea
                Universidad Rey Juan Carlos, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MSK YWS. Performed the experiments: JHY MSS. Analyzed the data: DJK. Contributed reagents/materials/analysis tools: DJK MSK. Wrote the paper: DJK YWS MSK.

                Article
                PONE-D-16-07626
                10.1371/journal.pone.0157268
                4918925
                27336309
                4144c97d-a68b-4efd-ba0e-a83e122cae04
                © 2016 Kim et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 February 2016
                : 26 May 2016
                Page count
                Figures: 4, Tables: 2, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: NRF-2014R1A6A3A01057664
                Funded by: funder-id http://dx.doi.org/10.13039/501100005006, Asan Institute for Life Sciences, Asan Medical Center;
                Award ID: 14−326
                This study was supported by grants from the National Research Foundation of Korea (NRF-2014R1A6A3A01057664), and the ASAN Institute for Life Sciences (14−326). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and health sciences
                Diagnostic medicine
                Diabetes diagnosis and management
                HbA1c
                Biology and life sciences
                Biochemistry
                Proteins
                Hemoglobin
                HbA1c
                Computer and Information Sciences
                Network Analysis
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Biology and Life Sciences
                Anatomy
                Nervous System
                Central Nervous System
                Medicine and Health Sciences
                Anatomy
                Nervous System
                Central Nervous System
                Biology and Life Sciences
                Anatomy
                Brain
                Medicine and Health Sciences
                Anatomy
                Brain
                Custom metadata
                The informed consent form for this study states explicitly that only the authorized person could manage the data provided. Data will be available upon request to researchers by permission of IRB of Asan Medical Center. Requests should be submitted to MSK ( mskim@ 123456amc.seoul.kr ).

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