text:
been developed as a patient-focused decision-support tool to provide
   evidence-based advice for physicians in their daily efforts to optimize
   metabolic control in diabetes care of their patients on an
   individualized basis. For this purpose, KADIS (R) was established in
   terms of a personalized, interactive in silico simulation procedure,
   implemented into a problem-related diabetes health care network and
   evaluated under different conditions by conducting open-label mono- and
   polycentric trials, and a case-control study, and last but not least, by
   application in routine diabetes outpatient care. The trial outcomes
   clearly show that the recommendations provided to the physicians by
   KADIS (R) lead to significant improvement of metabolic control. This
   model-based decision-support system provides an excellent tool to
   effectively guide physicians in personalized decision-making to achieve
   optimal metabolic control for their patients. (C) 2010 Elsevier Ireland
   Ltd. All rights reserved.}},
DOI = {{10.1016/j.cmpb.2010.06.001}},
ISSN = {{0169-2607}},
Unique-ID = {{ISI:000291188900012}},
}

@article{ ISI:000290649500025,
Author = {Muthyala, Sudhakar and Raj, V. R. Rana and Mohanty, Mira and Mohanan, P.
   V. and Nair, Prabha D.},
Title = {{The reversal of diabetes in rat model using mouse insulin producing
   cells - A combination approach of tissue engineering and
   macroencapsulation}},
Journal = {{ACTA BIOMATERIALIA}},
Year = {{2011}},
Volume = {{7}},
Number = {{5}},
Pages = {{2153-2162}},
Month = {{MAY}},
Abstract = {{Type 1 diabetes is a chronic disorder resulting from the autoimmune
   destruction of insulin-producing cells, a leading cause of morbidity and
   mortality all over the world. In this study a tissue engineering
   approach was compared with a macroencapsulation approach to reverse type
   1 diabetes in a rat model. using mouse pancreatic progenitor cell
   (PPC)-derived islet-like clusters and mouse islets. For the tissue
   engineering approach the cells were cultured on gelatin scaffolds
   cross-linked with EDC in the presence of polyvinylpyrrolidone in vitro
   (GPE scaffolds), while for the macroencapsulation approach the cells
   were encapsulated in polyurethane-polyvinylpyrrolidone
   semi-Interpenetrating networks. In the combination approach the cells
   cultured on GPE scaffolds were further encapsulated in a
   polyurethane-polyvinylpyrrolidone capsule. Real time PCR studies and the
   glucose challenge assay have shown that cells on GPE scaffolds could
   express and secrete insulin and glucagon in vitro. However, under in
   vivo conditions the animals treated by the tissue engineering approach
   died within 15-20 days and showed no reversal of their diabetes, due to
   infiltration of immune cells such as CD4 and CD8 cells and macrophages.
   In the macroencapsulation approach the animals showed euglycemia within
   25 days, which was maintained for further 20 days, but after that the
   animals died. Interestingly, in the combination approach the animals
   showed reversal of hyperglycemia, and remained euglycemic for up to 3
   months. The time needed to achieve initial euglycemia was different with
   different cell types, i e. the combination approach with mouse Islets
   achieved euglycemia within 15 days, whereas with PPC-derived islet-like
   clusters euglycemia was achieved within 25 days This study confirmed
   that a combination of tissue engineering and macroencapsulation with
   mouse islets could reverse diabetes and maintain euglycemia in an
   experimental diabetes rat model for 90 days. (C) 2011 Acta Materialia
   Inc Published by Elsevier Ltd All rights reserved}},
DOI = {{10.1016/j.actbio.2011.01.036}},
ISSN = {{1742-7061}},
Unique-ID = {{ISI:000290649500025}},
}

@article{ ISI:000290024700023,
Author = {Eliasson, Bjorn and Svensson, Ann-Marie and Miftaraj, Mervete and
   Jonasson, Junmei Miao and Eeg-Olofsson, Katarina and Sundell, Karolina
   Andersson and Gudbjornsdottir, Soffia},
Title = {{Clinical Use and Effectiveness of Lipid Lowering Therapies in Diabetes
   Mellitus-An Observational Study from the Swedish National Diabetes
   Register}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{4}},
Month = {{APR 29}},
Abstract = {{Objectives: To describe the use and evaluate the effectiveness of
   different lipid lowering therapies in unselected patients with type 1
   and type 2 diabetes in clinical practice.
   Design: Observational population-based study using the personal
   identification number to link information from the National Diabetes
   Register, the Prescribed Drug Register and the Patient register in
   Sweden. All patients in the NDR aged 18-75 years with diabetes more than
   one year were eligible, but only patients starting any lipid lowering
   treatment with at least three prescriptions 1 July 2006-30 June 2007
   were included (n = 37182). The mean blood lipid levels in 2008 and
   reductions in LDL cholesterol were examined.
   Results: Blood lipid levels were similar in patients treated with
   simvastatin, atorvastatin and rosuvastatin, showing similar lipid
   lowering effect as currently used. Users of pravastatin, fluvastatin,
   ezetimib and fibrate more seldom reach treatment goals. Moderate daily
   doses of the statins were used, with 76\% of simvastatin users taking 20
   mg or less, 48\% of atorvastatin users taking 10 mg, 55\% of pravastatin
   users taking 20 mg, and 76\% of rosuvastatin users taking 5 or 10 mg.
   Conclusions: This observational study shows that the LDL-C levels in
   patients taking simvastatin, atorvastatin or rosuvastatin are very
   similar as currently used, as well as their LDL-C lowering abilities.
   There is potential to intensify lipid lowering treatment to reduce the
   remaining high residual risk and achieve better fulfilment of treatment
   goals, since the commonly used doses are only low to moderate.}},
DOI = {{10.1371/journal.pone.0018744}},
Article-Number = {{e18744}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Andersson Sundell, Karolina/E-6046-2010
   Miao Jonasson, Junmei/B-3242-2014}},
Unique-ID = {{ISI:000290024700023}},
}

@article{ ISI:000289238700029,
Author = {Esguerra, Jonathan Lou S. and Bolmeson, Caroline and Cilio, Corrado M.
   and Eliasson, Lena},
Title = {{Differential Glucose-Regulation of MicroRNAs in Pancreatic Islets of
   Non-Obese Type 2 Diabetes Model Goto-Kakizaki Rat}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{4}},
Month = {{APR 7}},
Abstract = {{Background: The Goto-Kakizaki (GK) rat is a well-studied non-obese
   spontaneous type 2 diabetes (T2D) animal model characterized by impaired
   glucose-stimulated insulin secretion (GSIS) in the pancreatic beta
   cells. MicroRNAs (miRNAs) are short regulatory RNAs involved in many
   fundamental biological processes. We aim to identify miRNAs that are
   differentially-expressed in the pancreatic islets of the GK rats and
   investigate both their short-and long term glucose-dependence during
   glucose-stimulatory conditions.
   Methodology/Principal Findings: Global profiling of 348 miRNAs in the
   islets of GK rats and Wistar controls (females, 60 days, N = 6 for both
   sets) using locked nucleic acid (LNA)-based microarrays allowed for the
   clear separation of the two groups. Significant analysis of microarrays
   (SAM) identified 30 differentially-expressed miRNAs, 24 of which are
   predominantly upregulated in the GK rat islets. Monitoring of
   qPCR-validated miRNAs during GSIS experiments on isolated islets showed
   disparate expression trajectories between GK and controls indicating
   distinct short-and long-term glucose dependence. We specifically found
   expression of rno-miR-130a, rno-miR-132, rno-miR-212 and rno-miR-335 to
   be regulated by hyperglycaemia. The putative targets of upregulated
   miRNAs in the GK, filtered with glucose-regulated mRNAs, were found to
   be enriched for insulin-secretion genes known to be downregulated in T2D
   patients. Finally, the binding of rno-miR-335 to a fragment of the 3'UTR
   of one of known down-regulated exocytotic genes in GK islets, Stxbp1 was
   shown by luciferase assay.
   Conclusions/Significance: The perturbed miRNA network found in the GK
   rat islets is indicative of a system-wide impairment in the regulation
   of genes important for the normal functions of pancreatic islets,
   particularly in processes involving insulin secretion during glucose
   stimulatory conditions. Our findings suggest that the reduced insulin
   secretion observed in the GK rat may be partly due to upregulated miRNA
   expression leading to decreased production of key proteins of the
   insulin exocytotic machinery.}},
DOI = {{10.1371/journal.pone.0018613}},
Article-Number = {{e18613}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Esguerra, Jonathan/M-2366-2014
   }},
ORCID-Numbers = {{Esguerra, Jonathan/0000-0001-8454-6606
   Eliasson, Lena/0000-0002-6467-5029}},
Unique-ID = {{ISI:000289238700029}},
}

@article{ ISI:000288894800048,
Author = {Wu, Margaret and Singh, Sheo B. and Wang, Jun and Chung, Christine C.
   and Salituro, Gino and Karanam, Bindhu V. and Lee, Sang Ho and Powles,
   Maryann and Ellsworth, Kenneth P. and Lassman, Michael E. and Miller,
   Corey and Myers, Robert W. and Tota, Michael R. and Zhang, Bei B. and
   Li, Cai},
Title = {{Antidiabetic and antisteatotic effects of the selective fatty acid
   synthase (FAS) inhibitor platensimycin in mouse models of diabetes}},
Journal = {{PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA}},
Year = {{2011}},
Volume = {{108}},
Number = {{13}},
Pages = {{5378-5383}},
Month = {{MAR 29}},
Abstract = {{Platensimycin (PTM) is a recently discovered broad-spectrum antibiotic
   produced by Streptomyces platensis. It acts by selectively inhibiting
   the elongation-condensing enzyme FabF of the fatty acid biosynthesis
   pathway in bacteria. We report here that PTM is also a potent and highly
   selective inhibitor of mammalian fatty acid synthase. In contrast to two
   agents, C75 and cerulenin, that are widely used as inhibitors of
   mammalian fatty acid synthase, platensimycin specifically inhibits fatty
   acid synthesis but not sterol synthesis in rat primary hepatocytes. PTM
   preferentially concentrates in liver when administered orally to mice
   and potently inhibits hepatic de novo lipogenesis, reduces fatty acid
   oxidation, and increases glucose oxidation. Chronic administration of
   platensimycin led to a net reduction in liver triglyceride levels and
   improved insulin sensitivity in db/+ mice fed a high-fructose diet. PTM
   also reduced ambient glucose levels in db/db mice. These results provide
   pharmacological proof of concept of inhibiting fatty acid synthase for
   the treatment of diabetes and related metabolic disorders in animal
   models.}},
DOI = {{10.1073/pnas.1002588108}},
ISSN = {{0027-8424}},
Unique-ID = {{ISI:000288894800048}},
}

@article{ ISI:000288219100002,
Author = {Berisha, Stela Z. and Serre, David and Schauer, Philip and Kashyap,
   Sangeeta R. and Smith, Jonathan D.},
Title = {{Changes in Whole Blood Gene Expression in Obese Subjects with Type 2
   Diabetes Following Bariatric Surgery: a Pilot Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{3}},
Month = {{MAR 10}},
Abstract = {{Background: A pilot study was performed in order to investigate the
   effects of bariatric surgery on whole blood gene expression profiles in
   obese subjects with type 2 diabetes.
   Methodology/Principal Findings: Whole blood from eleven obese subjects
   with type 2 diabetes was collected in PAXgene tubes prior to and 6-12
   months after bariatric surgery. Total RNA was isolated, amplified,
   labeled and hybridized to Illumina gene expression microarrays. Clinical
   and expression data were analyzed using a paired t-test, and
   correlations between changes in clinical trait and transcript levels
   were calculated. Pathways were identified using Ingenuity Pathway
   Analysis and DAVID gene ontology software. Overall, bariatric surgery
   resulted in significant reduction of body mass index, fasting plasma
   glucose, fasting plasma insulin, and normalization of glycosylated
   hemoglobin levels. The expression levels of 204 transcripts,
   representing 200 unique genes, were significantly altered after
   bariatric surgery. Among the significantly regulated genes were GGT1,
   CAMP, DEFA1, LCN2, TP53, PDSS1, OLR1, CNTNAP5, DHCR24, HHAT and SARDH,
   which have been previously implicated in lipid metabolism, obesity
   and/or type 2 diabetes. Selected findings were replicated by
   quantitative real-time-PCR. The changes in expression of seven
   transcripts, WDR35, FLF45244, DHCR24, TIGD7, TOPBP1, TSHZ1, and FAM8A1
   were strongly correlated with the changes in body weight, fasting plasma
   glucose and glycosylated hemoglobin content. The top pathways associated
   with gene expression changes after bariatric surgery was lipid
   metabolism, small molecule biochemistry and gene expression. Two
   antimicrobial peptides were among the transcripts with the largest
   changes in gene expression after bariatric surgery.
   Conclusions/Significance: Data from this pilot study suggest that whole
   blood expression levels of specific transcripts may be useful as
   biomarkers associated with susceptibility for type 2 diabetes and/or
   therapeutic response.}},
DOI = {{10.1371/journal.pone.0016729}},
Article-Number = {{e16729}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000288219100002}},
}

@article{ ISI:000289531200012,
Author = {Lu, Pinyi and Bevan, David R. and Lewis, Stephanie N. and Hontecillas,
   Raquel and Bassaganya-Riera, Josep},
Title = {{Molecular modeling of lanthionine synthetase component C-like protein 2:
   a potential target for the discovery of novel type 2 diabetes
   prophylactics and therapeutics}},
Journal = {{JOURNAL OF MOLECULAR MODELING}},
Year = {{2011}},
Volume = {{17}},
Number = {{3}},
Pages = {{543-553}},
Month = {{MAR}},
Abstract = {{The rates of type 2 diabetes (T2D) are rising to epidemic proportions in
   the US and worldwide. While current T2D medications are efficacious,
   significant side effects have limited their use and availability. Our
   laboratory has discovered that abscisic acid (ABA) exerts anti-diabetic
   effects, in part, by activating peroxisome proliferator-activated
   receptor gamma (PPAR gamma). However, since ABA does not bind to the
   ligand-binding domain (LBD) of PPAR gamma, the mechanism of activation
   of PPAR gamma by ABA remains unknown. Lanthionine synthetase component
   C-like protein 2 (LANCL2) was predicted to be a novel target for the
   binding and signaling of ABA in human granulocytes and rat insulinoma
   cells. The goal of this study was to determine whether LANCL2 is a
   molecular target of ABA and other PPAR gamma agonists. To this end we
   performed homology modeling to construct a three-dimensional structure
   of LANCL2 using the crystal structure of LANCL1 as a template. Our
   molecular docking studies predicted that ABA and other PPAR gamma
   agonists (e.g., rosiglitazone and pioglitazone) share a binding site on
   the surface of LANCL2. The identification of a binding site for PPAR
   gamma agonists will facilitate the high-throughput virtual screening of
   large compound libraries and may shed new light on alternative
   mechanisms of PPAR gamma activation.}},
DOI = {{10.1007/s00894-010-0748-y}},
ISSN = {{1610-2940}},
EISSN = {{0948-5023}},
ORCID-Numbers = {{Lu, Pinyi/0000-0003-1962-5783}},
Unique-ID = {{ISI:000289531200012}},
}

@article{ ISI:000288643000004,
Author = {Morbiducci, Umberto and Di Benedetto, Giacomo and Kautzky-Willer,
   Alexandra and Deriu, Marco A. and Pacini, Giovanni and Tura, Andrea},
Title = {{Identification of a model of non-esterified fatty acids dynamics through
   genetic algorithms: The case of women with a history of gestational
   diabetes}},
Journal = {{COMPUTERS IN BIOLOGY AND MEDICINE}},
Year = {{2011}},
Volume = {{41}},
Number = {{3}},
Pages = {{146-153}},
Month = {{MAR}},
Abstract = {{Elevation in non-esterified fatty acids (NEFA) has been shown to
   modulate insulin secretion and it is considered as a risk factor for the
   development of type 2 diabetes. Here we present a method that
   complements a mathematical model of NEFA kinetics with genetic
   algorithms for model identification. The complemented strategy allowed
   to assess parameters of NEFA kinetics and to get insight into their
   relationship with insulin during oral glucose tolerance tests in women
   with former gestational diabetes: (i) providing a reliable estimation of
   the model parameters, (ii) assuring the usability of the model, and
   (iii) promoting and facilitating its application in a clinical context.
   (C) 2011 Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.compbiomed.2011.01.004}},
ISSN = {{0010-4825}},
ResearcherID-Numbers = {{Morbiducci, Umberto/A-7341-2012
   }},
ORCID-Numbers = {{Kautzky-Willer, Alexandra/0000-0002-3520-4105}},
Unique-ID = {{ISI:000288643000004}},
}

@article{ ISI:000287855600002,
Author = {Galvanin, Federico and Barolo, Massimiliano and Macchietto, Sandro and
   Bezzo, Fabrizio},
Title = {{Optimal design of clinical tests for the identification of physiological
   models of type 1 diabetes in the presence of model mismatch}},
Journal = {{MEDICAL \& BIOLOGICAL ENGINEERING \& COMPUTING}},
Year = {{2011}},
Volume = {{49}},
Number = {{3}},
Pages = {{263-277}},
Month = {{MAR}},
Abstract = {{How to design a clinical test aimed at identifying in the safest, most
   precise and quickest way the subject-specific parameters of a detailed
   model of glucose homeostasis in type 1 diabetes is the topic of this
   article. Recently, standard techniques of model-based design of
   experiments (MBDoE) for parameter identification have been proposed to
   design clinical tests for the identification of the model parameters for
   a single type 1 diabetic individual. However, standard MBDoE is affected
   by some limitations. In particular, the existence of a structural
   mismatch between the responses of the subject and that of the model to
   be identified, together with initial uncertainty in the model parameters
   may lead to design clinical tests that are sub-optimal (scarcely
   informative) or even unsafe (the actual response of the subject might be
   hypoglycaemic or strongly hyperglycaemic). The integrated use of two
   advanced MBDoE techniques (online model-based redesign of experiments
   and backoff-based MBDoE) is proposed in this article as a way to
   effectively tackle the above issue. Online model-based experiment
   redesign is utilised to exploit the information embedded in the
   experimental data as soon as the data become available, and to adjust
   the clinical test accordingly whilst the test is running. Backoff-based
   MBDoE explicitly accounts for model parameter uncertainty, and allows
   one to plan a test that is both optimally informative and safe by
   design. The effectiveness and features of the proposed approach are
   assessed and critically discussed via a simulated case study based on
   state-of-the-art detailed models of glucose homeostasis. It is shown
   that the proposed approach based on advanced MBDoE techniques allows
   defining safe, informative and subject-tailored clinical tests for model
   identification, with limited experimental effort.}},
DOI = {{10.1007/s11517-010-0717-8}},
ISSN = {{0140-0118}},
ResearcherID-Numbers = {{Barolo, Massimiliano/D-7872-2013
   }},
ORCID-Numbers = {{Macchietto, Sandro/0000-0001-6096-8126
   Barolo, Massimiliano/0000-0002-8125-5704}},
Unique-ID = {{ISI:000287855600002}},
}

@article{ ISI:000288533600006,
Author = {Avdal, Elif Unsal and Kizilci, Sevgi and Demirel, Neslihan},
Title = {{The Effects of Web-Based Diabetes Education on Diabetes Care Results A
   Randomized Control Study}},
Journal = {{CIN-COMPUTERS INFORMATICS NURSING}},
Year = {{2011}},
Volume = {{29}},
Number = {{2}},
Pages = {{101-106}},
Month = {{FEB}},
Abstract = {{This study aimed to test the effects of providing Web-based diabetes
   education to individuals with type 2 diabetes on the A(1c) level and
   health check attendance. The study participants comprised 122
   individuals with type 2 diabetes, who had access to the Internet, had
   completed their basic diabetes education, and had similar basic
   situational factors. Using a randomization method, these participants
   were chosen from the patients being monitored by the diabetes nurses.
   The experimental group (n = 61) was monitored via the Web. From
   measurements recorded in the sixth month of monitoring, we found that
   A(1c) levels of the individuals with diabetes who were monitored through
   the Web decreased (t = 6.63; P < .05), and the rate of attending health
   check visits increased (z = 5.97; P < .05), while no difference was
   detected in the control group (t = -0.63; P = .534; z = -0.80; P =
   .426). To maintain glycemic control, Web use could be adopted as a
   complementary tool for monitoring individuals with diabetes.}},
DOI = {{10.1097/NCN.0b013e3181fcbdc6}},
ISSN = {{1538-2931}},
Unique-ID = {{ISI:000288533600006}},
}

@article{ ISI:000288074200005,
Author = {Avdal, Elif Uensal and Kizilci, Sevgi and Demirel, Neslihan},
Title = {{The Effects of Web-Based Diabetes Education on Diabetes Care Results A
   Randomized Control Study}},
Journal = {{CIN-COMPUTERS INFORMATICS NURSING}},
Year = {{2011}},
Volume = {{29}},
Number = {{2}},
Pages = {{TC29-TC34}},
Month = {{FEB}},
Abstract = {{This study aimed to test the effects of providing Web-based diabetes
   education to individuals with type 2 diabetes on the A(1c) level and
   health check attendance. The study participants comprised 122
   individuals with type 2 diabetes, who had access to the Internet, had
   completed their basic diabetes education, and had similar basic
   situational factors. Using a randomization method, these participants
   were chosen from the patients being monitored by the diabetes nurses.
   The experimental group (n = 61) was monitored via the Web. From
   measurements recorded in the sixth month of monitoring, we found that
   A1c levels of the individuals with diabetes who were monitored through
   the Web decreased (t = 6.63; P G.05), and the rate of attending health
   check visits increased (z= 5.97; P <.05), while no difference was
   detected in the control group (t = -0.63; P =.534; z = -0.80; P =.426).
   To maintain glycemic control, Web use could be adopted as a
   complementary tool for monitoring individuals with diabetes.}},
DOI = {{10.1097/NCN.0b013e3182155318}},
ISSN = {{1538-2931}},
Unique-ID = {{ISI:000288074200005}},
}

@article{ ISI:000286698100024,
Author = {Kim, Hyunuk and Park, Hongil and Lee, Juho and Kim, Tae Hyung and Lee,
   Eun Seong and Oh, Kyung Taek and Lee, Kang Choon and Youn, Yu Seok},
Title = {{Highly porous large poly(lactic-co-glycolic acid) microspheres adsorbed
   with palmityl-acylated exendin-4 as a long-acting inhalation system for
   treating diabetes}},
Journal = {{BIOMATERIALS}},
Year = {{2011}},
Volume = {{32}},
Number = {{6}},
Pages = {{1685-1693}},
Month = {{FEB}},
Abstract = {{A porous large poly(lactic-co-glycolic acid) (PLGA) microspheres (MS)
   adsorbed with palmityl-acylated exendin-4 (Ex4-C(16)) was devised as an
   inhalation delivery system. The porous MS was prepared by a single o/w
   emulsification/solvent evaporation method using extractable Pluronic
   F68/F127, and its fabrication and formulation conditions were carefully
   optimized. Results show that the prepared MS was in the appropriate size
   range for inhalation and contained many surfaces and internal pores
   meaning low aerodynamic density. Ex4-C(16) was more efficiently adsorbed
   onto porous PLGA MSs than native exendin-4, and an approximately 5\%
   loading of Ex4-C(16) onto this porous MS (RG504H) was achieved. This
   optimized porous MS was found to be efficiently deposited throughout the
   entire lungs of mice including alveoli region. Furthermore, this porous
   MS adsorbed with Ex4-C(16) (approx. 100 mu g/mouse) displayed much
   protracted hypoglycemic efficacy in non-fasted type 2 diabetic db/db
   mice. Porous PLGA MS with adsorbed Ex4-C(16) showed the dual-advantages
   of (i) sustained release and acceptable drug-loading due to strong
   hydrophobic interaction and (ii) longer in vivo pulmonary hypoglycemic
   duration due to albumin-binding by the palmityl group. We consider that
   this new prototype of porous PLGA MS has considerable pharmaceutical
   potential as a type 2 anti-diabetic inhalation treatment. (C) 2010
   Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.biomaterials.2010.10.045}},
ISSN = {{0142-9612}},
Unique-ID = {{ISI:000286698100024}},
}

@article{ ISI:000286388300012,
Author = {Lee, Chang-Shing and Wang, Mei-Hui},
Title = {{A Fuzzy Expert System for Diabetes Decision Support Application}},
Journal = {{IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS}},
Year = {{2011}},
Volume = {{41}},
Number = {{1}},
Pages = {{139-153}},
Month = {{FEB}},
Abstract = {{An increasing number of decision support systems based on domain
   knowledge are adopted to diagnose medical conditions such as diabetes
   and heart disease. It is widely pointed that the classical ontologies
   cannot sufficiently handle imprecise and vague knowledge for some real
   world applications, but fuzzy ontology can effectively resolve data and
   knowledge problems with uncertainty. This paper presents a novel fuzzy
   expert system for diabetes decision support application. A five-layer
   fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation
   layer, fuzzy group domain layer, fuzzy personal relation layer, and
   fuzzy personal domain layer, is developed in the fuzzy expert system to
   describe knowledge with uncertainty. By applying the novel fuzzy
   ontology to the diabetes domain, the structure of the fuzzy diabetes
   ontology (FDO) is defined to model the diabetes knowledge. Additionally,
   a semantic decision support agent (SDSA), including a knowledge
   construction mechanism, fuzzy ontology generating mechanism, and
   semantic fuzzy decision making mechanism, is also developed. The
   knowledge construction mechanism constructs the fuzzy concepts and
   relations based on the structure of the FDO. The instances of the FDO
   are generated by the fuzzy ontology generating mechanism. Finally, based
   on the FDO and the fuzzy ontology, the semantic fuzzy decision making
   mechanism simulates the semantic description of medical staff for
   diabetes-related application. Importantly, the proposed fuzzy expert
   system can work effectively for diabetes decision support application.}},
DOI = {{10.1109/TSMCB.2010.2048899}},
ISSN = {{1083-4419}},
Unique-ID = {{ISI:000286388300012}},
}

@inproceedings{ ISI:000309191900044,
Author = {Omar, Wail M. and Ati, Modafar},
Editor = {{Abdulrazak, B and Giroux, S and Bouchard, B and Pigot, H and Mokhtari, M}},
Title = {{Smart Diabetes Management System Based on Service Oriented Architecture}},
Booktitle = {{TOWARD USEFUL SERVICES FOR ELDERLY AND PEOPLE WITH DISABILITIES}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2011}},
Volume = {{6719}},
Pages = {{290-295}},
Note = {{9th International Conference on Smart Homes and Health Telematics (ICOST
   2011), Montreal, CANADA, JUN 20-22, 2011}},
Organization = {{Univ Sherbrooke, DOMUS Lab; Univ Sherbrooke, Fac Sci; Canadian Inst
   Health Res; Ecole Technol Superleure; Alcatellucent Bell Lab; Univ
   Quebec a Chicoutimi; Quebec Network Res Ag; Inst Telecom Telecom \&
   Management; Univ Sherbrooke, Fac Genie; Kramer Ind}},
Abstract = {{Diabetes is one of the chronic diseases that have an effect on the life
   of a large number of people around the world. This chronic disease
   causes approximately 5\% of all deaths globally each year{[}1], and is
   most likely to increase exponentially within the coming few years.
   Reduction to the effect of such a disease, a close monitoring to
   patients is essential in order to control its level via increasing
   patients' awareness. Thus a smart system that is capable of collecting
   medical readings from patients, analyzing the data, and suggesting the
   appropriate diagnosis has become a necessity. This work advises a system
   that is capable of meeting all of the above along with reducing the
   physical access load on healthcare centers via instructing the system to
   notify both healthcare provider and patients with the acquired results}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-21534-6}},
Unique-ID = {{ISI:000309191900044}},
}

@inproceedings{ ISI:000308666100045,
Author = {Marcano-Cedeno, Alexis and Torres, Joaquin and Andina, Diego},
Editor = {{Ferrandez, JM and Sanchez, JRA and DeLaPaz, F and Toledo, FJ}},
Title = {{A Prediction Model to Diabetes Using Artificial Metaplasticity}},
Booktitle = {{NEW CHALLENGES ON BIOINSPIRED APPLICATIONS: 4TH INTERNATIONAL
   WORK-CONFERENCE ON THE INTERPLAY BETWEEN NATURAL AND ARTIFICIAL
   COMPUTATION, IWINAC 2011, PART II}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2011}},
Volume = {{6687}},
Pages = {{418-425}},
Note = {{4th International Work-Conference on the Interplay Between Natural and
   Artificial Computation (IWINAC)/Joint Workshop and Summer School on
   Astrostatistics and Data Mining in Large Astronomical Databases, La
   Palma, SPAIN, MAY 30-JUN 03, 2011}},
Organization = {{UNED; Univ Politecnica Cartagena; Spanish Minist Ciencia \& Innovac;
   CYTED; Red Nacl Computac Nat \& Artificial; APLIQUEM S L}},
Abstract = {{Diabetes is the most common disease nowadays in all populations and in
   all age groups. Different techniques of artificial intelligence has been
   applied to diabetes problem. This research proposed the artificial
   metaplasticity on multilayer perceptron (AMMLP) as prediction model for
   prediction of diabetes. The Pima Indians diabetes was used to test the
   proposed model AMMLP. The results obtained by AMMLP were compared with
   other algorithms, recently proposed by other researchers, that were
   applied to the same database. The best result obtained so far with the
   AMMLP algorithm is 89.93\%.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-21326-7}},
ORCID-Numbers = {{Andina, Diego/0000-0001-7036-2646
   ANDINA DE LA FUENTE, DIEGO/0000-0002-0022-7403}},
Unique-ID = {{ISI:000308666100045}},
}

@inproceedings{ ISI:000305356500017,
Author = {Wickramasinghe, Kumari and Alahakoon, Damminda and Schattner, Peter and
   Georgeff, Michael},
Editor = {{Wang, DH and Reynolds, M}},
Title = {{Self-Organizing Maps for Translating Health Care Knowledge: A Case Study
   in Diabetes Management}},
Booktitle = {{AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE}},
Series = {{Lecture Notes in Artificial Intelligence}},
Year = {{2011}},
Volume = {{7106}},
Pages = {{162-171}},
Note = {{24th Australasian Joint Conference on Artificial Intelligence (AI),
   Perth, AUSTRALIA, DEC 05-08, 2011}},
Abstract = {{Chronic Disease Management (CDM) is an important area of health care
   where Health Knowledge Management can provide substantial benefits. A
   web-based chronic disease management service, called cdmNet, is
   accumulating detailed data on CDM as it is being rolled out across
   Australia. This paper presents the application of unsupervised neural
   networks to cdmNet data to: (1) identify interesting patterns in
   diabetes data; and (2) assist diabetes related policy-making at
   different levels. The work is distinct from existing research in: (1)
   the data; (2) the objectives; and (3) the techniques used. The data
   represents the diabetes population across the entire primary care
   sector. The objectives include diabetes related decision and policy
   making at different levels. The pattern recognition techniques combine a
   traditional approach to data mining,involving the Self-Organizing Map
   (SOM), with an extension to include the Growing Self-Organizing Map
   (GSOM).}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-25831-2}},
Unique-ID = {{ISI:000305356500017}},
}

@inproceedings{ ISI:000304028100052,
Author = {Kasemthaweesab, Pornnapas and Kurutach, Werasak},
Editor = {{Jedrzejowicz, P and Nguyen, NT and Hoang, K}},
Title = {{Study of Diabetes Mellitus (DM) with Ophthalmic Complication Using
   Association Rules of Data Mining Technique}},
Booktitle = {{COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT
   I}},
Series = {{Lecture Notes in Artificial Intelligence}},
Year = {{2011}},
Volume = {{6922}},
Pages = {{527-536}},
Note = {{3rd International Conference on Computational Collective Intelligence
   (ICCCI 2011), Gdynia Maritime Univ, Gdynia, POLAND, SEP 21-23, 2011}},
Abstract = {{Association Rule Discovery is a significant data mining technique. In
   this paper, we applied this technique to discover fundamental
   association among a data set of diabetes mellitus (DM) patients with
   ophthalmic complication using a classifier based on gender, age and
   payment method of treatment expense. The result indicated that
   ``diabetes mellitus (DM) patients Type II aging between 60-69 years old
   with no occupation whose payment for their treatment expense was by
   Government Official Rights of Continuous Treatment tended to have
   diabetes mellitus (DM) with ophthalmic complication.{''} This conclusion
   is useful for healthcare treatment of adulthood patients, welfare
   improvement of public healthcare, provision of helpful recommendation
   for diabetes mellitus patients and further development in finding
   disease complication.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-23934-2}},
Unique-ID = {{ISI:000304028100052}},
}

@inproceedings{ ISI:000303349700024,
Author = {Georga, Eleni I. and Protopappas, Vasilios C. and Fotiadis, Dimitrios I.},
Editor = {{Lin, JC and Nikita, KS}},
Title = {{Predictive Metabolic Modeling for Type 1 Diabetes Using Free-Living Data
   on Mobile Devices}},
Booktitle = {{WIRELESS MOBILE COMMUNICATION AND HEALTHCARE}},
Series = {{Lecture Notes of the Institute for Computer Sciences Social Informatics
   and Telecommunications Engineering}},
Year = {{2011}},
Volume = {{55}},
Pages = {{187-193}},
Note = {{2nd International ICST Conference on Wireless Mobile Communication and
   Healthcare, Ayia Napa, CYPRUS, OCT 18-20, 2010}},
Organization = {{ICST}},
Abstract = {{This study presents a metabolic modeling scheme for glucose prediction
   of diabetic patients that is intended for use in mobile devices. We
   investigate the ability to model the multivariate, nonlinear and dynamic
   interactions in glucose metabolism using free-living data acquired from
   wearable sensors or inserted through suitable mobile applications. The
   physiological processes related to diabetes are simulated by
   compartmental models, which quantify the absorption of subcutaneously
   administered insulin, the absorption of glucose from the gut following a
   meal, as well as the effects of exercise on plasma glucose and insulin
   dynamics. In addition, Support Vector machines for Regression are
   employed to provide individualized predictions of the subcutaneous
   glucose concentrations. The proposed scheme is evaluated in terms of its
   predictive ability using real data recorded from two type 1 diabetic
   patients. Also, the incorporation of the predictive model in an
   integrated diabetes monitoring and management system is discussed.}},
ISSN = {{1867-8211}},
ISBN = {{978-3-642-20866-9}},
Unique-ID = {{ISI:000303349700024}},
}

@inproceedings{ ISI:000302374500047,
Author = {Ahmad, Aliza and Mustapha, Aida and Zahadi, Eliza Dianna and Masah,
   Norhayati and Yahaya, Nur Yasmin},
Editor = {{Snasel, V and Platos, J and ElQawasmeh, E}},
Title = {{Comparison between Neural Networks against Decision Tree in Improving
   Prediction Accuracy for Diabetes Mellitus}},
Booktitle = {{DIGITAL INFORMATION PROCESSING AND COMMUNICATIONS, PT 1}},
Series = {{Communications in Computer and Information Science}},
Year = {{2011}},
Volume = {{188}},
Pages = {{537-545}},
Note = {{International Conference on Digital Information Processing and
   Communications (ICDIPC 2011), VSB Tech Univ Ostrava, Ostrava, CZECH
   REPUBLIC, JUL 07-09, 2011}},
Organization = {{Springer; VSB Tech Univ Ostrava; Soc Digital Informat \& Wireless Commun}},
Abstract = {{This study is to compare the prediction accuracy of multilayer
   perceptron in neural networks against tree-based algorithms, in
   particular the ID3 and 148 algorithms on Pima Indian diabetes mellitus
   data set. The classification experiment is performed using algorithms in
   WEKA to determine the class diabetes or non-diabetes with the data set
   of 768 patients. Results showed that a pruned J48 tree performed with
   higher accuracy, which is 89.3\% as compared to 81.9\% by the multilayer
   perceptrons. On further removal of the number of times pregnant
   attribute, the prediction accuracy for the pruned J48 tree improved to
   89.7\%.}},
ISSN = {{1865-0929}},
ISBN = {{978-3-642-22388-4}},
ORCID-Numbers = {{MUSTAPHA, AIDA/0000-0002-9077-4995}},
Unique-ID = {{ISI:000302374500047}},
}

@inproceedings{ ISI:000301289400019,
Author = {Greiner, Dale L. and Brehm, Michael A. and Hosur, Vishnu and Harlan,
   David M. and Powers, Alvin C. and Shultz, Leonard D.},
Book-Group-Author = {{Annals NY Acad Sci}},
Title = {{Humanized mice for the study of type 1 and type 2 diabetes}},
Booktitle = {{ANIMAL MODELS: THEIR VALUE IN PREDICTING DRUG EFFICACY AND TOXICITY}},
Series = {{Annals of the New York Academy of Sciences}},
Year = {{2011}},
Volume = {{1245}},
Pages = {{55-58}},
Note = {{Conference on Animal Models and Their Value in Predicting Drug Efficacy
   and Toxicity, New York, NY, SEP 15-16, 2011}},
Abstract = {{The availability of immunodeficient mice engrafted with functional human
   immune systems and islets permits in vivo study of human diabetes
   without putting patients at risk.}},
DOI = {{10.1111/j.1749-6632.2011.06318.x}},
ISSN = {{0077-8923}},
ISBN = {{978-1-57331-875-4}},
Unique-ID = {{ISI:000301289400019}},
}

@inproceedings{ ISI:000298810002317,
Author = {Fioravanti, A. and Fico, G. and Arredondo, M. T. and Leuteritz, J. -P.},
Book-Group-Author = {{IEEE}},
Title = {{A Mobile Feedback System for Integrated E-health Platforms to improve
   Self-Care and Compliance of Diabetes Mellitus Patients}},
Booktitle = {{2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2011}},
Pages = {{3550-3553}},
Note = {{33rd Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBS), Boston, MA, AUG
   30-SEP 03, 2011}},
Organization = {{IEEE; Engn Med \& Biol Soc (EMBS)}},
Abstract = {{Exploiting the full potential of telemedical systems means using
   platform based solutions: data are recovered from biomedical sensors,
   hospital information systems, care-givers, as well as patients
   themselves, and are processed and redistributed in an either centralized
   or, more probably, decentralized way. The integration of all these
   different devices, and interfaces, as well as the automated analysis and
   representation of all the pieces of information are current key
   challenges in telemedicine.
   Mobile phone technology has just begun to offer great opportunities of
   using this diverse information for guiding, warning, and educating
   patients, thus increasing their autonomy and adherence to their
   prescriptions. However, most of these existing mobile solutions are not
   based on platform systems and therefore represent limited, isolated
   applications.
   This article depicts how telemedical systems, based on integrated health
   data platforms, can maximize prescription adherence in chronic patients
   through mobile feedback. The application described here has been
   developed in an EU-funded R\&D project called METABO, dedicated to
   patients with type 1 or type 2 Diabetes Mellitus.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-4122-8}},
ORCID-Numbers = {{Fico, Giuseppe/0000-0003-1551-4613}},
Unique-ID = {{ISI:000298810002317}},
}

@inproceedings{ ISI:000298810002318,
Author = {Guillen, Alejandra and Colas, Javier and Fico, Giuseppe and Guillen,
   Sergio},
Book-Group-Author = {{IEEE}},
Title = {{METABO: A New Paradigm Towards Diabetes Disease Management. An
   Innovative Business Model}},
Booktitle = {{2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2011}},
Pages = {{3554-3557}},
Note = {{33rd Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBS), Boston, MA, AUG
   30-SEP 03, 2011}},
Organization = {{IEEE; Engn Med \& Biol Soc (EMBS)}},
Abstract = {{Dealing with a chronic disease and, more specifically, with Diabetes
   Mellitus and other metabolic disorders, represents a great challenge for
   care givers, patients and the healthcare systems as their treatment
   requires continuous medical care and patient self management. The
   engagement of patients in the adoption of healthy lifestyles with a
   positive impact in the progression of their diseases is fundamental to
   avoid the appearance of chronic complications or co-morbidities. This
   paper presents the externalization of the health management of diabetic
   patients as an alternative to the current models of care for these
   patients that can help improve the quality of follow up and care
   delivery and contribute to the sustainability of the healthcare systems.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-4122-8}},
ORCID-Numbers = {{Fico, Giuseppe/0000-0003-1551-4613}},
Unique-ID = {{ISI:000298810002318}},
}

@inproceedings{ ISI:000298810003316,
Author = {Facchinetti, Andrea and Del Favero, Simone and Sparacino, Giovanni and
   Cobelli, Claudio},
Book-Group-Author = {{IEEE}},
Title = {{Detecting Failures of the Glucose Sensor-Insulin Pump System: Improved
   Overnight Safety Monitoring for Type-1 Diabetes}},
Booktitle = {{2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2011}},
Pages = {{4947-4950}},
Note = {{33rd Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBS), Boston, MA, AUG
   30-SEP 03, 2011}},
Organization = {{IEEE; Engn Med \& Biol Soc (EMBS)}},
Abstract = {{New sensors for real-time continuous glucose monitoring (CGM) and pumps
   for continuous subcutaneous insulin infusion (CSII), possibly mounted on
   the same device, opened new scenarios for Type-1 diabetes treatment.
   However, possible failures of either CGM or CSII can expose diabetic
   patients to risks that can be dangerous especially overnight. In this
   contribution we present a proof-of-concept method, developed in a
   state-space context and implemented through a Kalman estimator, to
   detect in real time possible overnight failures of the sensor-pump
   system by simultaneously using CGM and CSII data. The method is tested
   on two simulated and one real subject. Results show that the method is
   able to correctly generate alerts for sensor-pump failures and
   stimulates further investigation on its development.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4244-4122-8}},
ResearcherID-Numbers = {{Del Favero, Simone/C-3758-2016}},
ORCID-Numbers = {{Del Favero, Simone/0000-0002-8214-2752}},
Unique-ID = {{ISI:000298810003316}},
}

@inproceedings{ ISI:000297284900197,
Author = {Chemlal, Salim and Colberg, Sheri and Satin-Smith, Marta and Gyuricsko,
   Eric and Hubbard, Tom and Scerbo, Mark W. and McKenzie, Frederic D.},
Book-Group-Author = {{IEEE}},
Title = {{Blood Glucose Individualized Prediction for Type 2 Diabetes using iPhone
   Application}},
Booktitle = {{2011 IEEE 37TH ANNUAL NORTHEAST BIOENGINEERING CONFERENCE (NEBEC)}},
Year = {{2011}},
Note = {{IEEE 37th Annual Northeast Bioengineering Conference (NEBEC), Rensselaer
   Polytechn Inst (RPI), Troy, NY, APR 01-03, 2011}},
Organization = {{IEEE}},
Abstract = {{Type 2 diabetes is now the most rapidly growing form of diabetes and has
   become increasingly common among children. This paper presents our work
   of implementing an individualized real time predictive system for blood
   glucose in type 2 diabetes in an iPhone application. The developed
   application, called HealthiManage, provides relevant feedback to
   patients at each glucose input reading comparing the measured and
   predicted readings, facilitating improved self-management of the
   disease. The application incorporates activity recognition via a
   built-in accelerometer on the iPhone, which monitors any physical
   activity and adjusts predictions accordingly. Also, a reward component
   interface was incorporated that is intended to enhance patient
   compliance and encourage mainly teenagers to take control and improve
   their blood glucose regulation.
   The individualized prediction algorithm was tested and verified with
   real patient data. Different physical activities were also examined and
   classified for an accurate activity recognition component. The designed
   application with its predictive model, activity recognition, and other
   elements provide what we believe to be helpful feedback to monitor and
   manage type 2 diabetes and improve patient compliance.}},
ISBN = {{978-1-61284-826-6}},
Unique-ID = {{ISI:000297284900197}},
}

@inproceedings{ ISI:000296892600053,
Author = {Rajeswari, K. and Vaithiyanathan, V. and Gurumoorthy, T.},
Editor = {{Huang, C and Ortiz, J and Sears, S}},
Title = {{A Decision Support System for effective diagnosis of Risk in Type II
   Diabetes-A Case Study}},
Booktitle = {{BEYOND EXPERIENCE IN RISK ANALYSIS AND CRISIS RESPONSE}},
Series = {{Advances in Intelligent Systems Research}},
Year = {{2011}},
Volume = {{16}},
Pages = {{338-341}},
Note = {{3rd International Conference on Risk Analysis and Crisis Response, Texas
   A\&M Int Univ, Loredo, TX, MAY 22-25, 2011}},
Organization = {{Soc Risk Anal, Europe; Soc Risk Anal, Japan; Korean Soc Environm Toxicol}},
Abstract = {{The diagnosis of Diabetes is important issue. Diabetes, if not
   controlled in time, will lead to serious health problems including
   cardiovascular disease, chronic renal failure, retinal damage, nerve
   damage, impotence and gangrene with risk of amputation of toes, feet,
   and even legs. This prompts many researchers to work on
   multidisciplinary intelligent medical decision support system to help
   the Diabeticians. In this paper, a decision support system is designed
   to diagnose the risk level of patients.}},
ISSN = {{1951-6851}},
ISBN = {{978-94-91216-01-5}},
Unique-ID = {{ISI:000296892600053}},
}

@inproceedings{ ISI:000294218900300,
Author = {Zavitsanou, Stamatina and Panoskaltsi, Nicki and Mantalaris, Athanasios
   and Georgiadis, Michael C. and Pistikopoulos, Efstratios N.},
Editor = {{Pistikopoulos, EN and Georgiadis, MC and Kokossis, AC}},
Title = {{Modelling of the Insulin Delivery System for patients with Type 1
   Diabetes Mellitus}},
Booktitle = {{21ST EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING}},
Series = {{Computer-Aided Chemical Engineering}},
Year = {{2011}},
Volume = {{29}},
Pages = {{1500-1504}},
Note = {{21st European Symposium on Computer Aided Process Engineering
   (ESCAPE-21), Chalkidiki, GREECE, MAY 29-JUN 01, 2010-2011}},
Abstract = {{A compartmental mathematical model describing the insulin-glucose system
   of patients with type 1Diabetes is presented in this paper. The model
   consists of several subsystems including insulin infusion and
   absorption, exogenous and endogenous glucose introduction/production,
   glucose distribution in the periphery and provides a realistic
   prediction of the blood glucose concentration of an individual, under
   suggested insulin treatment. The simulation results describe daily
   profiles of glucose concentration in the blood and the influence of
   insulin on the underlying processes during an open loop insulin therapy.
   The validation of this model is the first step of designing a closed
   loop system which will automatically deliver the appropriate amount of
   insulin for the specific patient and will guarantee the prevention of
   hyperglycaemia and mainly hypoglycaemia, a condition of high severity.}},
ISSN = {{1570-7946}},
ISBN = {{978-0-444-53895-6}},
ResearcherID-Numbers = {{Georgiadis, Michael/A-4472-2013}},
ORCID-Numbers = {{Georgiadis, Michael/0000-0002-2016-5131}},
Unique-ID = {{ISI:000294218900300}},
}

@incollection{ ISI:000284291500016,
Author = {Green, Rylie A. and Martens, Penny J. and Nordon, Robert and
   Poole-Warren, Laura A.},
Editor = {{Artmann, GM and Minger, S and Hescheler, J}},
Title = {{Bio-synthetic Encapsulation Systems for Organ Engineering: Focus on
   Diabetes}},
Booktitle = {{STEM CELL ENGINEERING: PRINCIPLES AND APPLICATIONS}},
Year = {{2011}},
Pages = {{363-381}},
Abstract = {{The development of a safe and effective bio-synthetic encapsulation
   system will potentially improve the treatment of diseases known to
   benefit from cell implantation therapies. This is of particular
   importance in the progression towards a permanent treatment for type I
   diabetes. Cell-based therapies for insulin-dependent diabetics
   ultimately aim to eliminate the need for exogenous insulin through the
   implantation of donor islet cells or, alternately, stem cells
   differentiated into glucose-sensitive islet-like cells. Encapsulation
   devices are required to protect implanted cells from destructive host
   immune factors. A successful design will he bio-synthetic, having
   well-defined permeability and providing appropriate biomolecules for
   physiological functionality and survival of the encapsulated cells.}},
DOI = {{10.1007/978-3-642-11865-4\_16}},
ISBN = {{978-3-642-11864-7}},
ORCID-Numbers = {{Poole-Warren, Laura/0000-0002-4414-022X
   Martens, Penny/0000-0003-3098-1420}},
Unique-ID = {{ISI:000284291500016}},
}

@article{ ISI:000289710300020,
Author = {Ivanova, Anna and Petrova, Guenka and Wrona, Witold and Valov, Vasil and
   Stoimenova, Assena and Czech, Marcin},
Title = {{MODELLING THE LONG-TERM EFFECT OF DIABETES THERAPY. AN EXAMPLE WITH
   LIRAGLUTIDE}},
Journal = {{COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES}},
Year = {{2011}},
Volume = {{64}},
Number = {{3}},
Pages = {{449-456}},
Abstract = {{Goal of this study is to model the long-term health care and economic
   consequences of the new antidiabetic medicine (liraglutide) for the
   health care system in Bulgaria using the CORE diabetes model. The CORE
   diabetes model is a disease computer simulation model developed to
   project long-term health and social outcomes of interventions in type 1
   and type 2 diabetes, taking into account diabetes therapies, screening
   and treatment strategies for microvascular and end-stage complications,
   and multifactorial interventions. A total of 66 internal and external
   validation analyses were performed across a range of complications and
   outcomes simulated by the model. Disease progression probabilities and
   treatment pathways were based on published sources, and 7 modifiable
   risk factors of diabetes were used (HbA(1c), SBP, T-Chol, HDL, LDL,
   Triglycerides and BMI). The model was based on a series of 16
   interdependent Markov sub-models that simulate a progression of
   disease-related complications.
   As a result from the modelling the quality adjusted life years (QALYs)
   of diabetes patients increased with liraglutide 1.2 mg by 0.252 (SD
   0.129) years. Total costs increased by BGN 7722 resulting in an
   incremental cost per QALY gained which amounted to BGN 30 674. The
   univaried sensitivity analyses for liraglutide 1.2 mg revealed
   incremental cost-effectiveness ratio for QALY ranging from BGN 23 699 to
   BGN 156 330, when no discount rate for both future costs and outcomes
   was applied and change in HbA(1c) on par with comparator was set,
   respectively. Liraglutide 1.2 mg was shown to be cost effective for the
   treatment of type 2 diabetes if the willingness to pay thresholds was
   around BGN 30 000 per additional QALY gained.}},
ISSN = {{1310-1331}},
Unique-ID = {{ISI:000289710300020}},
}

@inproceedings{ ISI:000297541202023,
Author = {Alanis, Alma Y. and Sanchez, Edgar N. and Ruiz-Velazquez, Eduardo and
   Leon, Blanca S.},
Book-Group-Author = {{IEEE}},
Title = {{Neural Model of Blood Glucose Level for Type 1 Diabetes Mellitus
   Patients}},
Booktitle = {{2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)}},
Year = {{2011}},
Pages = {{2018-2023}},
Note = {{International Joint Conference on Neural Networks (IJCNN), San Jose, CA,
   JUL 31-AUG 05, 2011}},
Organization = {{Int Neural Network Soc (INNS); IEEE Computat Intelligence Soc (CIS);
   Natl Sci Fdn (NSF); Cognimem Technol, Inc; Univ Cincinnati Coll Engn \&
   Appl Sci; Toyota Res Inst N Amer; Univ Cincinnati, Sch Elect \& Compu
   Syst}},
Abstract = {{This paper presents on-line blood glucose level modeling for Type 1
   Diabetes Mellitus (T1DM) patients. The model is developed using a
   recurrent neural network trained with an extended Kalman filter based
   algorithm in order to develop an affine model, which captures the
   nonlinear behavior of the blood glucose metabolism. The goal is to
   derive an on-line dynamical mathematical model of the T1DM for the
   response of a patient to meal and subcutaneous insulin infusion.
   Simulation results are utilized for identification and for testing the
   applicability of the proposed scheme.}},
ISBN = {{978-1-4244-9636-5}},
Unique-ID = {{ISI:000297541202023}},
}

@inproceedings{ ISI:000309148700030,
Author = {Amin-Naseri, Mohammad Reza and Neshat, Najmeh},
Editor = {{Tan, Y and Shi, Y and Chai, Y and Wang, G}},
Title = {{An Expert System Based on Analytical Hierarchy Process for Diabetes Risk
   Assessment (DIABRA)}},
Booktitle = {{ADVANCES IN SWARM INTELLIGENCE, PT II}},
Series = {{Lecture Notes in Computer Science}},
Year = {{2011}},
Volume = {{6729}},
Pages = {{252-259}},
Note = {{2nd International Conferene on Swarm Intelligence (ICSI 2011),
   Chongqing, PEOPLES R CHINA, JUN 12-15, 2011}},
Organization = {{Chongqing Univ; Peking Univ; Chongqing Univ Post \& Telecommunicat; Xian
   Jiaotong Liverpool Univ; IEEE Comp Intelligence Soc; World Federat Soft
   Comp; Int Neural Network Soc; Chinese Assoc Artificial Intelligence; Nat
   Sci Fdn China}},
Abstract = {{DIABRA (DIABetes Risk Assessment) is a knowledge-based expert system
   developed to aid individuals to assess their chance for getting Type 2
   diabetes. The system core is a quantitative model, implemented by
   Analytical Hierarchy Process (AHP) mechanism, to evaluate the developed
   scenarios. The acquired knowledge as scenarios are scored by AHP
   mechanism and represented in the DIABRA. The validation results show the
   expert system gives a highly satisfactory performance when compared to
   human experts. In addition, the computerized system shows additional
   advantages which can be used as helpful tool to reduce the chance of
   getting Type 2 diabetes.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-21523-0}},
Unique-ID = {{ISI:000309148700030}},
}

@article{ ISI:000292341900001,
Author = {Ladyzynski, Piotr and Wojcicki, Jan M. and Foltynski, Piotr},
Title = {{Preventive Systems for the Late Complications of Diabetes}},
Journal = {{BIOCYBERNETICS AND BIOMEDICAL ENGINEERING}},
Year = {{2011}},
Volume = {{31}},
Number = {{2}},
Pages = {{3-21}},
Abstract = {{Aim of this work is to review and characterize methods and systems that
   are used to prevent onset and to slow down the progression of the late
   complications of diabetes. Two groups of methods and systems that might
   be used to prevent or to slow down the progression of the late
   complications of diabetes are characterized in this paper. Each of these
   two groups serves a different purpose. The first group is composed of
   the systems that facilitate a maintenance of strict metabolic control in
   diabetic patients, i.e. the systems which are used for monitoring and
   treatment of diabetes. The second group contains systems that are aimed
   at screening/monitoring or treatment of the risk factors or the early
   signs of the late complications. Obesity increases risk of diabetes and
   its complications. Thus, body mass monitoring and control systems are
   examples of the tools that belong to this group. Other examples include
   the diabetic retinopathy telescreening systems and the systems for
   monitoring of the diabetic foot syndrome.}},
ISSN = {{0208-5216}},
Unique-ID = {{ISI:000292341900001}},
}

@incollection{ ISI:000378911600019,
Author = {Cao, Hua and Lu, Deyin and Khoobehi, Bahram},
Editor = {{Ghista, DN}},
Title = {{Domain-Specific Software Engineering Design for Diabetes Mellitus Study
   Through Gene and Retinopathy Analysis}},
Booktitle = {{BIOMEDICAL SCIENCE, ENGINEERING AND TECHNOLOGY}},
Year = {{2011}},
Pages = {{447-468}},
ISBN = {{978-953-307-471-9}},
Unique-ID = {{ISI:000378911600019}},
}

@inproceedings{ ISI:000306342100012,
Author = {Marling, Cindy and Wiley, Matthew and Cooper, Tessa and Bunescu, Razvan
   and Shubrook, Jay and Schwartz, Frank},
Editor = {{Ram, A and Wiratunga, N}},
Title = {{The 4 Diabetes Support System: A Case Study in CBR Research and
   Development}},
Booktitle = {{CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2011}},
Series = {{Lecture Notes in Artificial Intelligence}},
Year = {{2011}},
Volume = {{6880}},
Pages = {{137-150}},
Note = {{19th International Conference on Case-Based Reasoning, London, ENGLAND,
   SEP 11-14, 2011}},
Abstract = {{This paper presents the 4 Diabetes Support System (TM) (4DSS) project as
   a case study in case-based reasoning (CBR) research and development.
   This project aims to help patients with type 1 diabetes on insulin pump
   therapy achieve and maintain good blood glucose control. Over the course
   of seven years and three clinical research studies, a series of defining
   cases altered the research and development path. Each of these cases
   suggested a new, unanticipated research direction or clinical
   application. New AI technologies, including naive Bayes classification
   and support vector regression, were incorporated. New medical research
   into glycemic variability and blood glucose prediction was undertaken.
   The CBR research paradigm has provided a strong framework for medical
   research as well as for artificial intelligence (AI) research. This new
   work has the potential to positively impact the health and wellbeing of
   patients with diabetes. This paper shares the 4DSS project experience.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-23290-9}},
Unique-ID = {{ISI:000306342100012}},
}

@article{ ISI:000297381300017,
Author = {Petrova, Guenka and Ivanova, Anna and Czech, Marcin and Valov, Vasil and
   Wrona, Witold and Niewada, Maciej and Savova, Alexandra},
Title = {{LIRAGLUTIDE VS. EXENATIDE IN COMBINATION WITH METFORMIN AND/OR
   SULFONYLUREA THERAPY IN TYPE 2 DIABETES MELLITUS THERAPY IN BULGARIA. A
   MODELLING STUDY}},
Journal = {{COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES}},
Year = {{2011}},
Volume = {{64}},
Number = {{10}},
Pages = {{1507-1514}},
Abstract = {{The objective of this study was to conduct an economic evaluation of
   liraglutide combined with metformin (MET) and/or sulfonylurea therapy
   (SU) in comparison with exenatide in combination with MET and/or SU
   therapy in type 2 diabetes not adequately controlled with MET and/or SU.
   The study was conducted with respect to conditions of the Bulgarian
   health care system, from the perspective of the health care services
   payer and included only direct medical costs and benefits.
   Cost-effectiveness economic analysis was applied to calculate the cost
   per quality adjusted life year (cost/QALY) and the cost per life year
   gained (cost/LYG), after modelling the cost of therapy and results from
   the clinical trial.
   In this model, the Bulgarian cost data were used and therapeutic
   practice analysis was performed. Clinical results were based on the LEAD
   6 trial, patients demographics and health state utility values were used
   as default values determined by the CORE diabetes model. Health state
   utility values for disease and treatment-related outcomes were used to
   determine quality-adjusted life years (QALYs).
   In the base case analysis, the total cost of therapy with liraglutide
   was higher than that with exenatide, but the complications cost was
   lower, which could be attributed to better haemoglobin A1c (HbA1c)
   control. Life expectancy and QALYs increased with liraglutide (1.8 mg
   ones per day) by 0.129 (SD 0.175) and 0.151 (SD 0.124) years,
   respectively. Total costs increased by 4151 Bulgarian Levs (BGN)
   resulting in an incremental cost per LYG of BGN 32 082 and incremental
   costs per QALY gained of BGN 27 404.
   The cost-effectiveness acceptability curves show that liraglutide (1.8
   mg) can be considered a cost-effective treatment option compared to
   exenatide, with 71\% probability of being a cost-effective treatment
   approach if the willingness to pay threshold is BGN 50 000 (based on
   QALY).
   Liraglutide (1.8 mg) has been shown to be cost effective when compared
   to exenatide for the treatment of type 2 diabetes if the hypothetical
   willingness to pay threshold is approximately BGN 50 000 per QALY.}},
ISSN = {{1310-1331}},
Unique-ID = {{ISI:000297381300017}},
}

@article{ ISI:000286799500003,
Author = {Tchuitcheu, G. Kouematchoua and Rienhoff, O.},
Title = {{Options for Diabetes Management in Sub-Saharan Africa with an Electronic
   Medical Record System}},
Journal = {{METHODS OF INFORMATION IN MEDICINE}},
Year = {{2011}},
Volume = {{50}},
Number = {{1}},
Pages = {{11-22}},
Abstract = {{Background: An increase of diabetes prevalence of up to 80\% is
   predicted in sub-Saharan Africa (SSA) by 2025 exceeding the worldwide
   55\%. Mortality rates of diabetes and HIV/AIDS are similar. Diabetes
   shares several common factors with HIV/AIDS and multidrug-resistant
   tuberculosis (MDR-TB). The latter two health problems have been
   efficiently managed by an open source electronic medical record system
   (EMRS) in Latin America. Therefore a similar solution for diabetes in
   SSA could be extremely helpful.
   Objectives: The aim was to design and validate a conceptual model for an
   EMRS to improve diabetes management in SSA making use of the HIV and TB
   experience.
   Methods: A review of the literature addressed diabetes care and
   management in SSA as well as existing examples of information and
   communication technology (ICT) use in SSA. Based on a need assessment
   conducted in SSA a conceptual model based on the traditionally
   structured healthcare system in SSA was mapped into a three-layer
   structure. Application modules were derived and a demonstrator
   programmed based on an open source EMRS. Then the approach was validated
   by SSA experts.
   Results: A conceptual model could be specified and validated which
   enhances a problem-oriented approach to diabetes management processes.
   The prototyp EMRS demonstrates options for a patient portal and
   simulation tools for education of health professional and patients in
   SSA.
   Conclusion: It is possible to find IT solutions for diabetes care in SSA
   which follow the same efficiency concepts as HIV or TB modules in Latin
   America. The local efficiency and sustainability of the solution will,
   however, depend on training and changes in work behavior.}},
DOI = {{10.3414/ME09-01-0021}},
ISSN = {{0026-1270}},
Unique-ID = {{ISI:000286799500003}},
}

@inproceedings{ ISI:000306442300042,
Author = {Deja, Rafal},
Editor = {{Yao, JT and Ramanna, S and Wang, GY and Suraj, Z}},
Title = {{Accuracy Evaluation of the System of Type 1 Diabetes Prediction}},
Booktitle = {{ROUGH SETS AND KNOWLEDGE TECHNOLOGY}},
Series = {{Lecture Notes in Artificial Intelligence}},
Year = {{2011}},
Volume = {{6954}},
Pages = {{321-326}},
Note = {{6th International Conference on Rough Sets and Knowledge Technology,
   Banff, CANADA, OCT 08-11, 2011}},
Organization = {{Univ Regina; Univ Calgary; Int Rough Set Soc; Chinese Assoc Artificial
   Intelligence, Rough Sets \& Computat Soc}},
Abstract = {{Based on genetic data we created the decision support system to classify
   and later on to predict the illness among the children with genetic
   susceptibility to DMT1. The system can recommend including a person to
   pre-diabetes therapy. While creating the system the classification
   problems appeared. Some of the algorithms based on the rough set theory
   have been applied to improve the classification accuracy.}},
ISSN = {{0302-9743}},
ISBN = {{978-3-642-24424-7}},
Unique-ID = {{ISI:000306442300042}},
}

@article{ ISI:000285714900001,
Author = {Chase, J. Geoffrey and Suhaimi, Fatanah and Penning, Sophie and Preiser,
   Jean-Charles and Le Compte, Aaron J. and Lin, Jessica and Pretty,
   Christopher G. and Shaw, Geoffrey M. and Moorhead, Katherine T. and
   Desaive, Thomas},
Title = {{Validation of a model-based virtual trials method for tight glycemic
   control in intensive care}},
Journal = {{BIOMEDICAL ENGINEERING ONLINE}},
Year = {{2010}},
Volume = {{9}},
Month = {{DEC 14}},
Abstract = {{Background: In-silico virtual patients and trials offer significant
   advantages in cost, time and safety for designing effective tight
   glycemic control (TGC) protocols. However, no such method has fully
   validated the independence of virtual patients (or resulting clinical
   trial predictions) from the data used to create them. This study uses
   matched cohorts from a TGC clinical trial to validate virtual patients
   and in-silico virtual trial models and methods.
   Methods: Data from a 211 patient subset of the Glucontrol trial in
   Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and
   Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by
   APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25).
   Virtual patients are created by fitting a clinically validated model to
   clinical data, yielding time varying insulin sensitivity profiles
   (SI(t)) that drives in-silico patients.
   Model fit and intra-patient (forward) prediction errors are used to
   validate individual in-silico virtual patients. Self-validation (tests A
   protocol on Group-A virtual patients; and B protocol on B virtual
   patients) and cross-validation (tests A protocol on Group-B virtual
   patients; and B protocol on A virtual patients) are used in comparison
   to clinical data to assess ability to predict clinical trial results.
   Results: Model fit errors were small (<0.25\%) for all patients,
   indicating model fitness. Median forward prediction errors were: 4.3,
   2.8 and 3.5\% for Group-A, Group-B and Overall (A+B), indicating
   individual virtual patients were accurate representations of real
   patients. SI and its variability were similar between cohorts indicating
   they were metabolically similar.
   Self and cross validation results were within 1-10\% of the clinical
   data for both Group-A and Group-B. Self-validation indicated clinically
   insignificant errors due to model and/or clinical compliance.
   Cross-validation clearly showed that virtual patients enabled by
   identified patient-specific SI(t) profiles can accurately predict the
   performance of independent and different TGC protocols.
   Conclusions: This study fully validates these virtual patients and in
   silico virtual trial methods, and clearly shows they can accurately
   simulate, in advance, the clinical results of a TGC protocol, enabling
   rapid in silico protocol design and optimization. These outcomes provide
   the first rigorous validation of a virtual in-silico patient and virtual
   trials methodology.}},
DOI = {{10.1186/1475-925X-9-84}},
Article-Number = {{84}},
ISSN = {{1475-925X}},
ResearcherID-Numbers = {{Chase, James/B-1584-2012
   Pretty, Christopher/B-6300-2014
   mohamad suhaimi, fatanah/F-2103-2014}},
ORCID-Numbers = {{Chase, James/0000-0001-9989-4849
   Pretty, Christopher/0000-0002-2265-6065
   }},
Unique-ID = {{ISI:000285714900001}},
}

@article{ ISI:000284467200017,
Author = {Manjoo, Priya and Joseph, Lawrence and Pilote, Louise and Dasgupta,
   Kaberi},
Title = {{Sex Differences in Step Count-Blood Pressure Association: A Preliminary
   Study in Type 2 Diabetes}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{11}},
Month = {{NOV 22}},
Abstract = {{Background: Walking and cardiovascular mortality are inversely
   associated in type 2 diabetes, but few studies have objectively measured
   associations of walking with individual cardiovascular risk factors.
   Such information would be useful for ``dosing{''} daily steps in
   clinical practice. This study aimed to quantify decrements in blood
   pressure and glycated hemoglobin (A1C) per 1,000 daily step increments.
   Methodology/Principal Findings: Two hundred and one subjects with type 2
   diabetes underwent assessments of step counts (pedometer-measured),
   blood pressure, A1C and anthropometric parameters. Due to missing data,
   the final analysis was conducted on 83 women and 102 men, with a mean
   age of 60 years. Associations of daily steps with blood pressure and A1C
   were evaluated using sex-specific multivariate linear regression models
   (adjusted for age, ethnicity, and BMI). Potential sex differences were
   confirmed in a combined model (women and men) with interaction terms.
   Mean values for daily steps, blood pressure, A1C and BMI were 5,357
   steps/day; 137/80 mm Hg; 7.7\% and 30.4 kg/m(2) respectively. A 1,000
   daily step increment among women was associated with a -2.6 (95\% CI:
   -4.1 to -1.1) mm Hg change in systolic and a -1.4 (95\% CI: -2.2 to
   -0.6) mm Hg change in diastolic blood pressure. Among men, corresponding
   changes were -0.7 (95\% CI: -2.1 to 0.7) and -0.6 (95\% CI: -1.4 to 0.3)
   mm Hg, respectively. Sex differences were confirmed in combined models.
   Step counts and A1C did not demonstrate clinically important
   associations.
   Conclusions/Significance: A 1,000 steps/day increment is associated with
   important blood pressure decrements among women with type 2 diabetes but
   the data were inconclusive among men. Targeted ``dose increments{''} of
   1,000 steps/day in women may lead to measurable blood pressure
   reductions. This information may be of potential use in the titration or
   ``dosing{''} of daily steps. No associations were found between step
   count increments and A1C.}},
DOI = {{10.1371/journal.pone.0014086}},
Article-Number = {{e14086}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000284467200017}},
}

@article{ ISI:000284327800010,
Author = {Vitry, Agnes I. and Roughead, Elizabeth E. and Preiss, Adrian K. and
   Ryan, Philip and Ramsay, Emmae N. and Gilbert, Andrew L. and Caughey,
   Gillian E. and Shakib, Sepehr and Esterman, Adrian and Zhang, Ying and
   McDermott, Robyn A.},
Title = {{Influence of Comorbidities on Therapeutic Progression of Diabetes
   Treatment in Australian Veterans: A Cohort Study}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{11}},
Month = {{NOV 17}},
Abstract = {{Background: This study assessed whether the number of comorbid
   conditions unrelated to diabetes was associated with a delay in
   therapeutic progression of diabetes treatment in Australian veterans.
   Methodology/Principal Findings: A retrospective cohort study was
   undertaken using data from the Australian Department of Veterans'
   Affairs (DVA) claims database between July 2000 and June 2008. The study
   included new users of metformin or sulfonylurea medicines. The outcome
   was the time to addition or switch to another antidiabetic treatment.
   The total number of comorbid conditions unrelated to diabetes was
   identified using the pharmaceutical-based comorbidity index, Rx-Risk-V.
   Competing risk regression analyses were conducted, with adjustments for
   a number of covariates that included age, gender, residential status,
   use of endocrinology service, number of hospitalisation episodes and
   adherence to diabetes medicines. Overall, 20134 veterans were included
   in the study. At one year, 23.5\% of patients with diabetes had a second
   medicine added or had switched to another medicine, with 41.4\%
   progressing by 4 years. The number of unrelated comorbidities was
   significantly associated with the time to addition of an antidiabetic
   medicine or switch to insulin (subhazard ratio {[}SHR] 0.87 {[}95\% CI
   0.84-0.91], P<0.001). Depression, cancer, chronic obstructive pulmonary
   disease, dementia, and Parkinson's disease were individually associated
   with a decreased likelihood of therapeutic progression. Age, residential
   status, number of hospitalisations and adherence to anti-diabetic
   medicines delayed therapeutic progression.
   Conclusions/Significance: Increasing numbers of unrelated conditions
   decreased the likelihood of therapeutic progression in veterans with
   diabetes. These results have implications for the development of quality
   measures, clinical guidelines and the construction of models of care for
   management of diabetes in elderly people with comorbidities.}},
DOI = {{10.1371/journal.pone.0014024}},
Article-Number = {{e14024}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Roughead, Elizabeth/G-4431-2010
   Caughey, Gillian/A-3041-2011
   Preiss, Adrian/A-4691-2011
   McDermott, Robyn/C-5824-2008
   Gilbert, Andrew/C-6892-2009
   Ramsay, Emmae/F-4494-2013
   Esterman, Adrian/D-1042-2009
   Vitry, Agnes/G-2822-2010}},
ORCID-Numbers = {{Roughead, Elizabeth/0000-0002-6811-8991
   Ramsay, Emmae/0000-0002-8708-5910
   Vitry, Agnes/0000-0003-1091-6453}},
Unique-ID = {{ISI:000284327800010}},
}

@article{ ISI:000284087800017,
Author = {Suhre, Karsten and Meisinger, Christa and Doering, Angela and Altmaier,
   Elisabeth and Belcredi, Petra and Gieger, Christian and Chang, David and
   Milburn, Michael V. and Gall, Walter E. and Weinberger, Klaus M. and
   Mewes, Hans-Werner and de Angelis, Martin Hrabe and Wichmann, H. -Erich
   and Kronenberg, Florian and Adamski, Jerzy and Illig, Thomas},
Title = {{Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in
   an Epidemiological Setting}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{11}},
Month = {{NOV 11}},
Abstract = {{Background: Metabolomics is the rapidly evolving field of the
   comprehensive measurement of ideally all endogenous metabolites in a
   biological fluid. However, no single analytic technique covers the
   entire spectrum of the human metabolome. Here we present results from a
   multiplatform study, in which we investigate what kind of results can
   presently be obtained in the field of diabetes research when combining
   metabolomics data collected on a complementary set of analytical
   platforms in the framework of an epidemiological study.
   Methodology/Principal Findings: 40 individuals with self-reported
   diabetes and 60 controls (male, over 54 years) were randomly selected
   from the participants of the population-based KORA (Cooperative Health
   Research in the Region of Augsburg) study, representing an extensively
   phenotyped sample of the general German population. Concentrations of
   over 420 unique small molecules were determined in overnight-fasting
   blood using three different techniques, covering nuclear magnetic
   resonance and tandem mass spectrometry. Known biomarkers of diabetes
   could be replicated by this multiple metabolomic platform approach,
   including sugar metabolites (1,5-anhydroglucoitol), ketone bodies
   (3-hydroxybutyrate), and branched chain amino acids. In some cases,
   diabetes-related medication can be detected (pioglitazone, salicylic
   acid).
   Conclusions/Significance: Our study depicts the promising potential of
   metabolomics in diabetes research by identification of a series of known
   and also novel, deregulated metabolites that associate with diabetes.
   Key observations include perturbations of metabolic pathways linked to
   kidney dysfunction (3-indoxyl sulfate), lipid metabolism
   (glycerophospholipids, free fatty acids), and interaction with the gut
   microflora (bile acids). Our study suggests that metabolic markers hold
   the potential to detect diabetes-related complications already under
   sub-clinical conditions in the general population.}},
DOI = {{10.1371/journal.pone.0013953}},
Article-Number = {{e13953}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Hrabe de Angelis, Martin/F-5531-2012
   Kronenberg, Florian/B-1736-2008
   Meisinger, Christine/B-5358-2014
   }},
ORCID-Numbers = {{Hrabe de Angelis, Martin/0000-0002-7898-2353
   Kronenberg, Florian/0000-0003-2229-1120
   Suhre, Karsten/0000-0001-9638-3912}},
Unique-ID = {{ISI:000284087800017}},
}

@article{ ISI:000283045300009,
Author = {Gkrania-Klotsas, Effrossyni and Ye, Zheng and Cooper, Andrew J. and
   Sharp, Stephen J. and Luben, Robert and Biggs, Mary L. and Chen,
   Liang-Kung and Gokulakrishnan, Kuppan and Hanefeld, Markolf and
   Ingelsson, Erik and Lai, Wen-An and Lin, Shih-Yi and Lind, Lars and
   Lohsoonthorn, Vitool and Mohan, Viswanathan and Muscari, Antonio and
   Nilsson, Goran and Ohrvik, John and Qiang, Jiang Chao and Jenny, Nancy
   Swords and Tamakoshi, Koji and Temelkova-Kurktschiev, Theodora and Wang,
   Ya-Yu and Yajnik, Chittaranjan Sakerlal and Zoli, Marco and Khaw,
   Kay-Tee and Forouhi, Nita G. and Wareham, Nicholas J. and Langenberg,
   Claudia},
Title = {{Differential White Blood Cell Count and Type 2 Diabetes: Systematic
   Review and Meta-Analysis of Cross-Sectional and Prospective Studies}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{10}},
Month = {{OCT 18}},
Abstract = {{Objective: Biological evidence suggests that inflammation might induce
   type 2 diabetes (T2D), and epidemiological studies have shown an
   association between higher white blood cell count (WBC) and T2D.
   However, the association has not been systematically investigated.
   Research Design and Methods: Studies were identified through
   computer-based and manual searches. Previously unreported studies were
   sought through correspondence. 20 studies were identified (8,647 T2D
   cases and 85,040 non-cases). Estimates of the association of WBC with
   T2D were combined using random effects meta-analysis; sources of
   heterogeneity as well as presence of publication bias were explored.
   Results: The combined relative risk (RR) comparing the top to bottom
   tertile of the WBC count was 1.61 (95\% CI: 1.45; 1.79, p =
   1.5{*}10(-18)). Substantial heterogeneity was present (I(2) = 83\%). For
   granulocytes the RR was 1.38 (95\% CI: 1.17; 1.64, p = 1.5{*}10(-4)),
   for lymphocytes 1.26 (95\% CI: 1.02; 1.56, p = 0.029), and for monocytes
   0.93 (95\% CI: 0.68; 1.28, p = 0.67) comparing top to bottom tertile. In
   cross-sectional studies, RR was 1.74 (95\% CI: 1.49; 2.02, p =
   7.7{*}10(-13)), while in cohort studies it was 1.48 (95\% CI: 1.22;
   1.79, p = 7.7{*}10(-5)). We assessed the impact of confounding in
   EPIC-Norfolk study and found that the age and sex adjusted HR of 2.19
   (95\% CI: 1.74; 2.75) was attenuated to 1.82 (95\% CI: 1.45; 2.29) after
   further accounting for smoking, T2D family history, physical activity,
   education, BMI and waist circumference.
   Conclusions: A raised WBC is associated with higher risk of T2D. The
   presence of publication bias and failure to control for all potential
   confounders in all studies means the observed association is likely an
   overestimate.}},
DOI = {{10.1371/journal.pone.0013405}},
Article-Number = {{e13405}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Luben, Robert/H-5519-2015
   }},
ORCID-Numbers = {{Luben, Robert/0000-0002-5088-6343
   Cooper, Andrew/0000-0003-1025-0268}},
Unique-ID = {{ISI:000283045300009}},
}

@article{ ISI:000282568400021,
Author = {Moore, Daniel J. and Zienkiewicz, Jozef and Kendall, Peggy L. and Liu,
   Danya and Liu, Xueyan and Veach, Ruth Ann and Collins, Robert D. and
   Hawiger, Jacek},
Title = {{In Vivo Islet Protection by a Nuclear Import Inhibitor in a Mouse Model
   of Type 1 Diabetes}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{10}},
Month = {{OCT 6}},
Abstract = {{In Background: Insulin-dependent Type 1 diabetes (T1D) is a devastating
   autoimmune disease that destroys beta cells within the pancreatic islets
   and afflicts over 10 million people worldwide. These patients face
   life-long risks for blindness, cardiovascular and renal diseases, and
   complications of insulin treatment. New therapies that protect islets
   from autoimmune destruction and allow continuing insulin production are
   needed. Increasing evidence regarding the pathomechanism of T1D
   indicates that islets are destroyed by the relentless attack by
   autoreactive immune cells evolving from an aberrant action of the
   innate, in addition to adaptive, immune system that produces islet-toxic
   cytokines, chemokines, and other effectors of islet inflammation. We
   tested the hypothesis that targeting nuclear import of stress-responsive
   transcription factors evoked by agonist-stimulated innate and adaptive
   immunity receptors would protect islets from autoimmune destruction.
   Principal Findings: Here we show that a first-in-class inhibitor of
   nuclear import, cSN50 peptide, affords in vivo islet protection
   following a 2-day course of intense treatment in NOD mice, which
   resulted in a diabetes-free state for one year without apparent
   toxicity. This nuclear import inhibitor precipitously reduces the
   accumulation of islet-destructive autoreactive lymphocytes while
   enhancing activation-induced cell death of T and B lymphocytes derived
   from autoimmune diabetes-prone, non-obese diabetic (NOD) mice that
   develop T1D. Moreover, in this widely used model of human T1D we noted
   attenuation of pro-inflammatory cytokine and chemokine production in
   immune cells.
   Conclusions: These results indicate that a novel form of immunotherapy
   that targets nuclear import can arrest inflammation-driven destruction
   of insulin-producing beta cells at the site of autoimmune attack within
   pancreatic islets during the progression of T1D.}},
DOI = {{10.1371/journal.pone.0013235}},
Article-Number = {{e13235}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Moore, Daniel/A-4924-2008}},
ORCID-Numbers = {{Moore, Daniel/0000-0002-6889-9345}},
Unique-ID = {{ISI:000282568400021}},
}

@article{ ISI:000283589600015,
Author = {Abu-Rmileh, Amjad and Garcia-Gabin, Winston},
Title = {{A Gain-Scheduling Model Predictive Controller for Blood Glucose Control
   in Type 1 Diabetes}},
Journal = {{IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}},
Year = {{2010}},
Volume = {{57}},
Number = {{10, 1}},
Pages = {{2478-2484}},
Month = {{OCT}},
Abstract = {{This paper presents a control strategy for blood glucose (BG) level
   regulation in type 1 diabetic patients. To design the controller,
   model-based predictive control scheme has been applied to a newly
   developed diabetic patient model. The controller is provided with a
   feedforward loop to improve meal compensation, a gain-scheduling scheme
   to account for different BG levels, and an asymmetric cost function to
   reduce hypoglycemic risk. A simulation environment that has been
   approved for testing of artificial pancreas control algorithms has been
   used to test the controller. The simulation results show a good
   controller performance in fasting conditions and meal disturbance
   rejection, and robustness against model-patient mismatch and errors in
   meal estimation.}},
DOI = {{10.1109/TBME.2009.2033663}},
ISSN = {{0018-9294}},
Unique-ID = {{ISI:000283589600015}},
}

@article{ ISI:000281711500003,
Author = {Ubeyli, Elif Derya},
Title = {{Automatic diagnosis of diabetes using adaptive neuro-fuzzy inference
   systems}},
Journal = {{EXPERT SYSTEMS}},
Year = {{2010}},
Volume = {{27}},
Number = {{4}},
Pages = {{259-266}},
Month = {{SEP}},
Abstract = {{A new approach based on an adaptive neuro-fuzzy inference system (ANFIS)
   is presented for diagnosis of diabetes diseases. The Pima Indians
   diabetes data set contains records of patients with known diagnosis. The
   ANFIS classifiers learn how to differentiate a new case in the domain by
   being given a training set of such records. The ANFIS classifier is used
   to detect diabetes diseases when eight features defining diabetes
   indications are used as inputs. The proposed ANFIS model combines neural
   network adaptive capabilities and the fuzzy logic qualitative approach.
   The conclusions concerning the impacts of features on the diagnosis of
   diabetes disease are obtained through analysis of the ANFIS. The
   performance of the ANFIS model is evaluated in terms of training
   performances and classification accuracies and the results confirm that
   the proposed ANFIS model has potential in detecting diabetes diseases.}},
DOI = {{10.1111/j.1468-0394.2010.00527.x}},
ISSN = {{0266-4720}},
Unique-ID = {{ISI:000281711500003}},
}

@article{ ISI:000280243300005,
Author = {Bi, Mark and Kao, Wen Hong Linda and Boerwinkle, Eric and Hoogeveen, Ron
   C. and Rasmussen-Torvik, Laura J. and Astor, Brad C. and North, Kari E.
   and Coresh, Josef and Koettgen, Anna},
Title = {{Association of rs780094 in GCKR with Metabolic Traits and Incident
   Diabetes and Cardiovascular Disease: The ARIC Study}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{7}},
Month = {{JUL 22}},
Abstract = {{Objective: The minor T-allele of rs780094 in the glucokinase regulator
   gene (GCKR) associates with a number of metabolic traits including
   higher triglyceride levels and improved glycemic regulation in study
   populations of mostly European ancestry. Using data from the
   Atherosclerosis Risk in Communities (ARIC) Study, we sought to replicate
   these findings, examine them in a large population-based sample of
   African American study participants, and to investigate independent
   associations with other metabolic traits in order to determine if
   variation in GKCR contributes to their observed clustering. In addition,
   we examined the association of rs780094 with incident diabetes, coronary
   heart disease (CHD), and stroke over up mean follow-up times of 8, 15,
   and 15 years, respectively.
   Research Design and Methods: Race-stratified analyses were conducted
   among 10,929 white and 3,960 black participants aged 45-64 at baseline
   assuming an additive genetic model and using linear and logistic
   regression and Cox proportional hazards models.
   Results: Previous findings replicated among white participants in
   multivariable adjusted models: the T-allele of rs780094 was associated
   with lower fasting glucose (p = 10(-7)) and insulin levels (p = 10(-6)),
   lower insulin resistance (HOMA-IR, p = 10(-9)), less prevalent diabetes
   (p = 10(-6)), and higher CRP (p = 10(-8)), 2-h postprandial glucose
   (OGTT, p = 10(-6)), and triglyceride levels (p = 10(-31)). Moreover, the
   T-allele was independently associated with higher HDL cholesterol levels
   (p = 0.022), metabolic syndrome prevalence (p = 0.043), and lower
   beta-cell function measured as HOMA-B (p = 0.011). Among black
   participants, the T-allele was associated only with higher triglyceride
   levels (p = 0.004) and lower insulin levels (p = 0.002) and HOMA-IR (p =
   0.013). Prospectively, the T-allele was associated with reduced
   incidence of diabetes (p = 10(-4)) among white participants, but not
   with incidence of CHD or stroke.
   Conclusions: Our findings indicate rs780094 has independent associations
   with multiple metabolic traits as well as incident diabetes, but not
   incident CHD or stroke. The magnitude of association between the SNP and
   most traits was of lower magnitude among African American compared to
   white participants.}},
DOI = {{10.1371/journal.pone.0011690}},
Article-Number = {{e11690}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Kottgen, Anna/D-2920-2012}},
Unique-ID = {{ISI:000280243300005}},
}

@article{ ISI:000208687300001,
Author = {Aumeunier, Aude and Grela, Francoise and Ramadan, Abdulraouf and Linh
   Pham Van and Bardel, Emilie and Alcala, Alejandro Gomez and Jeannin,
   Pascale and Akira, Shizuo and Bach, Jean-Francois and Thieblemont,
   Nathalie},
Title = {{Systemic Toll-Like Receptor Stimulation Suppresses Experimental Allergic
   Asthma and Autoimmune Diabetes in NOD Mice}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{7}},
Month = {{JUL 7}},
Abstract = {{Background: Infections may be associated with exacerbation of allergic
   and autoimmune diseases. Paradoxically, epidemiological and experimental
   data have shown that some microorganisms can also prevent these
   pathologies. This observation is at the origin of the hygiene hypothesis
   according to which the decline of infections in western countries is at
   the origin of the increased incidence of both Th1-mediated autoimmune
   diseases and Th2-mediated allergic diseases over the last decades. We
   have tested whether Toll-like receptor (TLR) stimulation can
   recapitulate the protective effect of infectious agents on allergy and
   autoimmunity.
   Methods and Findings: Here, we performed a systematic study of the
   disease-modifying effects of a set of natural or synthetic TLR agonists
   using two experimental models, ovalbumin (OVA)-induced asthma and
   spontaneous autoimmune diabetes, presenting the same genetic background
   of the non obese diabetic mouse (NOD) that is highly susceptible to both
   pathologies. In the same models, we also investigated the effect of
   probiotics. Additionally, we examined the effect of the genetic
   invalidation of MyD88 on the development of allergic asthma and
   spontaneous diabetes. We demonstrate that multiple TLR agonists prevent
   from both allergy and autoimmunity when administered parenterally.
   Probiotics which stimulate TLRs also protect from these two diseases.
   The physiological relevance of these findings is further suggested by
   the major acceleration of OVA-induced asthma in MyD88 invalidated mice.
   Our results strongly indicate that the TLR-mediated effects involve
   immunoregulatory cytokines such as interleukin (IL)-10 and transforming
   growth factor (TGF)-beta and different subsets of regulatory T cells,
   notably CD4(+)CD25(+)FoxP3(+) T cells for TLR4 agonists and NKT cells
   for TLR3 agonists.
   Conclusions/Significance: These observations demonstrate that systemic
   administration of TLR ligands can suppress both allergic and autoimmune
   responses. They provide a plausible explanation for the hygiene
   hypothesis. They also open new therapeutic perspectives for the
   prevention of these pathologies.}},
DOI = {{10.1371/journal.pone.0011484}},
Article-Number = {{e11484}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{JEANNIN, Pascale/K-8656-2015}},
Unique-ID = {{ISI:000208687300001}},
}

@article{ ISI:000279135400024,
Author = {Rignell-Hydbom, Anna and Elfving, Maria and Ivarsson, Sten A. and Lindh,
   Christian and Jonsson, Bo A. G. and Olofsson, Per and Rylander, Lars},
Title = {{A Nested Case-Control Study of Intrauterine Exposure to Persistent
   Organochlorine Pollutants in Relation to Risk of Type 1 Diabetes}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{6}},
Month = {{JUN 23}},
Abstract = {{Background: The incidence of type 1 diabetes in Europe is increasing at
   a rate of about 3\% per year and there is also an increasing incidence
   throughout the world. Type 1 diabetes is a complex disease caused by
   multiple genetic and environmental factors. Persistent organochlorine
   pollutants (POPs) have been suggested as a triggering factor for
   developing childhood type 1 diabetes. The aim of this case-control study
   was to assess possible impacts of in utero exposure to POPs on type 1
   diabetes.
   Methodology/Principal Findings: The study was performed as a
   case-control study within a biobank in Malmo, a city located in the
   Southern part of Sweden. The study included 150 cases (children who had
   their diagnosis mostly before 18 years of age) and 150 controls, matched
   for gender and day of birth. 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153)
   and the major DDT metabolite 1,1-dichloro-2,2-bis
   (p-chlorophenyl)-ethylene (p,p'-DDE) were used as a biomarkers for POP
   exposure. When comparing the quartile with the highest maternal serum
   concentrations of PCB-153 with the other quartiles, an odds ratio (OR)
   of 0.73 (95\% confidence interval {[}CI] 0.42, 1.27) was obtained.
   Similar results was obtained for p,p'-DDE (OR 0.56, 95\% CI 0.29, 1.08).
   Conclusions: The hypothesis that in utero exposure to POPs will trigger
   the risk for developing type 1 diabetes was not supported by the
   results. The risk estimates did, although not statistically significant,
   go in the opposite direction. However, it is not reasonable to believe
   that exposure to POPs should protect against type 1 diabetes.}},
DOI = {{10.1371/journal.pone.0011281}},
Article-Number = {{e11281}},
ISSN = {{1932-6203}},
ORCID-Numbers = {{Lindh, Christian/0000-0001-7435-9890
   Jonsson, Bo/0000-0002-8767-0589}},
Unique-ID = {{ISI:000279135400024}},
}

@article{ ISI:000279701100001,
Author = {Ferrari, Giselle L. and Marques, Jefferson L. B. and Gandhi, Rajiv A.
   and Heller, Simon R. and Schneider, Fabio K. and Tesfaye, Solomon and
   Gamba, Humberto R.},
Title = {{Using dynamic pupillometry as a simple screening tool to detect
   autonomic neuropathy in patients with diabetes: a pilot study}},
Journal = {{BIOMEDICAL ENGINEERING ONLINE}},
Year = {{2010}},
Volume = {{9}},
Month = {{JUN 17}},
Abstract = {{Background: Autonomic neuropathy is a common and serious complication of
   diabetes. Early detection is essential to enable appropriate
   interventional therapy and management. Dynamic pupillometry has been
   proposed as a simpler and more sensitive tool to detect subclinical
   autonomic dysfunction. The aim of this study was to investigate pupil
   responsiveness in diabetic subjects with and without cardiovascular
   autonomic neuropathy (CAN) using dynamic pupillometry in two sets of
   experiments.
   Methods: During the first experiment, one flash was administered and the
   pupil response was recorded for 3 s. In the second experiment, 25
   flashes at 1-s interval were administered and the pupil response was
   recorded for 30 s. Several time and pupil-iris radius-related parameters
   were computed from the acquired data. A total of 24 diabetic subjects
   (16 without and 8 with CAN) and 16 healthy volunteers took part in the
   study.
   Results: Our results show that diabetic subjects with and without CAN
   have sympathetic and parasympathetic dysfunction, evidenced by
   diminished amplitude reflexes and significant smaller pupil radius. It
   suggests that pupillary autonomic dysfunction occurs before a more
   generalized involvement of the autonomic nervous system, and this could
   be used to detect early autonomic dysfunction.
   Conclusions: Dynamic pupillometry provides a simple, inexpensive, and
   noninvasive tool to screen high-risk diabetic patients for diabetic
   autonomic neuropathy.}},
DOI = {{10.1186/1475-925X-9-26}},
Article-Number = {{26}},
ISSN = {{1475-925X}},
ResearcherID-Numbers = {{Marques, Jefferson /A-1208-2008
   Saude Publica, Inct/J-9544-2013
   Schneider, Fabio/F-2307-2011
   }},
ORCID-Numbers = {{Marques, Jefferson /0000-0001-8910-4735
   Heller, Simon/0000-0002-2425-9565}},
Unique-ID = {{ISI:000279701100001}},
}

@article{ ISI:000278017300001,
Author = {Bressler, Jan and Kao, W. H. Linda and Pankow, James S. and Boerwinkle,
   Eric},
Title = {{Risk of Type 2 Diabetes and Obesity Is Differentially Associated with
   Variation in FTO in Whites and African-Americans in the ARIC Study}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{5}},
Month = {{MAY 20}},
Abstract = {{Single nucleotide polymorphisms (SNPs) in the fat mass and obesity
   associated (FTO) gene are associated with body mass index (BMI) in
   populations of European descent. The FTO rs9939609 variant, first
   detected in a genome-wide association study of diabetes, conferred an
   increased disease risk that was abolished after adjustment for BMI,
   suggesting that the association may be due to variation in adiposity.
   The relationship between diabetes, four previously identified FTO
   polymorphisms that span a 19.6-kb genomic region, and obesity was
   therefore evaluated in the biracial population-based Atherosclerosis
   Risk in Communities Study with the goal of further refining the
   association by comparing results between the two ethnic groups. The
   prevalence of diabetes and obesity (BMI >= 30 kg/m(2)) was established
   at baseline, and diabetes was determined by either self-report, a
   fasting glucose level >= 126 mg/dL, or non-fasting glucose >= 200 mg/dL.
   There were 1,004 diabetes cases and 10,038 non-cases in whites, and 670
   cases and 2,780 non-cases in African-Americans. Differences in mean BMI
   were assessed by a general linear model, and multivariable logistic
   regression was used to predict the risk of diabetes and obesity. For
   white participants, the FTO rs9939609 A allele was associated with an
   increased risk of diabetes (odds ratio (OR) = 1.19, p < 0.001) and
   obesity (OR = 1.22, p < 0.001) under an additive genetic model that was
   similar for all of the SNPs analyzed. In African-Americans, only the
   rs1421085 C allele was a determinant of obesity risk (OR = 1.17, p =
   0.05), but was found to be protective against diabetes (OR = 0.79, p =
   0.03). Adjustment for BMI did not eliminate any of the observed
   associations with diabetes. Significant statistical interaction between
   race and the FTO variants suggests that the effect on diabetes
   susceptibility may be context dependent.}},
DOI = {{10.1371/journal.pone.0010521}},
Article-Number = {{e10521}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000278017300001}},
}

@article{ ISI:000278017300017,
Author = {Patel, Chirag J. and Bhattacharya, Jayanta and Butte, Atul J.},
Title = {{An Environment-Wide Association Study (EWAS) on Type 2 Diabetes Mellitus}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{5}},
Month = {{MAY 20}},
Abstract = {{Background: Type 2 Diabetes (T2D) and other chronic diseases are caused
   by a complex combination of many genetic and environmental factors. Few
   methods are available to comprehensively associate specific physical
   environmental factors with disease. We conducted a pilot
   Environmental-Wide Association Study (EWAS), in which epidemiological
   data are comprehensively and systematically interpreted in a manner
   analogous to a Genome Wide Association Study (GWAS).
   Methods and Findings: We performed multiple cross-sectional analyses
   associating 266 unique environmental factors with clinical status for
   T2D defined by fasting blood sugar (FBG) concentration >= 126 mg/dL. We
   utilized available Centers for Disease Control (CDC) National Health and
   Nutrition Examination Survey (NHANES) cohorts from years 1999 to 2006.
   Within cohort sample numbers ranged from 503 to 3,318. Logistic
   regression models were adjusted for age, sex, body mass index (BMI),
   ethnicity, and an estimate of socioeconomic status (SES). As in GWAS,
   multiple comparisons were controlled and significant findings were
   validated with other cohorts. We discovered significant associations for
   the pesticide-derivative heptachlor epoxide (adjusted OR in three
   combined cohorts of 1.7 for a 1 SD change in exposure amount; p<0.001),
   and the vitamin c-tocopherol (adjusted OR 1.5; p<0.001). Higher
   concentrations of polychlorinated biphenyls (PCBs) such as PCB170
   (adjusted OR 2.2; p<0.001) were also found. Protective factors
   associated with T2D included b-carotenes (adjusted OR 0.6; p<0.001).
   Conclusions and Significance: Despite difficulty in ascertaining
   causality, the potential for novel factors of large effect associated
   with T2D justify the use of EWAS to create hypotheses regarding the
   broad contribution of the environment to disease. Even in this study
   based on prior collected epidemiological measures, environmental factors
   can be found with effect sizes comparable to the best loci yet found by
   GWAS.}},
DOI = {{10.1371/journal.pone.0010746}},
Article-Number = {{e10746}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000278017300017}},
}

@article{ ISI:000276728000003,
Author = {Choi, Samjin and Lee, Gi-Ja and Chae, Su-Jin and Kang, Sung Wook and
   Yin, Chang-Shik and Lee, Seung-Hoon and Choi, Seok Keun and Park,
   Hun-Kuk},
Title = {{Potential neuroprotective effects of acupuncture stimulation on diabetes
   mellitus in a global ischemic rat model}},
Journal = {{PHYSIOLOGICAL MEASUREMENT}},
Year = {{2010}},
Volume = {{31}},
Number = {{5}},
Pages = {{633-647}},
Month = {{MAY}},
Abstract = {{Acupuncture (ACU) is known to be effective in ischemia treatment, and
   glutamate (GLU) excitotoxicity is an important factor in neuronal cell
   death. We observed the effect of ACU on cerebral blood flow (\%CBF) and
   Delta GLU (the changes in GLU release) in the ischemic stroke rat model
   of diabetic mellitus (DM). A global ischemia was induced using the
   eleven-vessel occlusion (11-VO) method in 14 Sprague-Dawley rats (DM),
   which were randomly divided into two groups: the control group and the
   ACU-treatment group. Extracellular Delta GLU was assessed using an
   intra-cerebral biosensor system measuring 256 samples per second,
   simultaneously with \%CBF and electroencephalogram. ACU stimulation was
   applied to ACU points GB34 and GB39 during the ischemic period.
   Twenty-three diagnostic parameters were proposed first for a detailed
   analysis of changes in \%CBF and GLU release during
   ischemia/reperfusion. ACU rats showed a significant decrease in ischemic
   (p < 0.05) and reperfusion \%CBF (p < 0.0001) than control rats, and a
   significantly larger decrease in ischemic Delta GLU (p < 0.05) and peak
   level of reperfusion Delta GLU (p < 0.005) than control rats. From these
   results, we suggest that ACU stimulation is responsible for the
   potential protection of neurons through suppression of \%CBF response in
   the increased plasma osmolality and extracellular Delta GLU in diabetic
   rats under ischemic conditions.}},
DOI = {{10.1088/0967-3334/31/5/003}},
ISSN = {{0967-3334}},
ResearcherID-Numbers = {{Lee, Seunghoon/A-6892-2012}},
Unique-ID = {{ISI:000276728000003}},
}

@article{ ISI:000276642100085,
Author = {Takeda, Shuko and Sato, Naoyuki and Uchio-Yamada, Kozue and Sawada,
   Kyoko and Kunieda, Takanori and Takeuchi, Daisuke and Kurinami, Hitomi
   and Shinohara, Mitsuru and Rakugi, Hiromi and Morishita, Ryuichi},
Title = {{Diabetes-accelerated memory dysfunction via cerebrovascular inflammation
   and A beta deposition in an Alzheimer mouse model with diabetes}},
Journal = {{PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA}},
Year = {{2010}},
Volume = {{107}},
Number = {{15}},
Pages = {{7036-7041}},
Month = {{APR 13}},
Abstract = {{Recent epidemiological studies suggest that diabetes mellitus is a
   strong risk factor for Alzheimer disease. However, the underlying
   mechanisms remain largely unknown. In this study, to investigate the
   pathophysiological interaction between these diseases, we generated
   animal models that reflect the pathologic conditions of both diseases.
   We crossed Alzheimer transgenic mice (APP23) with two types of diabetic
   mice (ob/ob and NSY mice), and analyzed their metabolic and brain
   pathology. The onset of diabetes exacerbated Alzheimer-like cognitive
   dysfunction without an increase in brain amyloid-beta burden in
   double-mutant (APP(+)-ob/ob) mice. Notably, APP(+)-ob/ob mice showed
   cerebrovascular inflammation and severe amyloid angiopathy. Conversely,
   the cross-bred mice showed an accelerated diabetic phenotype compared
   with ob/ob mice, suggesting that Alzheimer amyloid pathology could
   aggravate diabetes. Similarly, APP(+)-NSY fusion mice showed more severe
   glucose intolerance compared with diabetic NSY mice. Furthermore,
   high-fat diet feeding induced severe memory deficits in APP(+)-NSY mice
   without an increase in brain amyloid-beta load. Here, we created
   Alzheimer mouse models with early onset of cognitive dysfunction.
   Cerebrovascular changes and alteration in brain insulin signaling might
   play a pivotal role in this relationship. These findings could provide
   insights into this intensely debated association.}},
DOI = {{10.1073/pnas.1000645107}},
ISSN = {{0027-8424}},
Unique-ID = {{ISI:000276642100085}},
}

@article{ ISI:000277938800002,
Author = {Abu-Rmileh, Amjad and Garcia-Gabin, Winston and Zambrano, Darine},
Title = {{Internal model sliding mode control approach for glucose regulation in
   type 1 diabetes}},
Journal = {{BIOMEDICAL SIGNAL PROCESSING AND CONTROL}},
Year = {{2010}},
Volume = {{5}},
Number = {{2}},
Pages = {{94-102}},
Month = {{APR}},
Abstract = {{Patients with type 1 diabetes require insulin therapy to maintain blood
   glucose levels within safe ranges since their pancreas is unable to
   complete its function. The development of a closed-loop artificial
   pancreas capable of maintaining normoglycemia during daily life will
   dramatically improve the quality of life for insulin-dependent diabetic
   patients In this work, a closed-loop control strategy for blood glucose
   level regulation in type 1 diabetic patients is presented A robust
   controller is designed using a combination of internal model and sliding
   mode control techniques Also, the controller is provided with a
   feedforward loop to improve meal compensation. A simulation environment
   designed for testing the artificial pancreas control algorithms has been
   used to evaluate the controller. The simulation results show a good
   controller performance in fasting conditions and meal disturbance
   rejection, and robustness against model-patient mismatch and meal
   estimation errors (C) 2009 Elsevier Ltd. All rights reserved}},
DOI = {{10.1016/j.bspc.2009.12.003}},
ISSN = {{1746-8094}},
Unique-ID = {{ISI:000277938800002}},
}

@article{ ISI:000275122000017,
Author = {Maltarollo, Vinicius G. and Homem-de-Mello, Paula and Honorio, Kathia M.},
Title = {{Theoretical study on the molecular and electronic properties of some
   substances used for diabetes mellitus treatment}},
Journal = {{JOURNAL OF MOLECULAR MODELING}},
Year = {{2010}},
Volume = {{16}},
Number = {{4}},
Pages = {{799-804}},
Month = {{APR}},
Abstract = {{Diabetes mellitus (DM) is a disease that affects a large number of
   people, and the number of problems associated with the disease has been
   increasing in the past few decades. These problems include
   cardiovascular disorders, blindness and the eventual need to amputate
   limbs. Therefore, the quality of life for people living with DM is less
   than it is for healthy people. In several cases, metabolic syndrome
   (MS), which can be considered a disturbance of the lipid metabolism, is
   associated with DM. In this work, two drugs used to treat DM,
   pioglitazone and rosiglitazone, were studied using theoretical methods,
   and their molecular properties were related to the biological activity
   of these drugs. From the results, it was possible to correlate the
   properties of each substance-particularly electronic properties-with the
   biological interactions that are linked to their pharmacological
   effects. These results suggest that there are future prospects for
   designing or developing new drugs based on the correlation between
   theoretical and experimental properties.}},
DOI = {{10.1007/s00894-009-0627-6}},
ISSN = {{1610-2940}},
EISSN = {{0948-5023}},
ResearcherID-Numbers = {{Homem-de-Mello, Paula/B-6618-2011
   Honorio, Kathia/D-4998-2012}},
ORCID-Numbers = {{Homem-de-Mello, Paula/0000-0002-7049-4689
   Honorio, Kathia/0000-0002-6938-0676}},
Unique-ID = {{ISI:000275122000017}},
}

@article{ ISI:000275328300008,
Author = {Widenmaier, Scott B. and Kim, Su-Jin and Yang, Gary K. and De Los Reyes,
   Thomas and Nian, Cuilan and Asadi, Ali and Seino, Yutaka and Kieffer,
   Timothy J. and Kwok, Yin Nam and McIntosh, Christopher H. S.},
Title = {{A GIP Receptor Agonist Exhibits beta-Cell Anti-Apoptotic Actions in Rat
   Models of Diabetes Resulting in Improved beta-Cell Function and Glycemic
   Control}},
Journal = {{PLOS ONE}},
Year = {{2010}},
Volume = {{5}},
Number = {{3}},
Month = {{MAR 9}},
Abstract = {{Aims: The gastrointestinal hormone GIP promotes pancreatic islet
   function and exerts pro-survival actions on cultured beta-cells.
   However, GIP also promotes lipogenesis, thus potentially restricting its
   therapeutic use. The current studies evaluated the effects of a
   truncated GIP analog, D-Ala(2)-GIP(1-30) (D-GIP(1-30)), on glucose
   homeostasis and beta-cell mass in rat models of diabetes.
   Materials and Methods: The insulinotropic and pro-survival potency of
   D-GIP(1-30) was evaluated in perfused pancreas preparations and cultured
   INS-1 beta-cells, respectively, and receptor selectivity evaluated using
   wild type and GIP receptor knockout mice. Effects of D-GIP(1-30) on
   beta-cell function and glucose homeostasis, in vivo, were determined
   using Lean Zucker rats, obese Vancouver diabetic fatty rats,
   streptozotocin treated rats, and obese Zucker diabetic fatty rats, with
   effects on beta-cell mass determined in histological studies of
   pancreatic tissue. Lipogenic effects of D-GIP(1-30) were evaluated on
   cultured 3T3-L1 adipocytes.
   Results: Acutely, D-GIP(1-30) improved glucose tolerance and insulin
   secretion. Chronic treatment with D-GIP(1-30) reduced levels of islet
   pro-apoptotic proteins in Vancouver diabetic fatty rats and preserved
   beta-cell mass in streptozotocin treated rats and Zucker diabetic fatty
   rats, resulting in improved insulin responses and glycemic control in
   each animal model, with no change in body weight. In in vitro studies,
   D-GIP(1-30) exhibited equivalent potency to GIP(1-42) on beta-cell
   function and survival, but greatly reduced action on lipoprotein lipase
   activity in 3T3-L1 adipocytes.
   Conclusions: These findings demonstrate that truncated forms of GIP
   exhibit potent anti-diabetic actions, without pro-obesity effects, and
   that the C-terminus contributes to the lipogenic actions of GIP.}},
DOI = {{10.1371/journal.pone.0009590}},
Article-Number = {{e9590}},
ISSN = {{1932-6203}},
ORCID-Numbers = {{Yang, Gary/0000-0002-3039-4392}},
Unique-ID = {{ISI:000275328300008}},
}

@article{ ISI:000275897600002,
Author = {Pielmeier, Ulrike and Andreassen, Steen and Nielsen, Birgitte S. and
   Chase, J. Geoffrey and Haure, Pernille},
Title = {{A simulation model of insulin saturation and glucose balance for
   glycemic control in ICU patients}},
Journal = {{COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}},
Year = {{2010}},
Volume = {{97}},
Number = {{3}},
Pages = {{211-222}},
Month = {{MAR}},
Abstract = {{Consistent tight blood sugar control in critically ill patients has
   proven elusive. Properly accounting for the saturation of insulin action
   and reducing the need for frequent measurements are important aspects in
   intensive insulin therapy. This paper presents a composite metabolic
   model, `Glucosafe', that integrates models and parameters from normal
   physiology and accounts for the reduced rate of glucose gut absorption
   and saturation of insulin action in patients with reduced insulin
   sensitivity. Particularly, two different sites of reduced insulin
   sensitivity, before and after the non-linearity of insulin action, are
   explored with this model. These approaches are assessed based on the
   model's accuracy in retrospectively predicting blood glucose
   measurements of 10 randomly chosen, hyperglycemic intensive care
   patients. For each patient, median absolute percent error is <25\% for
   prediction times <= 270

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib