text:
@article{ ISI:000305347400057,
Author = {Ayele, Ketema and Tesfa, Bisrat and Abebe, Lakew and Tilahun, Tizta and
   Girma, Eshetu},
Title = {{Self Care Behavior among Patients with Diabetes in Harari, Eastern
   Ethiopia: The Health Belief Model Perspective}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{4}},
Month = {{APR 17}},
Abstract = {{Background: Diabetes mellitus is a chronic disease that requires
   lifelong medical treatments and a life style adjustment. To prevent
   serious morbidity and mortality, it requires dedication to demanding
   self-care behaviors in multiple domains. The objective of this study was
   to identify predictors of self care behaviors among patients with
   diabetes.
   Methods: From a total of 425 follow up diabetic patients, a quantitative
   cross sectional study was conducted among 222 of them from three
   different hospitals in Harar town, from March to April, 2011. The sample
   was taken using simple random sampling method. Data was collected using
   pretested questionnaire. Descriptive statistics multiple logistic
   regression analysis were also used to assess the predicators of self
   care behaviors among patients with diabetes.
   Result: Majority of the study respondents 134 (60.4\%) were female and
   the mean age was 49.7 (SD +/- 14.7) years. More than half 147(66.2\%) of
   them were medically diagnosed with type-2 diabetes. 208(93.7\%) had
   general knowledge about diabetes and specific knowledge about diabetes
   self care 207(93.2\%). Large proportion of them had moderate perceived
   susceptibility 174(78.4\%) and severity 112(50.5\%). More than half of
   the respondents 149(67.1\%) had less perceived barrier while only 30
   (13.5\%) of them had high self efficacy to self care practices related
   to diabetes mellitus. Only 87(39.2\%) followed the recommended self care
   practices on diabetes.
   Conclusions: Patients with less frequent information were less likely to
   take diabetes self care. Patients who were more educated, middle income,
   had high perceived severity of diabetes and less perceived barrier to
   self care were more likely to take diabetes self care. To increase the
   self care behavior, diabetes messages should focus on severity of
   diabetes and how to overcome barriers for self care by segmenting the
   audiences based on income and educational status with increasing the
   frequency and reach of message on diabetes.}},
DOI = {{10.1371/journal.pone.0035515}},
Article-Number = {{e35515}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000305347400057}},
}

@article{ ISI:000305297500073,
Author = {Qian, Yun and Lu, Feng and Dong, Meihua and Lin, Yudi and Li, Huizhang
   and Chen, Jian and Shen, Chong and Jin, Guangfu and Hu, Zhibin and Shen,
   Hongbing},
Title = {{Genetic Variants of IDE-KIF11-HHEX at 10q23.33 Associated with Type 2
   Diabetes Risk: A Fine-Mapping Study in Chinese Population}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{4}},
Month = {{APR 10}},
Abstract = {{Background: Genome-wide association studies (GWAS) in populations of
   European ancestry have mapped a type 2 diabetes susceptibility region to
   chromosome 10q23.33 containing IDE, KIF11 and HHEX genes
   (IDE-KIF11-HHEX), which has also been replicated in Chinese populations.
   However, the functional relevance for genetic variants at this locus is
   still unclear. It is critical to systematically assess the relationship
   of genetic variants in this region with the risk of type 2 diabetes.
   Methodology/Principal Findings: A fine-mapping study was conducted by
   genotyping fourteen tagging single-nucleotide polymorphisms (SNPs) in a
   290-kb linkage disequilibrium (LD) region using a two-stage case-control
   study of type 2 diabetes in a Chinese Han population. Suggestive
   associations (P<0.05) observed from 1,200 cases and 1,200 controls in
   the first stage were further replicated in 1,725 cases and 2,081
   controls in the second stage. Seven tagging SNPs were consistently
   associated with type 2 diabetes in both stages (P<0.05), with combined
   odds ratios (ORs) ranging from 1.14 to 1.33 in the combined analysis.
   The most significant locus was rs7923837 {[}OR = 1.33, 95\% confidence
   interval (CI): 1.21-1.47] at the 3'-flanking region of HHEX gene. SNP
   rs1111875 was found to be another partially independent locus (OR =
   1.23, 95\% CI: 1.13-1.35) in this region that was associated with type 2
   diabetes risk. A cumulative effect of rs7923837 and rs1111875 was
   observed with individuals carrying 1, 2, and 3 or 4 risk alleles having
   a 1.27, 1.44, and 1.73-fold increased risk, respectively, for type 2
   diabetes (P for trend = 4.1E-10).
   Conclusions/Significance: Our results confirm that genetic variants of
   the IDE-KIF11-HHEX region at 10q23.33 contribute to type 2 diabetes
   susceptibility and suggest that rs7923837 may represent the strongest
   signal related to type 2 diabetes risk in the Chinese Han population.}},
DOI = {{10.1371/journal.pone.0035060}},
Article-Number = {{e35060}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000305297500073}},
}

@article{ ISI:000305012700068,
Author = {Hovatta, Iiris and de Mello, Vanessa D. F. and Kananen, Laura and
   Lindstrom, Jaana and Eriksson, Johan G. and Ilanne-Parikka, Pirjo and
   Keinanen-Kiukaanniemi, Sirkka and Peltonen, Markku and Tuomilehto,
   Jaakko and Uusitupa, Matti},
Title = {{Leukocyte Telomere Length in the Finnish Diabetes Prevention Study}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{4}},
Month = {{APR 6}},
Abstract = {{Leukocyte telomere length (TL) is considered a biomarker for biological
   aging. Shortened TL has been observed in many complex diseases,
   including type 2 diabetes (T2DM). Lifestyle intervention studies, e. g.
   the Diabetes Prevention Study (DPS), have shown a decrease in the
   incidence of T2DM by promoting healthy lifestyles in individuals with
   impaired glucose tolerance (IGT). Our aim was to study in the DPS the
   influence of the lifestyle intervention on TL. TL was measured by
   quantitative PCR-based method at two time points (N = 334 and 343) on
   average 4.5 years apart during the active intervention and
   post-intervention follow-up. TL inversely correlated with age. Our main
   finding was that TL increased in about two thirds of the individuals
   both in the intervention and in the control groups during follow-up; TL
   increased most in individuals with the shortest TL at the first
   measurement. TL was not associated with development of T2DM, nor did
   lifestyle intervention have an effect on TL. No association between
   insulin secretion or insulin resistance indices and TL was observed. We
   did not detect an association between TL and development of T2DM in the
   DPS participants. It could be due to all participants being overweight
   and having IGT at baseline, both of which have been found to be
   independently associated with shorter leukocyte TL in some earlier
   studies. TL had no substantial role in worsening of glucose tolerance in
   people with IGT. Our study confirms that leukocyte TL can increase with
   time even in obese people with impaired glucose metabolism.}},
DOI = {{10.1371/journal.pone.0034948}},
Article-Number = {{e34948}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Hovatta, Iiris/D-6871-2012
   Peltonen, Markku/F-3037-2015
   }},
ORCID-Numbers = {{Hovatta, Iiris/0000-0002-5990-7892
   Peltonen, Markku/0000-0002-3767-4223
   de Mello, Vanessa/0000-0003-3414-6983}},
Unique-ID = {{ISI:000305012700068}},
}

@article{ ISI:000304780500011,
Author = {Kelly, M. Ann and Rees, Simon D. and Hydrie, M. Zafar I. and Shera, A.
   Samad and Bellary, Srikanth and O'Hare, J. Paul and Kumar, Sudhesh and
   Taheri, Shahrad and Basit, Abdul and Barnett, Anthony H. and DIAGRAM
   Consortium and SAT2D Consortium},
Title = {{Circadian Gene Variants and Susceptibility to Type 2 Diabetes: A Pilot
   Study}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{4}},
Month = {{APR 2}},
Abstract = {{Background: Disruption of endogenous circadian rhythms has been shown to
   increase the risk of developing type 2 diabetes, suggesting that
   circadian genes might play a role in determining disease susceptibility.
   We present the results of a pilot study investigating the association
   between type 2 diabetes and selected single nucleotide polymorphisms
   (SNPs) in/near nine circadian genes. The variants were chosen based on
   their previously reported association with prostate cancer, a disease
   that has been suggested to have a genetic link with type 2 diabetes
   through a number of shared inherited risk determinants.
   Methodology/Principal Findings:The pilot study was performed using two
   genetically homogeneous Punjabi cohorts, one resident in the United
   Kingdom and one indigenous to Pakistan. Subjects with (N = 1732) and
   without (N = 1780) type 2 diabetes were genotyped for thirteen circadian
   variants using a competitive allele-specific polymerase chain reaction
   method. Associations between the SNPs and type 2 diabetes were
   investigated using logistic regression. The results were also combined
   with in silico data from other South Asian datasets (SAT2D consortium)
   and white European cohorts (DIAGRAM+) using meta-analysis. The
   rs7602358G allele near PER2 was negatively associated with type 2
   diabetes in our Punjabi cohorts (combined odds ratio {[}OR]= 0.75
   {[}0.66-0.86], p = 3.18 x 10(-5)), while the BMAL1 rs11022775T allele
   was associated with an increased risk of the disease (combined OR = 1.22
   {[}1.07-1.39], p = 0.003). Neither of these associations was replicated
   in the SAT2D or DIAGRAM+ datasets, however. Meta-analysis of all the
   cohorts identified disease associations with two variants, rs2292912 in
   CRY2 and rs12315175 near CRY1, although statistical significance was
   nominal (combined OR 1.05 {[}1.01-1.08], p = 0.008 and OR = 0.95
   {[}0.91-0.99], p = 0.015 respectively).
   Conclusions/significance: None of the selected circadian gene variants
   was associated with type 2 diabetes with study-wide significance after
   meta-analysis. The nominal association observed with the CRY2 SNP,
   however, complements previous findings and confirms a role for this
   locus in disease susceptibility.}},
DOI = {{10.1371/journal.pone.0032670}},
Article-Number = {{e32670}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Taheri, Shahrad/B-4194-2013
   Rudan, Igor/I-1467-2012
   Elliott, Amanda/G-5120-2012
   Boehm, Bernhard/F-8750-2015
   Meitinger, Thomas/O-1318-2015
   kumar, sudhesh/D-6945-2013
   Hide, Winston/C-7217-2009}},
ORCID-Numbers = {{Taheri, Shahrad/0000-0001-8314-1500
   Rudan, Igor/0000-0001-6993-6884
   kumar, sudhesh/0000-0003-4326-5941
   Zeggini, Eleftheria/0000-0003-4238-659X
   Hide, Winston/0000-0002-8621-3271}},
Unique-ID = {{ISI:000304780500011}},
}

@article{ ISI:000303062800030,
Author = {Trombetta, Maddalena and Bonetti, Sara and Boselli, MariaLinda and
   Turrini, Fabiola and Malerba, Giovanni and Trabetti, Elisabetta and
   Pignatti, PierFranco and Bonora, Enzo and Bonadonna, Riccardo C.},
Title = {{CACNA1E Variants Affect Beta Cell Function in Patients with Newly
   Diagnosed Type 2 Diabetes. The Verona Newly Diagnosed Type 2 Diabetes
   Study (VNDS) 3}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{3}},
Month = {{MAR 9}},
Abstract = {{Background: Genetic variability of the major subunit (CACNA1E) of the
   voltage-dependent Ca2+ channel Ca(V)2.3 is associated to risk of type 2
   diabetes, insulin resistance and impaired insulin secretion in
   nondiabetic subjects. The aim of the study was to test whether CACNA1E
   common variability affects beta cell function and/or insulin sensitivity
   in patients with newly diagnosed type 2 diabetes.
   Methodology/Principal Findings: In 595 GAD-negative, drug naive patients
   (mean +/- SD; age: 58.5 +/- 10.2 yrs; BMI: 29.9 +/- 5 kg/m(2), HbA1c:
   7.0 +/- 1.3) with newly diagnosed type 2 diabetes we: 1. genotyped 10
   tag SNPs in CACNA1E region reportedly covering similar to 93\% of
   CACNA1E common variability: rs558994, rs679931, rs2184945, rs10797728,
   rs3905011, rs12071300, rs175338, rs3753737, rs2253388 and rs4652679; 2.
   assessed clinical phenotypes, insulin sensitivity by the euglycemic
   insulin clamp and beta cell function by state-of-art modelling of
   glucose/C-peptide curves during OGTT. Five CACNA1E tag SNPs (rs10797728,
   rs175338, rs2184945, rs3905011 and rs4652679) were associated with
   specific aspects of beta cell function (p<0.05-0.01). Both major alleles
   of rs2184945 and rs3905011 were each (p<0.01 and p<0.005, respectively)
   associated to reduced proportional control with a demonstrable additive
   effect (p<0.005). In contrast, only the major allele of rs2253388 was
   related weakly to more severe insulin resistance (p<0.05).
   Conclusions/Significance: In patients with newly diagnosed type 2
   diabetes CACNA1E common variability is strongly associated to beta cell
   function. Genotyping CACNA1E might be of help to infer the beta cell
   functional phenotype and to select a personalized treatment.}},
DOI = {{10.1371/journal.pone.0032755}},
Article-Number = {{e32755}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{pignatti, pier /B-4736-2012
   }},
ORCID-Numbers = {{Malerba, Giovanni/0000-0001-8705-8560}},
Unique-ID = {{ISI:000303062800030}},
}

@article{ ISI:000303017700051,
Author = {Tiv, Michel and Viel, Jean-Francois and Mauny, Frederic and Eschwege,
   Eveline and Weill, Alain and Fournier, Cecile and Fagot-Campagna, Anne
   and Penfornis, Alfred},
Title = {{Medication Adherence in Type 2 Diabetes: The ENTRED Study 2007, a French
   Population-Based Study}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{3}},
Month = {{MAR 5}},
Abstract = {{Background: Adherence to prescribed medications is a key dimension of
   healthcare quality. The aim of this large population-based study was to
   evaluate self-reported medication adherence and to identify factors
   linked with poor adherence in patients with type 2 diabetes in France.
   Methodology: The ENTRED study 2007, a French national survey of people
   treated for diabetes, was based on a representative sample of patients
   who claimed reimbursement for oral hypoglycaemic agents and/or insulin
   at least three times between August 2006 and July 2007, and who were
   randomly selected from the database of the two main National Health
   Insurance Systems. Medication adherence was determined using a six-item
   self-administered questionnaire. A multinomial polychotomous logistic
   regression model was used to identify factors associated with medication
   adherence in the 3,637 persons with type 2 diabetes.
   Principal Findings: Thirty nine percent of patients reported good
   medication adherence, 49\% medium adherence and 12\% poor adherence. The
   factors significantly associated with poor adherence in multivariate
   analysis were socio-demographic factors: age <45 years, non-European
   geographical origin, financial difficulties and being professionally
   active; disease and therapy-related factors: HbA(1c) > 8\% and existing
   diabetes complications; and health care-related factors: difficulties
   for taking medication alone, decision making by the patient only, poor
   acceptability of medical recommendations, lack of family or social
   support, need for information on treatment, reporting no confidence in
   the future, need for medical support and follow-up by a specialist
   physician.
   Conclusions: In a country with a high level of access to healthcare, our
   study demonstrated a substantial low level of medication adherence in
   type 2 diabetic patients. Better identification of those with poor
   adherence and individualised suitable recommendations remain essential
   for better healthcare management.}},
DOI = {{10.1371/journal.pone.0032412}},
Article-Number = {{e32412}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Viel, Jean-Francois/K-2224-2015}},
ORCID-Numbers = {{Viel, Jean-Francois/0000-0002-6716-7468}},
Unique-ID = {{ISI:000303017700051}},
}

@article{ ISI:000300768100014,
Author = {Kho, Abel N. and Hayes, M. Geoffrey and Rasmussen-Torvik, Laura and
   Pacheco, Jennifer A. and Thompson, William K. and Armstrong, Loren L.
   and Denny, Joshua C. and Peissig, Peggy L. and Miller, Aaron W. and Wei,
   Wei-Qi and Bielinski, Suzette J. and Chute, Christopher G. and Leibson,
   Cynthia L. and Jarvik, Gail P. and Crosslin, David R. and Carlson,
   Christopher S. and Newton, Katherine M. and Wolf, Wendy A. and Chisholm,
   Rex L. and Lowe, William L.},
Title = {{Use of diverse electronic medical record systems to identify genetic
   risk for type 2 diabetes within a genome-wide association study}},
Journal = {{JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION}},
Year = {{2012}},
Volume = {{19}},
Number = {{2}},
Pages = {{212-218}},
Month = {{MAR}},
Abstract = {{Objective Genome-wide association studies (GWAS) require high
   specificity and large numbers of subjects to identify genotype-phenotype
   correlations accurately. The aim of this study was to identify type 2
   diabetes (T2D) cases and controls for a GWAS, using data captured
   through routine clinical care across five institutions using different
   electronic medical record (EMR) systems.
   Materials and Methods An algorithm was developed to identify T2D cases
   and controls based on a combination of diagnoses, medications, and
   laboratory results. The performance of the algorithm was validated at
   three of the five participating institutions compared against clinician
   review. A GWAS was subsequently performed using cases and controls
   identified by the algorithm, with samples pooled across all five
   institutions.
   Results The algorithm achieved 98\% and 100\% positive predictive values
   for the identification of diabetic cases and controls, respectively, as
   compared against clinician review. By standardizing and applying the
   algorithm across institutions, 3353 cases and 3352 controls were
   identified. Subsequent GWAS using data from five institutions replicated
   the TCF7L2 gene variant (rs7903146) previously associated with T2D.
   Discussion By applying stringent criteria to EMR data collected through
   routine clinical care, cases and controls for a GWAS were identified
   that subsequently replicated a known genetic variant. The use of
   standard terminologies to define data elements enabled pooling of
   subjects and data across five different institutions to achieve the
   robust numbers required for GWAS.
   Conclusions An algorithm using commonly available data from five
   different EMR can accurately identify T2D cases and controls for genetic
   study across multiple institutions.}},
DOI = {{10.1136/amiajnl-2011-000439}},
ISSN = {{1067-5027}},
ResearcherID-Numbers = {{Jarvik, Gail/N-6476-2014
   Bielinski, Suzette/A-2238-2009
   }},
ORCID-Numbers = {{Jarvik, Gail/0000-0002-6710-8708
   Bielinski, Suzette/0000-0002-2905-5430
   Pacheco, Jennifer/0000-0001-8021-5818}},
Unique-ID = {{ISI:000300768100014}},
}

@article{ ISI:000302733900001,
Author = {Oba, Shino and Noda, Mitsuhiko and Waki, Kayo and Nanri, Akiko and Kato,
   Masayuki and Takahashi, Yoshihiko and Poudel-Tandukar, Kalpana and
   Matsushita, Yumi and Inoue, Manami and Mizoue, Tetsaya and Tsugane,
   Shoichiro and Japan Public Hlth Ctr Based Prospe},
Title = {{Smoking Cessation Increases Short-Term Risk of Type 2 Diabetes
   Irrespective of Weight Gain: The Japan Public Health Center-Based
   Prospective Study}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{2}},
Month = {{FEB 13}},
Abstract = {{Objective: The effect of smoking cessation on the risk of diabetes has
   been reported previously. However, it is unknown whether the association
   is influenced by weight gain and other potential risk factors.
   Methods: The Japan Public Health Center-Based Prospective Study
   established in 1990 for Cohort I and in 1993 for Cohort II provided
   data, and 25,875 men and 33,959 women were analyzed. The response rate
   to the baseline questionnaire was 80.9\%, and 68.4\% of the respondents
   participated both the 5- and 10-year follow-up surveys. Smoking
   cessation was noted during the initial five years and the development of
   diabetes was reported in the subsequent five years.
   Results: An increased risk was observed among individuals who newly quit
   smoking compared with never smokers among men (odds ratio (OR) = 1.42,
   95\% CI = 1.03-1.94) and women (OR = 2.84, CI = 1.53-5.29). The risk of
   developing diabetes among male new quitters who gained 3 kg or more
   during the 5-year follow-up did not substantially differ from the risk
   among male never smokers with less than 3 kg of weight gain or no weight
   gain, while an increased risk was observed among male new quitters with
   less or no weight gain (OR = 1.46, 95\% CI 1.00-2.14). An insignificant
   increased risk was observed among male new quitters with a family
   history of diabetes compared with male never smokers with a family
   history of diabetes. The risk was more than twice as high for male new
   quitters who used to smoke 25 or more cigarettes per day compared with
   never smokers (OR = 2.15, 95\% CI: 1.34-3.47).
   Discussion: An increased risk of diabetes was implied among individuals
   who quit smoking. However, the increased risk was not implied among
   those who gained weight over the 5-years of follow-up. Those who had
   major risk factors for diabetes or who smoked heavier had a higher risk.}},
DOI = {{10.1371/journal.pone.0017061}},
Article-Number = {{e17061}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Tsugane, Shocichiro/A-2424-2015}},
Unique-ID = {{ISI:000302733900001}},
}

@article{ ISI:000301639600068,
Author = {Maatouk, Imad and Wild, Beate and Wesche, Daniela and Herzog, Wolfgang
   and Raum, Elke and Mueller, Heiko and Rothenbacher, Dietrich and
   Stegmaier, Christa and Schellberg, Dieter and Brenner, Hermann},
Title = {{Elderly People with Diabetes: Results of a German Cohort Study}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{1}},
Month = {{JAN 24}},
Abstract = {{Background: The aim of the study was to determine predictors that
   influence health-related quality of life (HRQOL) in a large cohort of
   elderly diabetes patients from primary care over a follow-up period of
   five years.
   Methods and Results: At the baseline measurement of the ESTHER cohort
   study (2000-2002), 1375 out of 9953 participants suffered from diabetes
   (13.8\%). 1057 of these diabetes patients responded to the second-follow
   up (2005-2007). HRQOL at baseline and follow-up was measured using the
   SF-12; mental component scores (MCS) and physical component scores (PCS)
   were calculated; multiple linear regression models were used to
   determine predictors of HRQOL at follow-up. As possible predictors for
   HRQOL, the following baseline variables were examined: treatment with
   insulin, glycated hemoglobin (HbA1c), number of diabetes related
   complications, number of comorbid diseases, Body-Mass-Index (BMI),
   depression and HRQOL. Regression analyses were adjusted for
   sociodemographic variables and smoking status. 1034 patients (97.8\%)
   responded to the SF-12 both at baseline and after five years and were
   therefore included in the study. Regression analyses indicated that
   significant predictors of decreased MCS were a lower HRQOL, a higher
   number of diabetes related complications and a reported history of
   depression at baseline. Complications, BMI, smoking and HRQOL at
   baseline significantly predicted PCS at the five year follow-up.
   Conclusions: Our findings expand evidence from previous cross-sectional
   data indicating that in elderly diabetes patients, depression, diabetes
   related complications, smoking and BMI are temporally predictive for
   HRQOL.}},
DOI = {{10.1371/journal.pone.0031088}},
Article-Number = {{e31088}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000301639600068}},
}

@article{ ISI:000301570600013,
Author = {Yoo, Dong Eun and Park, Jung Tak and Oh, Hyung Jung and Kim, Seung Jun
   and Lee, Mi Jung and Shin, Dong Ho and Han, Seung Hyeok and Yoo,
   Tae-Hyun and Choi, Kyu Hun and Kang, Shin-Wook},
Title = {{Good Glycemic Control Is Associated with Better Survival in Diabetic
   Patients on Peritoneal Dialysis: A Prospective Observational Study}},
Journal = {{PLOS ONE}},
Year = {{2012}},
Volume = {{7}},
Number = {{1}},
Month = {{JAN 23}},
Abstract = {{Background: The effect of glycemic control after starting peritoneal
   dialysis (PD) on the survival of diabetic PD patients has largely been
   unexplored, especially in Asian population.
   Methods: We conducted a prospective observational study, in which 140
   incident PD patients with diabetes were recruited. Patients were divided
   into tertiles according to the means of quarterly HbA1C levels measured
   during the first year after starting PD. We examined the association
   between HbA1C and all-cause mortality using Cox proportional hazards
   models.
   Results: The mean age was 58.7 years, 59.3\% were male, and the mean
   follow-up duration was 3.5 years (range 0.4-9.5 years). The mean HbA1C
   levels were 6.3\%, 7.1\%, and 8.5\% in the 1st, 2nd, and 3rd tertiles,
   respectively. Compared to the 1st tertile, the all-cause mortality rates
   were higher in the 2nd {[}hazard ratio (HR), 4.16; 95\% confidence
   interval (CI), 0.91-18.94; p = 0.065] and significantly higher in the
   3rd (HR, 13.16; 95\% CI, 2.67-64.92; p = 0.002) tertiles (p for trend =
   0.005), after adjusting for confounding factors. Cardiovascular
   mortality, however, did not differ significantly among the tertiles (p
   for trend = 0.682). In contrast, non-cardiovascular deaths, most of
   which were caused by infection, were more frequent in the 2nd (HR, 7.67;
   95\% CI, 0.68-86.37; p = 0.099) and the 3rd (HR, 51.24; 95\% CI,
   3.85-681.35; p = 0.003) tertiles than the 1st tertile (p for trend =
   0.007).
   Conclusions: Poor glycemic control is associated with high mortality
   rates in diabetic PD patients, suggesting that better glycemic control
   may improve the outcomes of these patients.}},
DOI = {{10.1371/journal.pone.0030072}},
Article-Number = {{e30072}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000301570600013}},
}

@inproceedings{ ISI:000319285900075,
Author = {Martinez, Ignacio V. and Hernando, M. Elena and Gomez, Enrique J. and
   Villares, Ricardo and Mellado, Mario},
Editor = {{Rocha, A and CalvoManzano, JA and Reis, LP and Cota, MP}},
Title = {{Definition of an Agent-Based Model of the Autoimmune Response in Type 1
   Diabetes}},
Booktitle = {{INFORMATION SYSTEMS AND TECHNOLOGIES}},
Series = {{Iberian Conference on Information Systems and Technologies}},
Year = {{2012}},
Note = {{7th Iberian Conference on Information Systems and Technologies (CISTI),
   Madrid, SPAIN, JUN 20-23, 2012}},
Organization = {{Univ Politecnica Madrid (UPM), Asociacion Iberica Sistemas \&
   Tecnologias Informacion (AISTI)}},
Abstract = {{This work describes the architecture of an agent based computer model
   that simulates the autoimmune response in Type 1 diabetes in a
   three-dimensional cubic volume representing a fragment of pancreas of a
   mouse. Immune cells and beta-cells are modeled as agents, making the
   abstraction of one cell - one agent. We present our preliminary results
   for the interactions between beta-cell agents and macrophage agents and
   a model of cell motility in the continuous three-dimensional volume.
   This agent-based approach allows implementing multi-scale computational
   modeling capturing subcellular characteristics into cell agents and
   obtaining outcomes from agent populations at tissue and systemic levels.}},
ISSN = {{2166-0727}},
ISBN = {{978-1-4673-2843-2}},
ResearcherID-Numbers = {{Hernando, M. Elena/A-9575-2008}},
ORCID-Numbers = {{Hernando, M. Elena/0000-0001-6182-313X}},
Unique-ID = {{ISI:000319285900075}},
}

@inproceedings{ ISI:000316869201018,
Author = {Luangruangrong, Wuttichai and Rodtook, Annupan and Chimmanee, Sanon},
Book-Group-Author = {{IEEE}},
Title = {{Study of Type 2 Diabetes Risk Factors Using Neural Network For Thai
   People and Tuning Neural Network Parameters.}},
Booktitle = {{PROCEEDINGS 2012 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND
   CYBERNETICS (SMC)}},
Series = {{IEEE International Conference on Systems Man and Cybernetics Conference
   Proceedings}},
Year = {{2012}},
Pages = {{991-996}},
Note = {{IEEE International Conference on Systems, Man, and Cybernetics (SMC),
   Seoul, SOUTH KOREA, OCT 14-17, 2012}},
Organization = {{IEEE Systems, Man, and Cybernetics Soc (SMC); IEEE; Korea Univ; Korean
   Soc Cognit Sci (KSCS); Korean Inst Informat Scientists and Engineers Soc
   Computat Intelligence (KSCI); Hi Seoul; Korea Tourism Org; Asian Off
   Aerosp Res and Dev (AOARD); Natl Res Fdn Korea (NRF); World Class Univ;
   Korea Univ, WCU Res Div Brain and Cognit Engn}},
Abstract = {{Risk factors for Type 2 diabetes is very important for developing
   diabetes prediction tools instead of blood testing. Recently, many
   researches have studied risk factors of diabetes in order to apply them
   to be a tool for diabetes prediction by using Logistic Regression (LR),
   Radial Basis and Back-propagation Neural Network (BNN). However, the
   accuracy is not higher. This paper presents new factors that are smoking
   and alcohol consumption to improve accuracy in diabetes prediction. Some
   traditional factors i.e., body mass index (BMI), blood pressure (BP) and
   waist circumference (WC) and Family History (FMH) are also proposed to
   extent by adjusting and additional range. The proposed diabetes
   prediction method is based on BNN. Approximately 2,000 cases of Thai
   people at BMC hospital, Thailand during 2010 to 2012 are used to train
   the BNN. From experiment results, each proposed factors i.e., FMH,
   Alcohol consumption factor, Smoking Factors and WC gives a value of
   accuracy that is higher than baseline as 83.35\%, 83.5\%, 83.6\% and
   83.65\%, respectively. After that, this paper focuses on tuning neural
   network parameter, which is divided into 3 main steps: number of hidden
   nodes, sequence of integrating the proposed factors, and other parameter
   i.e., learning rate, and Iteration. Finally, the proposed factors and
   tuning BNN parameters introduce a high accuracy compared with the
   baseline up to 1.2\%.}},
ISSN = {{1062-922X}},
ISBN = {{978-1-4673-1714-6}},
Unique-ID = {{ISI:000316869201018}},
}

@inproceedings{ ISI:000315044701085,
Author = {Thirugnanam, Mythili and Kumar, Praveen and Srivatsan, S. Vignesh and
   Nerlesh, C. R.},
Editor = {{Rajesh, R and Ganesh, K and Koh, SCL}},
Title = {{Improving the Prediction rate of Diabetes Diagnosis Using Fuzzy, Neural
   Network, Case Based (FNC) approach}},
Booktitle = {{INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING}},
Series = {{Procedia Engineering}},
Year = {{2012}},
Volume = {{38}},
Pages = {{1709-1718}},
Note = {{International Conference on Modelling Optimisation and Computing
   (ICMOC), Kumarakoil, INDIA, APR 10-11, 2012}},
Organization = {{Dept Mech Engn, Noorul Islam Ctr Higher Educ}},
Abstract = {{In today's world the diabetes mellitus has become a major health problem
   among the people of all ages. Most of the researchers have proposed
   different systems to diagnose diabetes, in spite of which the accuracy
   of the prediction rate of diabetes is not so significant. Most of the
   techniques suggested were to detect diabetes associated with the parts
   of the body such as eye, heart or legs. All the developed systems aimed
   to identify diabetes by increasing the accuracy of the prediction rate
   but fail to do so in most of the cases as there were some issues that
   had not been discussed. The existing systems had number of drawbacks
   like some were only concentrated for women and who were less than 21
   years old {[}5], some gave greater accuracy if only one data set was
   used {[}6], some needed dataset of very good quality {[}12], some needed
   to standardize the ontology {[}10] and some even failed to show the
   performance of the developed systems {[}7]. So there is an need to
   develop a diabetes diagnosis system that improves the accuracy of the
   prediction rate by considering all the factors into account. This paper
   presents a novel approach for diagnosis of diabetes which has two stages
   to predict the diabetes status. The initial prediction stage adopts two
   computational intelligence and knowledge engineering techniques such as
   fuzzy logic (F), neural network (N) and case based reasoning (C) as an
   individual approach (FCN). The final prediction stage applies rule based
   algorithm to the values obtained from the initial stage. The benefit of
   applying these stages is that the accuracy of prediction rate will be
   higher when compared to using only the initial prediction stage done by
   most of the suggested systems for predicting the occurrence of diabetes
   mellitus. (c) 2012 Published by Elsevier Ltd. Selection and/or
   peer-review under responsibility of Noorul Islam Centre for Higher
   Education}},
DOI = {{10.1016/j.proeng.2012.06.208}},
ISSN = {{1877-7058}},
Unique-ID = {{ISI:000315044701085}},
}

@inproceedings{ ISI:000313296503029,
Author = {Georga, Eleni I. and Protopappas, Vasilios C. and Polyzos, Demosthenes
   and Fotiadis, Dimitrios I.},
Book-Group-Author = {{IEEE}},
Title = {{A Predictive Model of Subcutaneous Glucose Concentration in Type 1
   Diabetes Based on Random Forests}},
Booktitle = {{2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2012}},
Pages = {{2889-2892}},
Note = {{34th Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBS), San Diego, CA, AUG
   28-SEP 01, 2012}},
Organization = {{IEEE; Engn Med \& Biol Soc (EMBS); CAS; SMC; PubMed; MEDLINE}},
Abstract = {{In this study, an individualized predictive model of the subcutaneous
   glucose concentration in type 1 diabetes is presented, which relies on
   the Random Forests regression technique. A multivariate dataset is
   utilized concerning the s.c. glucose profile, the plasma insulin
   concentration, the intestinal absorption of meal-derived glucose and the
   daily energy expenditure. In an attempt to capture daily rhythms in
   glucose metabolism, we also introduce a time feature in the predictive
   analysis. The dataset comes from the continuous multi-day recordings of
   27 type 1 patients in free-living conditions. Evaluating the performance
   of the proposed method by 10-fold cross validation, an average RMSE of
   6.60, 8.15, 9.25 and 10.83 mg/dl for 15, 30, 60 and 120 min prediction
   horizons, respectively, was attained.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4577-1787-1}},
Unique-ID = {{ISI:000313296503029}},
}

@inproceedings{ ISI:000313296506068,
Author = {Costa, Clayton M. and Gondim, Dikson D. and Gondim, Dibson D. and
   Soares, Heliana B. and Ribeiro, Anna G. C. D. and Silva, Ikaro and
   Winkler, Erick and Celi, Leo and Guerreiro, Ana M. G. and Leite, Cicilia
   R. M.},
Book-Group-Author = {{IEEE}},
Title = {{S2DIA: A Diagnostic System for Diabetes mellitus using SANA platform}},
Booktitle = {{2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2012}},
Pages = {{6078-6081}},
Note = {{34th Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBS), San Diego, CA, AUG
   28-SEP 01, 2012}},
Organization = {{IEEE; Engn Med \& Biol Soc (EMBS); CAS; SMC; PubMed; MEDLINE}},
Abstract = {{Currently, Diabetes is a very common disease around the world, and with
   an increase in sedentary lifestyles, obesity and an aging population the
   number of people with Diabetes worldwide will increase by more than
   50\%. In this context, the MIT (Massachusetts Institute of Teehnology)
   developed the SANA platform, which brings the benefits of information
   technology to the field of healthcare. It offers healthcare delivery in
   remote areas, improves patient access to medical specialists for faster,
   higher quality, and more cost effective diagnosis and intervention. For
   these reasons, we developed a system for diagnosis of Diabetes using the
   SANA platform, called S2DIA. It is the first step towards knowing the
   risks for type 2 Diabetes, and it will be evaluated, especially, in
   remote/poor areas of Brazil.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4577-1787-1}},
Unique-ID = {{ISI:000313296506068}},
}

@inproceedings{ ISI:000313296506128,
Author = {San, Phyo Phyo and Ling, Sai Ho and Nguyen, Hung T.},
Book-Group-Author = {{IEEE}},
Title = {{Intelligent Detection of Hypoglycemic Episodes in Children with Type 1
   Diabetes using Adaptive Neural-Fuzzy Inference System}},
Booktitle = {{2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE
   AND BIOLOGY SOCIETY (EMBC)}},
Series = {{IEEE Engineering in Medicine and Biology Society Conference Proceedings}},
Year = {{2012}},
Pages = {{6325-6328}},
Note = {{34th Annual International Conference of the IEEE
   Engineering-in-Medicine-and-Biology-Society (EMBS), San Diego, CA, AUG
   28-SEP 01, 2012}},
Organization = {{IEEE; Engn Med \& Biol Soc (EMBS); CAS; SMC; PubMed; MEDLINE}},
Abstract = {{Hypoglycemia, or low blood glucose, is the most common complication
   experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous
   and can result in unconsciousness, seizures and even death. The most
   common physiological parameter to be effected from hypoglycemic reaction
   are heart rate (HR) and correct QT interval (QTc) of the
   electrocardiogram (ECG) signal. Based on physiological parameters, an
   intelligent diagnostics system, using the hybrid approach of adaptive
   neural fuzzy inference system (ANFIS), is developed to recognize the
   presence of hypoglycemia. The proposed ANFIS is characterized by
   adaptive neural network capabilities and the fuzzy inference system. To
   optimize the membership functions and adaptive network parameters, a
   global learning optimization algorithm called hybrid particle swarm
   optimization with wavelet mutation (HPSOWM) is used. For clinical study,
   15 children with Type 1 diabetes volunteered for an overnight study. All
   the real data sets are collected from the Department of Health,
   Government of Western Australia. Several experiments were conducted with
   5 patients each, for a training set (184 data points), a validation set
   (192 data points) and a testing set (153 data points), which are
   randomly selected. The effectiveness of the proposed detection method is
   found to be satisfactory by giving better sensitivity, 79.09\% and
   acceptable specificity, 51.82\%.}},
ISSN = {{1557-170X}},
ISBN = {{978-1-4577-1787-1}},
ORCID-Numbers = {{Ling, Sai Ho/0000-0003-0849-5098}},
Unique-ID = {{ISI:000313296506128}},
}

@inproceedings{ ISI:000313560200005,
Author = {Lan, Guo-Cheng and Lee, Chao-Hui and Lee, Yu-Yen and Tseng, Vincent S.
   and Chin, Chu-Yu and Day, Miin-Luen and Wang, Shyh-Chyi and Chang,
   Ching-Nain and Cheng, Shyr-Yuan and Wu, Jin-Shang},
Editor = {{Kuo, YH and Tseng, VSM and Kao, HY and Hong, TP and Horng, MF}},
Title = {{Disease Risk Prediction by Mining Personalized Health Trend Patterns: A
   Case Study on Diabetes}},
Booktitle = {{2012 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL
   INTELLIGENCE (TAAI)}},
Series = {{Conference on Technologies and Applications of Artificial Intelligence}},
Year = {{2012}},
Pages = {{27-32}},
Note = {{17th Annual Conference on Technologies and Applications of Artificial
   Intelligence (TAAI), Taiwanese Assoc Artificial Intelligence (TAAI),
   Tainan, TAIWAN, NOV 16-18, 2012}},
Organization = {{Taiwanese Assoc Artificial Intelligence; Natl Cheng Kung Univ, Dept Comp
   Sci \& Informat Engn; NCKU, Comp \& Network Ctr; Natl Sci Council;
   Minist Educ; IEEE, Tainan Sect; IEEE Comp Soc, Tainan Chapter; IEEE
   Signal Process Soc, Tainan Chapter; Inst Informat Ind; Inst Informat
   Sci, Acad Sinica; Res Ctr Informat Technol Innovat; Natl Univ Kaohsiung;
   Natl Kaohsiung Univ Appl Sci; Natl Chung Cheng Univ; Natl Dong Hwa Univ}},
Abstract = {{Health examination has played an important role for maintaining people's
   health since it can not only help people understand their own health
   conditions clearly but also avoid missing the best timing of disease
   treatment. However, in current health examination systems, people get
   only a basic report from single health examination and no advanced
   health risk analysis is provided. In this paper, we proposed an
   effective mechanism for chronic disease risk prediction by mining the
   data containing historical health records and personal life style
   information. Value change trends of the data are important for disease
   status prediction, and we defined significant ones as health risk
   patterns in our mechanism. Risks of a chronic disease can be predicted
   early with a mechanism built with our health risk patterns and it also
   proven work well through experimental evaluations on real datasets. Our
   method outperformed traditional mechanism in terms of accuracy,
   precision and sensitivity for predicting the risk of diabetes. In
   particular, insightful observations show that the consideration of
   life-style information can effectively enhance whole performance for
   risk prediction. Moreover, classification rules produced by our
   mechanism which integrates C4.5 and CBA provide physicians disease
   related health risk patterns such that appropriate treatments could be
   given to people for disease prevention.}},
DOI = {{10.1109/TAAI.2012.53}},
ISSN = {{2376-6816}},
ISBN = {{978-0-7695-4919-4; 978-1-4673-4976-5}},
Unique-ID = {{ISI:000313560200005}},
}

@inproceedings{ ISI:000312600600001,
Author = {Kang, Chiyong and Yu, Hyeji and Yi, Gwan-Su},
Book-Group-Author = {{ACM}},
Title = {{Detecting Type 2 Diabetes Causal Single Nucleotide Polymorphism
   Combinations from a Genome-Wide Association Study Dataset with Optimal
   Filtration}},
Booktitle = {{PROCEEDINGS OF THE ACM SIXTH INTERNATIONAL WORKSHOP ON DATA AND TEXT
   MINING IN BIOMEDICAL INFORMATICS}},
Year = {{2012}},
Pages = {{1-8}},
Note = {{6th ACM International Workshop on Data and Text Mining in Biomedical
   Informatics (DTMBIO) in Conjunction with the 21st ACM International
   Conference on Information and Knowledge Management (CIKM), Maui, HI, OCT
   29-NOV 02, 2012}},
Organization = {{Assoc Comp Machinery (ACM); Assoc Comp Machinery (ACM) Special Interest
   Grp Informat Retrieval (SIGIR); Assoc Comp Machinery (ACM) SIGWEB}},
Abstract = {{The identification of causal single nucleotide polymorphisms (SNPs) for
   complex diseases like type 2 diabetes (T2D) is a challenge because of
   the low statistical power of individual markers from a genome-wide
   association study (GWAS). SNP combinations are suggested to compensate
   for the low statistical power of individual markers, but SNP
   combinations from GWAS generate high computational complexity. Hence, we
   aim to detect T2D causal SNP combinations from a GWAS dataset with
   optimal filtration and to discover the biological meaning of the
   detected SNP combinations. Optimal filtration can enhance the
   statistical power of SNP combinations by comparing the error rates of
   SNP combinations from various Bonferroni thresholds and p-value
   range-based thresholds combined with linkage disequilibrium (LD)
   pruning. T2D causal SNP combinations are selected using random forests
   with variable selection from an optimal SNP dataset. The selected SNPs
   with SNP combinations are mapped with multi-dimensional levels of
   T2D-related information and gene set enrichment analysis (GSEA). A T2D
   causal SNP combination containing 101 SNPs from the Wellcome Trust Case
   Control Consortium (WTCCC) GWAS dataset are selected, with an error rate
   of 10.25\%. Matching with known disease genes and gene sets revealed the
   relationships between T2D and SNP combinations. We propose a detection
   method for complex disease causal SNP combinations from an optimal SNP
   dataset by using random forests with variable selection. Mapping the
   biological meanings of detected SNP combinations can help uncover
   complex disease mechanisms.}},
ISBN = {{978-1-4503-1716-0}},
Unique-ID = {{ISI:000312600600001}},
}

@inproceedings{ ISI:000310938200080,
Author = {Lunze, K. and Brendel, M. D. and Leonhardt, S.},
Editor = {{Jobbagy, A}},
Title = {{Preliminary Results of a Type-1 Diabetes Swine Model}},
Booktitle = {{5TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND
   BIOLOGICAL ENGINEERING, PTS 1 AND 2}},
Series = {{IFMBE Proceedings}},
Year = {{2012}},
Volume = {{37}},
Pages = {{307-310}},
Note = {{5th European Conference of the International Federation for Medical and
   Biological Engineering, Budapest, HUNGARY, SEP 14-18, 2011}},
Abstract = {{This article presents initial results of a chemically induced diabetic
   swine model. A Gottingen minipig was equipped with a commercially
   available subcutaneous measuring glucose sensor and an insulin pump.
   Blood and subcutaneous glucose concentrations were observed during
   healthy and diabetic periods. Induction of stable diabetes mellitus by
   Streptozocin injection was confirmed by oral glucose tolerance test. The
   measurements showed, that blood glucose level in Gottingen minipigs is
   in a similar range, albeit discretely lower compared to human subjects.
   Subcutaneous and intravenous glucose concentration were similar but
   differing. In the future, it seems to be reasonable to use a swine model
   as intermediate translational step between in-silica and human trials
   for the validation of control algorithms for blood glucose control.}},
ISSN = {{1680-0737}},
ISBN = {{978-3-642-23507-8}},
Unique-ID = {{ISI:000310938200080}},
}

@article{ ISI:000305734300001,
Author = {Torres-Rovira, L. and Astiz, S. and Caro, A. and Lopez-Bote, C. and
   Ovilo, C. and Pallares, P. and Perez-Solana, M. L. and Sanchez-Sanchez,
   R. and Gonzalez-Bulnes, A.},
Title = {{Diet-Induced Swine Model with Obesity/Leptin Resistance for the Study of
   Metabolic Syndrome and Type 2 Diabetes}},
Journal = {{SCIENTIFIC WORLD JOURNAL}},
Year = {{2012}},
Abstract = {{The objective of the present study was to determine the suitability of a
   swine breed with leptin resistance and predisposition to obesity (the
   Iberian pig) as model for studies on metabolic syndrome and type 2
   diabetes. Thus, six Iberian sows had ad libitum access to food enriched
   with saturated fat (SFAD group; food consumption was estimated to be 4.5
   kg/animal/day) whilst four females acted as controls and were fed with 2
   kg/animal/day of a commercial maintenance diet. After three months of
   differential feeding, SFAD animals developed central obesity,
   dyslipidemia, insulin resistance and impaired glucose tolerance, and
   elevated blood pressure; the five parameters associated with the
   metabolic syndrome. Thus, the current study characterizes the Iberian
   pig as a robust, amenable, and reliable translational model for studies
   on nutrition-associated diseases.}},
DOI = {{10.1100/2012/510149}},
Article-Number = {{510149}},
ISSN = {{1537-744X}},
ResearcherID-Numbers = {{Lopez-Bote, Clemente/F-5103-2014
   Ovilo, Cristina/N-9931-2014}},
ORCID-Numbers = {{Lopez-Bote, Clemente/0000-0002-6839-8057
   Ovilo, Cristina/0000-0002-5738-8435}},
Unique-ID = {{ISI:000305734300001}},
}

@article{ ISI:000299648400019,
Author = {Chien, Shou-Wei and Bau, Cho-Tsan and Lin, Ker-Chung and Wang, Kuo-An
   and Chen, Yung-Fu and Chen, Jong-Chen},
Title = {{EVALUATION OF INTELLIGENT SYSTEM TO THE CONTROL OF DIABETES}},
Journal = {{INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL}},
Year = {{2012}},
Volume = {{8}},
Number = {{1B}},
Pages = {{779-790}},
Month = {{JAN}},
Abstract = {{Diabetes, which ranks 4th among the top ten causes of death in Taiwan,
   is one of the most important medical issues in the 21st century.
   However, for most diabetic patients, their blood sugar is not under good
   control, especially in ICUs. Good control of blood sugar may reduce the
   risk of sepsis to 34\% for patients in ICUs. The importance of good
   blood sugar control for patients in ICUs is manifested by a significant
   reduction of mortality and morbidity. This study designed a clinical
   decision support system (CDSS) using a support vector machine (SVM) to
   predict if a critically ill patient can have good glucose control after
   insulin administration. A filter method based on logistic regression
   analysis (LRA) and a wrapper method based on recursive feature
   elimination (RFE) were adopted to select salient features from 10
   variables for CDSS design. Data on 231 patients (2492 records) were
   collected covering four years from an ICU. Four significant variables (p
   < 0.05) using LRA in contrast to five ones using RFE algorithm, were
   selected. The results show that the predictive accuracy under
   cross-validation was 93.50\% for features selected with LRA, and the
   accuracy, sensitivity and specificity with SVM-RFE were 95.75\%, 92.71\%
   and 99.81\%, respectively. It could predict the outcome quite accurately
   after having injected a certain dose of insulin. The proposed system may
   help doctors effectively assess their patients in determining insulin
   dose for better glucose control in an ICU setting.}},
ISSN = {{1349-4198}},
Unique-ID = {{ISI:000299648400019}},
}

@article{ ISI:000298152700005,
Author = {Featherstone, Imogen and Keen, Justin},
Title = {{Do integrated record systems lead to integrated services? An
   observational study of a multi-professional system in a diabetes service}},
Journal = {{INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS}},
Year = {{2012}},
Volume = {{81}},
Number = {{1}},
Pages = {{45-52}},
Month = {{JAN}},
Abstract = {{Purpose: There are few opportunities to study the use of integrated
   electronic health record (EHR) systems, where single patient records are
   used by a number of health care staff. The purpose of the study was to
   understand how an integrated electronic health record system was used by
   health care staff in the treatment and management of diabetes patients.
   The investigation was focused on the interface between the primary and
   secondary care services which patients access for their diabetes care.
   Methods: Observations of the use of an integrated electronic health
   record during patients' consultations with health care staff were
   conducted over a three month period. Twelve patients were followed
   through their consultations with a range of health care staff at a
   secondary care Diabetes Centre and in primary care settings. A thematic
   analysis of the observation data was undertaken.
   Results: The integrated electronic health record system had been
   implemented across the primary and secondary care interface and was used
   by many, but not all, clinicians involved in the care of patients with
   diabetes. In some areas of care it enabled health care staff to access
   more accurate and detailed information to inform their clinical
   decision-making. Issues including negotiating rules for accessing
   patient records and duplication of recording in paper record systems had
   not been resolved consistently across services.
   Conclusions: The findings offer suggestive evidence that a shared
   electronic health record can support more integrated care. Unresolved
   issues in implementing the system across all services and settings
   highlight the governance problems that can arise when systems are
   developed locally but are then extended across organisational and
   professional boundaries. (C) 2011 Elsevier Ireland Ltd. All rights
   reserved.}},
DOI = {{10.1016/j.ijmedinf.2011.09.002}},
ISSN = {{1386-5056}},
Unique-ID = {{ISI:000298152700005}},
}

@inproceedings{ ISI:000350201500031,
Author = {Yan, Ke and Zhang, David},
Book-Group-Author = {{IEEE}},
Title = {{A Novel Breath Analysis System for Diabetes Diagnosis}},
Booktitle = {{2012 INTERNATIONAL CONFERENCE ON COMPUTERIZED HEALTHCARE (ICCH)}},
Year = {{2012}},
Pages = {{165-169}},
Note = {{International Conference on Computerized Healthcare (ICCH), Hong Kong,
   PEOPLES R CHINA, DEC 17-18, 2012}},
Organization = {{IEEE Hong Kong Sect Comp Chapter; IEEE Comp Soc; Hong Kong Polytechn
   Univ}},
Abstract = {{A noninvasive breath analysis system for diabetes diagnosis is proposed
   in this paper. It utilizes commercial chemical sensors to detect acetone
   in human breath. The device is portable, fast and easy to operate.
   Experiments with real breath samples from both inpatient and outpatient
   diabetics validated the accuracy of this system. An optimal sensor array
   for diabetes diagnosis is decided according to the classification
   accuracy. We hope this system could contribute a step toward a practical
   system that could be used for diabetes screening.}},
ISBN = {{978-1-4673-5127-0}},
Unique-ID = {{ISI:000350201500031}},
}

@incollection{ ISI:000310546800002,
Author = {Deja, Rafal},
Editor = {{Kapczynski, A and Tkacz, E and Rostanski, M}},
Title = {{Comparison of Rules Synthesis Methods Accuracy in the System of Type 1
   Diabetes Prediction}},
Booktitle = {{INTERNET-TECHNICAL DEVELOPMENTS AND APPLICATIONS 2}},
Series = {{Advances in Intelligent and Soft Computing}},
Year = {{2012}},
Volume = {{118}},
Pages = {{13-24}},
Abstract = {{While creating the decision support system we encounter the
   classification accuracy problem. In the paper author compares the
   accuracy of two rules synthesis algorithms based on the rough set
   theory. This comparison is based on the medical support system that goal
   is to predict the illness among the children with genetic susceptibility
   to DMT1. The system can help to recommend including a person to
   pre-diabetes therapy.}},
ISSN = {{1867-5662}},
ISBN = {{978-3-642-25354-6; 978-3-642-25355-3}},
Unique-ID = {{ISI:000310546800002}},
}

@article{ ISI:000298369100008,
Author = {Lee, Timothy C. and Glynn, Robert J. and Pena, Jessica M. and Paynter,
   Nina P. and Conen, David and Ridker, Paul M. and Pradhan, Aruna D. and
   Buring, Julie E. and Albert, Michelle A.},
Title = {{Socioeconomic Status and Incident Type 2 Diabetes Mellitus: Data from
   the Women's Health Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{12}},
Month = {{DEC 14}},
Abstract = {{Objectives: We prospectively examined whether socioeconomic status (SES)
   predicts incident type II diabetes (diabetes), a cardiovascular risk
   equivalent and burgeoning public health epidemic among women.
   Methods: Participants include 23,992 women with Hb(A1c) levels <6\% and
   no CVD or diabetes at baseline followed from February 1993 to March
   2007. SES was measured by education and income while diabetes was
   self-reported.
   Results: Over 12.3 years of follow-up, 1,262 women developed diabetes.
   In age and race adjusted models, the relative risk of diabetes decreased
   with increasing education (<2 years of nursing, 2 to <4 years of
   nursing, bachelor's degree, master's degree, and doctorate: 1.0, 0.7
   {[}95\% Confidence Interval (CI), 0.6-0.8], 0.6 (95\% CI, 0.5-0.7), 0.5
   (95\% CI, 0.4-0.6), 0.4 (95\% CI, 0.3-0.5); p(trend)<0.001). Adjustment
   for traditional and non-traditional cardiovascular risk factors
   attenuated this relationship (education: p(trend)=0.96). Similar
   associations were observed between income categories and diabetes.
   Conclusion: Advanced education and increasing income were both inversely
   associated with incident diabetes even in this relatively well-educated
   cohort. This relationship was largely explained by behavioral factors,
   particularly body mass index.}},
DOI = {{10.1371/journal.pone.0027670}},
Article-Number = {{e27670}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000298369100008}},
}

@article{ ISI:000297832500003,
Author = {Calm, R. and Garcia-Jaramillo, M. and Bondia, J. and Sainz, M. A. and
   Vehi, J.},
Title = {{Comparison of interval and Monte Carlo simulation for the prediction of
   postprandial glucose under uncertainty in type 1 diabetes mellitus}},
Journal = {{COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}},
Year = {{2011}},
Volume = {{104}},
Number = {{3}},
Pages = {{325-332}},
Month = {{DEC}},
Abstract = {{In this paper, the problem of tackling uncertainty in the prediction of
   postprandial blood glucose is analyzed. Two simulation approaches, Monte
   Carlo and interval models, are studied and compared. Interval simulation
   is carried out using modal interval analysis. Simulation of a
   glucoregulatory model with uncertainty in insulin sensitivities, glucose
   absorption and food intake is carried out using both methods. Interval
   simulation is superior in predicting all severe and mild hyper- and
   hypoglycemia episodes. Furthermore, much less computational time is
   required for interval simulation than for Monte Carlo simulation. (C)
   2010 Elsevier Ireland Ltd. All rights reserved.}},
DOI = {{10.1016/j.cmpb.2010.08.008}},
ISSN = {{0169-2607}},
ResearcherID-Numbers = {{Vehi, Josep/D-6602-2013
   }},
ORCID-Numbers = {{Vehi, Josep/0000-0001-6884-9789
   Calm, Remei/0000-0002-2756-9315}},
Unique-ID = {{ISI:000297832500003}},
}

@article{ ISI:000297557900005,
Author = {Alanis, Alma Y. and Leon, Blanca S. and Sanchez, Edgar N. and
   Ruiz-Velazquez, Eduardo},
Title = {{BLOOD GLUCOSE LEVEL NEURAL MODEL FOR TYPE 1 DIABETES MELLITUS PATIENTS}},
Journal = {{INTERNATIONAL JOURNAL OF NEURAL SYSTEMS}},
Year = {{2011}},
Volume = {{21}},
Number = {{6}},
Pages = {{491-504}},
Month = {{DEC}},
Abstract = {{This paper deals with the 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 a dynamical mathematical model for the T1DM as the response of a
   patient to meal and subcutaneous insulin infusion. Experimental data
   given by continuous glucose monitoring system is utilized for
   identification and for testing the applicability of the proposed scheme
   to T1DM subjects.}},
DOI = {{10.1142/S0129065711003000}},
ISSN = {{0129-0657}},
Unique-ID = {{ISI:000297557900005}},
}

@article{ ISI:000297197000032,
Author = {Lopez-Rios, Laura and Novoa, Francisco J. and Chirino, Ricardo and
   Varillas, Francisco and Boronat-Cortes, Mauro and Waegner, Ana M.},
Title = {{Interaction between Cholesteryl Ester Transfer Protein and Hepatic
   Lipase Encoding Genes and the Risk of Type 2 Diabetes: Results from the
   Telde Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{11}},
Month = {{NOV 3}},
Abstract = {{Background and Aim: Diabetic dyslipidaemia is common in type 2 diabetes
   (T2D) and insulin resistance and often precedes the onset of T2D. The
   Taq1B polymorphism in CETP (B1 and B2 alleles) (rs708272) and the G-250A
   polymorphism in LIPC (rs2070895) are associated with changes in enzyme
   activity and lipid concentrations. Our aim was to assess the effects of
   both polymorphisms on the risk of T2D.
   Methods and Results: In a case-control study from the population-based
   Telde cohort, both polymorphisms were analysed by PCR-based methods.
   Subjects were classified, according to an oral glucose tolerance test,
   into diabetic (N = 115) and pre-diabetic (N = 116); 226 subjects with
   normal glucose tolerance, matched for age and gender, were included as
   controls. Chi-square (comparison between groups) and logistic regression
   (identification of independent effects) were used for analysis. The B1B1
   Taq1B CETP genotype frequency increased with worsening glucose
   metabolism (42.5\%, 46.1\% and 54.3\% in control, IGR and diabetic
   group; p = 0.042). This polymorphism was independently associated with
   an increased risk of diabetes (OR: 1.828; IC 95\%: 1.12-2.99; p =
   0.016), even after adjusting by confounding variables, whereas the LIPC
   polymorphism was not. Regarding the interaction between both
   polymorphisms, in the B1B1 genotype carriers, the absence of the minor
   (A) allele of the LIPC polymorphism increased the risk of having
   diabetes.
   Conclusion: The presence of the B1B1 Taq1B CETP genotype contributes to
   the presence of diabetes, independently of age, sex, BMI and waist.
   However, among carriers of B1B1, the presence of GG genotype of the -250
   LIPC polymorphism increases this risk further.}},
DOI = {{10.1371/journal.pone.0027208}},
Article-Number = {{e27208}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000297197000032}},
}

@article{ ISI:000295193400030,
Author = {Ganji, Mostafa Fathi and Abadeh, Mohammad Saniee},
Title = {{A fuzzy classification system based on Ant Colony Optimization for
   diabetes disease diagnosis}},
Journal = {{EXPERT SYSTEMS WITH APPLICATIONS}},
Year = {{2011}},
Volume = {{38}},
Number = {{12}},
Pages = {{14650-14659}},
Month = {{NOV-DEC}},
Abstract = {{Classification systems have been widely utilized in medical domain to
   explore patient's data and extract a predictive model. This model helps
   physicians to improve their prognosis, diagnosis or treatment planning
   procedures. The aim of this paper is to use an Ant Colony-based
   classification system to extract a set of fuzzy rules for diagnosis of
   diabetes disease, named FCS-ANTMINER. We will review some recent methods
   and describe a new and efficient approach that leads us to considerable
   results for diabetes disease classification problem. FCS-ANTMINER has
   new characteristics that make it different from the existing methods
   that have utilized the Ant Colony Optimization (ACO) for classification
   tasks. The obtained classification accuracy is 84.24\% which reveals
   that FCS-ANTMINER outperforms several famous and recent methods in
   classification accuracy for diabetes disease diagnosis. (C) 2011
   Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.eswa.2011.05.018}},
ISSN = {{0957-4174}},
EISSN = {{1873-6793}},
Unique-ID = {{ISI:000295193400030}},
}

@article{ ISI:000295984400022,
Author = {Brown, Christopher T. and Davis-Richardson, Austin G. and Giongo,
   Adriana and Gano, Kelsey A. and Crabb, David B. and Mukherjee, Nabanita
   and Casella, George and Drew, Jennifer C. and Ilonen, Jorma and Knip,
   Mikael and Hyoty, Heikki and Veijola, Riitta and Simell, Tuula and
   Simell, Olli and Neu, Josef and Wasserfall, Clive H. and Schatz, Desmond
   and Atkinson, Mark A. and Triplett, Eric W.},
Title = {{Gut Microbiome Metagenomics Analysis Suggests a Functional Model for the
   Development of Autoimmunity for Type 1 Diabetes}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{10}},
Month = {{OCT 17}},
Abstract = {{Recent studies have suggested a bacterial role in the development of
   autoimmune disorders including type 1 diabetes (T1D). Over 30 billion
   nucleotide bases of Illumina shotgun metagenomic data were analyzed from
   stool samples collected from four pairs of matched T1D case-control
   subjects collected at the time of the development of T1D associated
   autoimmunity (i.e., autoantibodies). From these, approximately one
   million open reading frames were predicted and compared to the SEED
   protein database. Of the 3,849 functions identified in these samples,
   144 and 797 were statistically more prevalent in cases and controls,
   respectively. Genes involved in carbohydrate metabolism, adhesions,
   motility, phages, prophages, sulfur metabolism, and stress responses
   were more abundant in cases while genes with roles in DNA and protein
   metabolism, aerobic respiration, and amino acid synthesis were more
   common in controls. These data suggest that increased adhesion and
   flagella synthesis in autoimmune subjects may be involved in triggering
   a T1D associated autoimmune response. Extensive differences in metabolic
   potential indicate that autoimmune subjects have a functionally aberrant
   microbiome. Mining 16S rRNA data from these datasets showed a higher
   proportion of butyrate-producing and mucin-degrading bacteria in
   controls compared to cases, while those bacteria that produce short
   chain fatty acids other than butyrate were higher in cases. Thus, a key
   rate-limiting step in butyrate synthesis is more abundant in controls.
   These data suggest that a consortium of lactate-and butyrate-producing
   bacteria in a healthy gut induce a sufficient amount of mucin synthesis
   to maintain gut integrity. In contrast, non-butyrate-producing
   lactate-utilizing bacteria prevent optimal mucin synthesis, as
   identified in autoimmune subjects.}},
DOI = {{10.1371/journal.pone.0025792}},
Article-Number = {{e25792}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{wasserfall, clive/J-9078-2012
   Giongo, Adriana/P-8718-2015}},
ORCID-Numbers = {{wasserfall, clive/0000-0002-3522-8932
   Giongo, Adriana/0000-0002-1412-7040}},
Unique-ID = {{ISI:000295984400022}},
}

@article{ ISI:000295966900080,
Author = {Burger, Koert N. J. and Beulens, Joline W. J. and Boer, Jolanda M. A.
   and Spijkerman, Annemieke M. W. and van der A, Daphne L.},
Title = {{Dietary Glycemic Load and Glycemic Index and Risk of Coronary Heart
   Disease and Stroke in Dutch Men and Women: The EPIC-MORGEN Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{10}},
Month = {{OCT 5}},
Abstract = {{Background: The associations of glycemic load (GL) and glycemic index
   (GI) with the risk of cardiovascular diseases (CVD) are not
   well-established, particularly in men, and may be modified by gender.
   Objective: To assess whether high dietary GL and GI increase the risk of
   CVD in men and women.
   Methods: A large prospective cohort study (EPIC-MORGEN) was conducted
   within the general Dutch population among 8,855 men and 10,753 women,
   aged 21-64 years at baseline (1993-1997) and free of diabetes and CVD.
   Dietary intake was assessed with a validated food-frequency
   questionnaire and GI and GL were calculated using Foster-Powell's
   international table of GI. Information on morbidity and mortality was
   obtained through linkage with national registries. Cox proportional
   hazards analysis was performed to estimate hazard ratios (HRs) for
   incident coronary heart disease (CHD) and stroke, while adjusting for
   age, CVD risk factors, and dietary factors.
   Results: During a mean follow-up of 11.9 years, 581 CHD cases and 120
   stroke cases occurred among men, and 300 CHD cases and 109 stroke cases
   occurred among women. In men, GL was associated with an increased CHD
   risk (adjusted HR per SD increase, 1.17 {[}95\% CI, 1.02-1.35]), while
   no significant association was found in women (1.09 {[}0.89-1.33]). GI
   was not associated with CHD risk in both genders, while it was
   associated with increased stroke risk in men (1.27 {[}1.02-1.58]) but
   not in women (0.96 {[}0.75-1.22]). Similarly, total carbohydrate intake
   and starch intake were associated with a higher CHD risk in men (1.23
   {[}1.04-1.46]; and 1.24 {[}1.07-1.45]), but not in women.
   Conclusion: Among men, high GL and GI, and high carbohydrate and starch
   intake, were associated with increased risk of CVD.}},
DOI = {{10.1371/journal.pone.0025955}},
Article-Number = {{e25955}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000295966900080}},
}

@article{ ISI:000295966900071,
Author = {Li, Chia-Ing and Chien, Ling and Liu, Chiu-Shong and Lin, Wen-Yuan and
   Lai, Ming-May and Lee, Cheng-Chun and Chen, Fei-Na and Li, Tsai-Chung
   and Lin, Cheng-Chieh},
Title = {{Prospective Validation of American Diabetes Association Risk Tool for
   Predicting Pre-Diabetes and Diabetes in Taiwan-Taichung Community Health
   Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{10}},
Month = {{OCT 5}},
Abstract = {{Background: A simple diabetes risk tool that does not require laboratory
   tests would be beneficial in screening individuals at higher risk. Few
   studies have evaluated the ability of these tools to identify new cases
   of pre-diabetes. This study aimed to assess the ability of the American
   Diabetes Association Risk Tool (ADART) to predict the 3-year incidence
   of pre-diabetes and diabetes in Taiwanese.
   Methods: This was a 3-year prospective study of 1021 residents with
   normoglycemia at baseline, gathered from a random sample of residents
   aged 40-88 years in a metropolitan city in Taiwan. The areas under the
   curve (AUCs) of three models were compared: ADART only, ADART plus
   lifestyle behaviors at baseline, and ADART plus lifestyle behaviors and
   biomarkers at baseline. The performance of ADART was compared with that
   of 16 tools that had been reported in the literature.
   Results: The AUCs and their 95\% confidence intervals (CIs) were 0.60
   (0.54-0.66) for men and 0.72 (0.66-0.77) for women in model 1; 0.62
   (0.56-0.68) for men and 0.74 (0.68-0.80) for women in model 2; and 0.64
   (0.58-0.71) for men and 0.75 (0.69-0.80) for women in model 3. The AUCs
   of these three models were all above 0.7 in women, but not in men. No
   significant difference in either women or men (p = 0.268 and 0.156,
   respectively) was observed in the AUC of these three models. Compared to
   16 tools published in the literature, ADART had the second largest AUC
   in both men and women.
   Conclusions: ADART is a good screening tool for predicting the
   three-year incidence of pre-diabetes and diabetes in females of a
   Taiwanese population. The performance of ADART in men was similar to the
   results with other tools published in the literature. Its performance
   was one of the best among the tools reported in the literature.}},
DOI = {{10.1371/journal.pone.0025906}},
Article-Number = {{e25906}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Li, Tsai-Chung/P-2052-2015
   Lin, Cheng-Chieh/P-2564-2015}},
ORCID-Numbers = {{Li, Tsai-Chung/0000-0002-3346-7462
   Lin, Cheng-Chieh/0000-0002-9625-6216}},
Unique-ID = {{ISI:000295966900071}},
}

@article{ ISI:000294802800035,
Author = {Sidorenkov, Grigory and Haaijer-Ruskamp, Flora M. and de Zeeuw, Dick and
   Denig, Petra},
Title = {{A Longitudinal Study Examining Adherence to Guidelines in Diabetes Care
   According to Different Definitions of Adequacy and Timeliness}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{9}},
Month = {{SEP 8}},
Abstract = {{Background: Performance indicators assessing quality of diabetes care
   often look at single processes, e. g. whether an HbA1c test was
   conducted. Adequate care, however, consists of consecutive processes
   which should be taken in time (clinical pathways). We assessed quality
   of diabetes care by looking at single processes versus clinical
   pathways. In addition, we evaluated the impact of time period
   definitions on this quality assessment.
   Methodology: We conducted a cohort study in 2007-2008 using the GIANTT
   (Groningen Initiative to Analyse type 2 diabetes Treatment) database.
   Proportions of patients adequately managed for HbA1c, systolic blood
   pressure (SBP), LDL-cholesterol (LDL-C), and albumin/creatinin ratio
   (ACR) were calculated for the pathway of (1) risk factor level testing,
   (2) treatment intensification when indicated, (3) response to treatment
   evaluation. Strict and wide time periods for each step were defined.
   Proportions of patients adequately managed regarding the overall pathway
   and single steps, using strict or wide time periods were compared using
   odds ratios (OR) with 95\% confidence intervals.
   Findings: Of 11176 patients diagnosed with type 2 diabetes, 9439 with
   complete follow-up were included. The majority received annual
   examination of HbA1c (86\%) and SBP (86\%), whereas this was 67\% for
   LDL-C and 49\% for ACR. Adequate management regarding the three-step
   pathway was observed in 73\%, 53\%, 46\%, 41\% of patients for HbA1c,
   SBP, LDL-C, and ACR respectively. Quality scores reduced significantly
   due to the second step (OR 0.43, 0.18, 0.44, 0.74), but were not much
   further reduced by the third step. Timely treatment evaluation occurred
   in 88\% for HbA1c, 87\% for SBP, 83\% for LDL-C, and 76\% for ACR. The
   overall score was not significantly changed by using strict time
   windows.
   Conclusion: Quality estimates of glycemic, blood pressure and
   cholesterol management are substantially reduced when looking at
   clinical pathways as compared to estimates based on commonly used simple
   process measures.}},
DOI = {{10.1371/journal.pone.0024278}},
Article-Number = {{e24278}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Denig, Petra/L-9865-2014
   de Zeeuw, Dick/E-9080-2014}},
ORCID-Numbers = {{Denig, Petra/0000-0002-7929-4739
   de Zeeuw, Dick/0000-0003-3434-7777}},
Unique-ID = {{ISI:000294802800035}},
}

@article{ ISI:000293729900006,
Author = {Adolfsson, Eva Thors and Rosenblad, Andreas},
Title = {{Reporting systems, reporting rates and completeness of data reported
   from primary healthcare to a Swedish quality register - The National
   Diabetes Register}},
Journal = {{INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS}},
Year = {{2011}},
Volume = {{80}},
Number = {{9}},
Pages = {{663-668}},
Month = {{SEP}},
Abstract = {{Objective: The aims of this paper were to study the reporting rate and
   completeness of data reported from primary healthcare centres (PHCCs) in
   Sweden to the Swedish National Diabetes Register (NDR), with a special
   attention on the relation between these measures and the reporting
   system used by the PHCCs.
   Method: A national survey conducted in Swedish primary healthcare
   covering the year 2006. A questionnaire was used to collect data from
   523 PHCCs. Data on 87,099 adult diabetic patients attending these PHCCs
   and reported to the NDR were obtained from the register. In Sweden,
   participation in the NDR is voluntary. The data were reported through
   the Internet, either online using a web-based system or by direct
   transmission. The main outcome measures were reporting rate and
   completeness of reported data.
   Results: Of the 523 PHCCs, almost two-thirds had reported <75\% of their
   diabetic patients to the NDR. The lowest reporting rate was found among
   the largest PHCCs, while the highest was found among small PHCCs (p <
   0.001). Reasons given for not reporting data to the NDR were lack of
   time and lack of personnel resources. Altogether, 73.1\% of the PHCCs
   reported data to the NDR online using a web-based system, 20.5\% used
   direct transmission and 6.3\% used both systems. The PHCCs that reported
   data through direct transmission systems reported almost 70\% of their
   diabetic patients to the NDR, while PHCCs using web-based systems
   reported 54\% of their diabetic patients to the NDR. Adjusted for other
   factors, using direct transmission increased the reporting rate by 13.0
   percentage points. However, the web-based system contributed to a higher
   completeness of data than the direct transmission system.
   Conclusions: A direct transmission system facilitates a high reporting
   rate to the register at the expense of lower completeness of the
   reported data. (C) 2011 Elsevier Ireland Ltd. All rights reserved.}},
DOI = {{10.1016/j.ijmedinf.2011.06.006}},
ISSN = {{1386-5056}},
ResearcherID-Numbers = {{Rosenblad, Andreas/G-8513-2013}},
ORCID-Numbers = {{Rosenblad, Andreas/0000-0003-3691-8326}},
Unique-ID = {{ISI:000293729900006}},
}

@article{ ISI:000294678300032,
Author = {Faurholt-Jepsen, Daniel and Range, Nyagosya and PrayGod, George and
   Jeremiah, Kidola and Faurholt-Jepsen, Maria and Aabye, Martine Grosos
   and Changalucha, John and Christensen, Dirk Lund and Pipper, Christian
   Bressen and Krarup, Henrik and Witte, Daniel Rinse and Andersen, Aase
   Bengaard and Friis, Henrik},
Title = {{Diabetes Is a Risk Factor for Pulmonary Tuberculosis: A Case-Control
   Study from Mwanza, Tanzania}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{8}},
Month = {{AUG 30}},
Abstract = {{Background: Diabetes and TB are associated, and diabetes is increasingly
   common in low-income countries where tuberculosis (TB) is highly
   endemic. However, the role of diabetes for TB has not been assessed in
   populations where HIV is prevalent.
   Methods: A case-control study was conducted in an urban population in
   Tanzania among culture-confirmed pulmonary TB patients and non-TB
   neighbourhood controls. Participants were tested for diabetes according
   to WHO guidelines and serum concentrations of acute phase reactants were
   measured. The association between diabetes and TB, and the role of HIV
   as an effect modifier, were examined using logistic regression. Since
   blood glucose levels increase during the acute phase response, we
   adjusted for elevated serum acute phase reactants.
   Results: Among 803 cases and 350 controls the mean (SD) age was 34.8
   (11.9) and 33.8 (12.0) years, and the prevalence of diabetes was 16.7\%
   (95\% CI: 14.2; 19.4) and 9.4\% (6.6; 13.0), respectively. Diabetes was
   associated with TB (OR 2.2, 95\% CI: 1.5; 3.4, p < 0.001). However, the
   association depended on HIV status (interaction, p = 0.01) due to a
   stronger association among HIV uninfected (OR 4.2, 95\% CI: 1.5; 11.6, p
   = 0.01) compared to HIV infected (OR 0.1, 95\% CI: 0.01; 1.8, p = 0.13)
   after adjusting for age, sex, demographic factors and elevated serum
   acute phase reactants.
   Conclusion: Diabetes is a risk factor for TB in HIV uninfected, whereas
   the association in HIV infected patients needs further study. The
   increasing diabetes prevalence may be a threat to TB control.}},
DOI = {{10.1371/journal.pone.0024215}},
Article-Number = {{e24215}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Faurholt-Jepsen, Daniel/D-9453-2012
   Witte, Daniel/C-1722-2008
   Friis, Henrik/N-2047-2014
   }},
ORCID-Numbers = {{Faurholt-Jepsen, Daniel/0000-0001-8561-0155
   Witte, Daniel/0000-0002-0769-2922
   Friis, Henrik/0000-0002-2848-2940
   Pipper, Christian Bressen/0000-0003-0261-616X}},
Unique-ID = {{ISI:000294678300032}},
}

@article{ ISI:000293953500004,
Author = {Zemlin, Annalise E. and Matsha, Tandi E. and Hassan, Mogamat S. and
   Erasmus, Rajiv T.},
Title = {{HbA1c of 6.5\% to Diagnose Diabetes Mellitus - Does It Work for Us? -
   The Bellville South Africa Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{8}},
Month = {{AUG 12}},
Abstract = {{Background: HbA1c has been the gold standard for glycaemic control
   follow-up for decades. In 2009, a level of 6.5\% (48 mmol/mol) was
   proposed as diagnostic for diabetes. We test this cut-off in our
   community.
   Methods: Participants (946) from a community-based study were screened
   for diabetes using either a fasting blood glucose or oral glucose
   tolerance test (OFTT). The HbA1c cut-off of 6.5\% was tested for each
   group. A receiver operator characteristic (ROC) curve for both groups
   was generated to establish an optimal cut-off.
   Results: Our study included 224 (23.7\%) males and 722 (76.3\%) females.
   Using fasting blood glucose alone, 117 (14\%) were diagnosed with
   diabetes -250\% had an HbA1c value of >= 6.5\% (48 mmol/mol). Using an
   OGTT, 147 (18\%) were diagnosed with diabetes -246\% had an HbA1c value
   of >= 6.5\% (48 mmol/mol). ROC curves found a level of 6.1\% (43
   mmol/mol) to be optimal in both groups (AUC 0.85 and 0.82 respectively).
   The sensitivities were 80\% and 75\% and the specificities 77\% and 78\%
   respectively.
   Conclusions: A cut off of 6.5\% (48 mmol/mol) is a good diagnostic tool
   with its high specificity; however the low sensitivity limits its use.
   We found a level of 6.1\% (43 mmol/mol) to be optimal. This emphasizes
   the need for evidenced based values to be established in various
   population groups.}},
DOI = {{10.1371/journal.pone.0022558}},
Article-Number = {{e22558}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000293953500004}},
}

@article{ ISI:000293172900037,
Author = {Pettersson, Ulrika Sofia and Christoffersson, Gustaf and Massena, Sara
   and Ahl, David and Jansson, Leif and Henriksnas, Johanna and Phillipson,
   Mia},
Title = {{Increased Recruitment but Impaired Function of Leukocytes during
   Inflammation in Mouse Models of Type 1 and Type 2 Diabetes}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{7}},
Month = {{JUL 25}},
Abstract = {{Background: Patients suffering from diabetes show defective bacterial
   clearance. This study investigates the effects of elevated plasma
   glucose levels during diabetes on leukocyte recruitment and function in
   established models of inflammation.
   Methodology/Principal Findings: Diabetes was induced in C57Bl/6 mice by
   intravenous alloxan (causing severe hyperglycemia), or by high fat diet
   (moderate hyperglycemia). Leukocyte recruitment was studied in
   anaesthetized mice using intravital microscopy of exposed cremaster
   muscles, where numbers of rolling, adherent and emigrated leukocytes
   were quantified before and during exposure to the inflammatory chemokine
   MIP-2 (0.5 nM). During basal conditions, prior to addition of chemokine,
   the adherent and emigrated leukocytes were increased in both alloxan-(62
   +/- 18\% and 85 +/- 21\%, respectively) and high fat diet-induced (77
   +/- 25\% and 86 +/- 17\%, respectively) diabetes compared to control
   mice. MIP-2 induced leukocyte emigration in all groups, albeit
   significantly more cells emigrated in alloxan-treated mice (15.3 +/-
   1.0) compared to control (8.0 +/- 1.1) mice. Bacterial clearance was
   followed for 10 days after subcutaneous injection of bioluminescent S.
   aureus using non-invasive IVIS imaging, and the inflammatory response
   was assessed by Myeloperoxidase-ELISA and confocal imaging. The
   phagocytic ability of leukocytes was assessed using LPS-coated
   fluorescent beads and flow cytometry. Despite efficient leukocyte
   recruitment, alloxan-treated mice demonstrated an impaired ability to
   clear bacterial infection, which we found correlated to a 50\% decreased
   phagocytic ability of leukocytes in diabetic mice.
   Conclusions/Significance: These results indicate that reduced ability to
   clear bacterial infections observed during experimentally induced
   diabetes is not due to reduced leukocyte recruitment since sustained
   hyperglycemia results in increased levels of adherent and emigrated
   leukocytes in mouse models of type 1 and type 2 diabetes. Instead,
   decreased phagocytic ability observed for leukocytes isolated from
   diabetic mice might account for the impaired bacterial clearance.}},
DOI = {{10.1371/journal.pone.0022480}},
Article-Number = {{e22480}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Christoffersson, Gustaf/I-3756-2012
   }},
ORCID-Numbers = {{Christoffersson, Gustaf/0000-0002-9640-9702}},
Unique-ID = {{ISI:000293172900037}},
}

@article{ ISI:000293097300039,
Author = {Cui, Bin and Zhu, Xiaolin and Xu, Min and Guo, Ting and Zhu, Dalong and
   Chen, Gang and Li, Xuejun and Xu, Lingyan and Bi, Yufang and Chen,
   Yuhong and Xu, Yu and Li, Xiaoying and Wang, Weiqing and Wang, Haifeng
   and Huang, Wei and Ning, Guang},
Title = {{A Genome-Wide Association Study Confirms Previously Reported Loci for
   Type 2 Diabetes in Han Chinese}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{7}},
Month = {{JUL 22}},
Abstract = {{Background: Genome-wide association study (GWAS) has identified more
   than 30 loci associated with type 2 diabetes (T2D) in Caucasians.
   However, genomic understanding of T2D in Asians, especially Han Chinese,
   is still limited.
   Methods and Principal Findings: A two-stage GWAS was performed in Han
   Chinese from Mainland China. The discovery stage included 793 T2D cases
   and 806 healthy controls genotyped using Illumina Human 660- and
   610-Quad BeadChips; and the replication stage included two independent
   case-control populations (a total of 4445 T2D cases and 4458 controls)
   genotyped using TaqMan assay. We validated the associations of KCNQ1
   (rs163182, p = 2.085 x 10(-17), OR 1.28) and C2CD4A/B (rs1370176, p =
   3.677 x 10(-4), OR 1.124; rs1436953, p = 7.753 x 10(-6), OR 1.141;
   rs7172432, p = 4.001 x 10(-5), OR 1.134) in Han Chinese.
   Conclusions and Significance: Our study represents the first GWAS of T2D
   with both discovery and replication sample sets recruited from Han
   Chinese men and women residing in Mainland China. We confirmed the
   associations of KCNQ1 and C2CD4A/B with T2D, with the latter for the
   first time being examined in Han Chinese. Arguably, eight more
   independent loci were replicated in our GWAS.}},
DOI = {{10.1371/journal.pone.0022353}},
Article-Number = {{e22353}},
ISSN = {{1932-6203}},
ORCID-Numbers = {{Zhu, Xiaolin/0000-0002-3221-595X}},
Unique-ID = {{ISI:000293097300039}},
}

@article{ ISI:000295252700009,
Author = {Loots, Du Toit and Pieters, Marlien and Islam, Md Shahidul and Botes,
   Lisa},
Title = {{Antidiabetic effects of Aloe ferox and Aloe greatheadii var. davyana
   leaf gel extracts in a low-dose streptozotocin diabetes rat model}},
Journal = {{SOUTH AFRICAN JOURNAL OF SCIENCE}},
Year = {{2011}},
Volume = {{107}},
Number = {{7-8}},
Pages = {{43-48}},
Month = {{JUL-AUG}},
Abstract = {{The medicinal use and commercialisation of the plants Aloe ferox and
   Aloe greatheadii are primarily based on research done on Aloe vera and
   Aloe arborescens. Consequently, in this study we investigated the
   possible antidiabetic effects of ethanol extracts of A. ferox and A.
   greatheadii var. davyana leaf gel in a streptozotocin (STZ)-induced type
   2 diabetes rat model. Fifty male Wistar rats, weighing 200 g 250 g, were
   randomly divided into five groups of n =10: normal control rats,
   diabetic control rats, diabetic rats receiving A. ferox leaf gel extract
   (300 mg/kg), diabetic rats receiving A. greatheadii leaf gel extract
   (300 mg/kg), and diabetic rats receiving glibenclamide (600 mu g/kg).
   Diabetes was induced by a single intraperitoneal injection of STZ (40
   mg/kg). Rats were sacrificed 5 weeks after injection, following a
   12-hour fast, and blood and tissue samples were collected. Compared to
   the normal control group, STZ significantly increased relative liver and
   kidney weights, end-point plasma glucose, fructosamine, oxidative
   stress, liver enzymes, total cholesterol (TC), triglycerides, very low
   density lipoprotein-cholesterol and TC: high density
   lipoprotein-cholesterol (HDL-C) values and reduced serum insulin levels.
   Treatment with A. greatheadii moderately increased serum insulin and
   HDL-C levels and moderately reduced end-point plasma glucose and liver
   alkaline phosphatase (ALP) and significantly decreased TC:HDL-C ratios.
   A. ferox supplementation similarly resulted in moderately increased
   serum insulin, accompanied by slight corrections in ALP and HDL-C,
   without any change to end-point plasma glucose values. A. greatheadii
   and, to a lesser extent, A. ferox, resulted in a clinically relevant
   improved diabetic state (indicated by moderate to high effect sizes),
   suggesting that these Aloe species may show promise for treating
   diabetes.}},
DOI = {{10.4102/sajs.v107i7/8.532}},
ISSN = {{0038-2353}},
EISSN = {{1996-7489}},
Unique-ID = {{ISI:000295252700009}},
}

@article{ ISI:000289047700046,
Author = {Calisir, Duygu and Dogantekin, Esin},
Title = {{An automatic diabetes diagnosis system based on LDA-Wavelet Support
   Vector Machine Classifier}},
Journal = {{EXPERT SYSTEMS WITH APPLICATIONS}},
Year = {{2011}},
Volume = {{38}},
Number = {{7}},
Pages = {{8311-8315}},
Month = {{JUL}},
Abstract = {{In this paper, an automatic diagnosis system for diabetes on Linear
   Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine
   Classifier: LDA-MWSVM is introduced. The structure of this automatic
   system based on LDA-MWSVM for the diagnosis of diabetes is composed of
   three stages: The feature extraction and feature reduction stage by
   using the Linear Discriminant Analysis (LDA) method and the
   classification stage by using Morlet Wavelet Support Vector Machine
   (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used
   to separate features variables between healthy and patient (diabetes)
   data in the first stage. The healthy and patient (diabetes) features
   obtained in the first stage are given to inputs of the MWSVM classifier
   in the second stage. Finally, in the third stage, the correct diagnosis
   performance of this automatic system based on LDA-MWSVM for the
   diagnosis of diabetes is calculated by using sensitivity and specificity
   analysis, classification accuracy, and confusion matrix, respectively.
   The classification accuracy of this system was obtained at about
   89.74\%. (C) 2011 Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.eswa.2011.01.017}},
ISSN = {{0957-4174}},
Unique-ID = {{ISI:000289047700046}},
}

@article{ ISI:000292092600032,
Author = {Chang, Chia-Hsuin and Toh, Sengwee and Lin, Jou-Wei and Chen, Shu-Ting
   and Kuo, Chuei-Wen and Chuang, Lee-Ming and Lai, Mei-Shu},
Title = {{Cancer Risk Associated with Insulin Glargine among Adult Type 2 Diabetes
   Patients - A Nationwide Cohort Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{6}},
Month = {{JUN 27}},
Abstract = {{Background: Preclinical and observational studies raise the concern
   about the safety of insulin glargine in terms of cancer initiation and
   promotion. This study is designed to examine cancer incidence associated
   with use of insulin glargine vs. intermediate/long-acting human insulin
   (HI).
   Methodology: A retrospective cohort study using the Taiwan National
   Health Insurance claims database was conducted to identify adult
   patients with type 2 diabetes mellitus and without a history of cancer
   who initiated insulin glargine (n = 10,190) or intermediate/long-acting
   HI (n = 49,253) during 2004-2007. Exclusive users were followed from the
   date of insulin initiation to the earliest of cancer diagnosis, death,
   disenrollment, or December 31 2007. We estimated adjusted hazard ratios
   and 95\% confidence intervals (CIs) with Cox proportional hazards models
   adjusting for baseline propensity score.
   Findings: The incidence rate of all cancer per 1,000 person-years was
   13.8 for insulin glargine initiators (179 cases) and 16.0 for
   intermediate/long-acting HI initiators (1,445 cases) during an average
   follow-up of 2 years. No significant difference in overall cancer risk
   between insulin glargine initiators and HI initiators was found. For
   men, however, the adjusted hazard ratio of insulin glargine use as
   compared with intermediate/long-acting HI was 2.15 (95\% CI 1.01-4.59)
   for pancreatic cancer, and 2.42 (95\% CI 1.50-8.40) for prostate cancer.
   The increased risk was not observed among women.
   Conclusions: Insulin glargine use did not increase the risk of overall
   cancer incidence as compared with HI. The positive associations with
   pancreatic and prostate cancer need further evaluation and validation.}},
DOI = {{10.1371/journal.pone.0021368}},
Article-Number = {{e21368}},
ISSN = {{1932-6203}},
ORCID-Numbers = {{Lai, Mei-shu/0000-0001-6130-3468
   CHUANG, LEE-MING/0000-0003-0978-2662
   CHANG, CHIA-HSUIN/0000-0003-0024-6177}},
Unique-ID = {{ISI:000292092600032}},
}

@article{ ISI:000291734100066,
Author = {Sanghera, Dharambir K. and Been, Latonya F. and Ralhan, Sarju and
   Wander, Gurpreet S. and Mehra, Narinder K. and Singh, Jai Rup and
   Ferrell, Robert E. and Kamboh, Mohammed I. and Aston, Christopher E.},
Title = {{Genome-Wide Linkage Scan to Identify Loci Associated with Type 2
   Diabetes and Blood Lipid Phenotypes in the Sikh Diabetes Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{6}},
Month = {{JUN 16}},
Abstract = {{In this investigation, we have carried out an autosomal genome-wide
   linkage analysis to map genes associated with type 2 diabetes (T2D) and
   five quantitative traits of blood lipids including total cholesterol,
   high-density lipoprotein (HDL) cholesterol, low-density lipoprotein
   (LDL) cholesterol, very low-density lipoprotein (VLDL) cholesterol, and
   triglycerides in a unique family-based cohort from the Sikh Diabetes
   Study (SDS). A total of 870 individuals (526 male/344 female) from 321
   families were successfully genotyped using 398 polymorphic
   microsatellite markers with an average spacing of 9.26 cM on the
   autosomes. Results of non-parametric multipoint linkage analysis using
   Sall statistics (implemented in Merlin) did not reveal any chromosomal
   region to be significantly associated with T2D in this Sikh cohort.
   However, linkage analysis for lipid traits using QTL-ALL analysis
   revealed promising linkage signals with p <= 0.005 for total
   cholesterol, LDL cholesterol, and HDL cholesterol at chromosomes 5p15,
   9q21, 10p11, 10q21, and 22q13. The most significant signal (p = 0.0011)
   occurred at 10q21.2 for HDL cholesterol. We also observed linkage
   signals for total cholesterol at 22q13.32 (p = 0.0016) and 5p15.33 (p =
   0.0031) and for LDL cholesterol at 10p11.23 (p = 0.0045). Interestingly,
   some of linkage regions identified in this Sikh population coincide with
   plausible candidate genes reported in recent genome-wide association and
   meta-analysis studies for lipid traits. Our study provides the first
   evidence of linkage for loci associated with quantitative lipid traits
   at four chromosomal regions in this Asian Indian population from Punjab.
   More detailed examination of these regions with more informative
   genotyping, sequencing, and functional studies should lead to rapid
   detection of novel targets of therapeutic importance.}},
DOI = {{10.1371/journal.pone.0021188}},
Article-Number = {{e21188}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000291734100066}},
}

@article{ ISI:000291356400001,
Author = {Herder, Christian and Baumert, Jens and Zierer, Astrid and Roden,
   Michael and Meisinger, Christa and Karakas, Mahir and Chambless, Lloyd
   and Rathmann, Wolfgang and Peters, Annette and Koenig, Wolfgang and
   Thorand, Barbara},
Title = {{Immunological and Cardiometabolic Risk Factors in the Prediction of Type
   2 Diabetes and Coronary Events: MONICA/KORA Augsburg Case-Cohort Study}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{6}},
Month = {{JUN 6}},
Abstract = {{Background: This study compares inflammation-related biomarkers with
   established cardiometabolic risk factors in the prediction of incident
   type 2 diabetes and incident coronary events in a prospective
   case-cohort study within the population-based MONICA/KORA Augsburg
   cohort.
   Methods and Findings: Analyses for type 2 diabetes are based on 436
   individuals with and 1410 individuals without incident diabetes.
   Analyses for coronary events are based on 314 individuals with and 1659
   individuals without incident coronary events. Mean follow-up times were
   almost 11 years. Areas under the receiver-operating characteristic curve
   (AUC), changes in Akaike's information criterion (Delta AIC), integrated
   discrimination improvement (IDI) and net reclassification index (NRI)
   were calculated for different models. A basic model consisting of age,
   sex and survey predicted type 2 diabetes with an AUC of 0.690. Addition
   of 13 inflammation-related biomarkers (CRP, IL-6, IL-18, MIF,
   MCP-1/CCL2, IL-8/CXCL8, IP-10/CXCL10, adiponectin, leptin, RANTES/CCL5,
   TGF-beta 1, sE-selectin, sICAM-1; all measured in nonfasting serum)
   increased the AUC to 0.801, whereas addition of cardiometabolic risk
   factors (BMI, systolic blood pressure, ratio total/HDL-cholesterol,
   smoking, alcohol, physical activity, parental diabetes) increased the
   AUC to 0.803 (Delta AUC {[}95\% CI] 0.111 {[}0.092-0.149] and 0.113
   {[}0.093-0.149], respectively, compared to the basic model). The
   combination of all inflammation-related biomarkers and cardiometabolic
   risk factors yielded a further increase in AUC to 0.847 (Delta AUC
   {[}95\% CI] 0.044 {[}0.028-0.066] compared to the cardiometabolic risk
   model). Corresponding AUCs for incident coronary events were 0.807,
   0.825 (Delta AUC {[}95\% CI] 0.018 {[}0.013-0.038] compared to the basic
   model), 0.845 (Delta AUC {[}95\% CI] 0.038 {[}0.028-0.059] compared to
   the basic model) and 0.851 (Delta AUC {[}95\% CI] 0.006 {[}0.003-0.021]
   compared to the cardiometabolic risk model), respectively.
   Conclusions: Inclusion of multiple inflammation-related biomarkers into
   a basic model and into a model including cardiometabolic risk factors
   significantly improved the prediction of type 2 diabetes and coronary
   events, although the improvement was less pronounced for the latter
   endpoint.}},
DOI = {{10.1371/journal.pone.0019852}},
Article-Number = {{e19852}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Meisinger, Christine/B-5358-2014
   Thorand, Barbara/B-5349-2014
   Peters, Annette/A-6117-2011}},
ORCID-Numbers = {{Thorand, Barbara/0000-0002-8416-6440
   }},
Unique-ID = {{ISI:000291356400001}},
}

@article{ ISI:000291310600008,
Author = {Lim, Soo and Yoon, Ji Won and Kang, Seon Mee and Choi, Sung Hee and Cho,
   Bong Jun and Kim, Min and Park, Ho Seon and Cho, Hyun Ju and Shin,
   Hayley and Kim, Young-Bum and Kim, Hyo Soo and Jang, Hak Chul and Park,
   Kyong Soo},
Title = {{EGb761, a Ginkgo Biloba Extract, Is Effective Against Atherosclerosis In
   Vitro, and in a Rat Model of Type 2 Diabetes}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{6}},
Month = {{JUN 2}},
Abstract = {{Background: EGb761, a standardized Ginkgo biloba extract, has
   antioxidant and antiplatelet aggregation and thus might protect against
   atherosclerosis. However, molecular and functional properties of EGb761
   and its major subcomponents have not been well characterized. We
   investigated the effect of EGb761 and its major subcomponents
   (bilobalide, kaemferol, and quercetin) on preventing atherosclerosis in
   vitro, and in a rat model of type 2 diabetes.
   Methods and Results: EGb761 (100 and 200 mg/kg) or normal saline
   (control) were administered to Otsuka Long-Evans Tokushima Fatty rats,
   an obese insulin-resistant rat model, for 6 weeks (from 3 weeks before
   to 3 weeks after carotid artery injury). Immunohistochemical staining
   was performed to investigate cell proliferation and apoptosis in the
   injured arteries. Cell migration, caspase-3 activity and DNA
   fragmentation, monocyte adhesion, and ICAM-1/VCAM-1 levels were explored
   in vitro. Treatment with EGb761 dose-dependently reduced intima-media
   ratio, proliferation of vascular smooth muscle cells (VSMCs) and induced
   greater apoptosis than the controls. Proliferation and migration of
   VSMCs in vitro were also decreased by the treatment of EGb761. Glucose
   homeostasis and circulating adiponectin levels were improved, and plasma
   hsCRP concentrations were decreased in the treatment groups. Caspase-3
   activity and DNA fragmentation increased while monocyte adhesion and
   ICAM-1/VCAM-1 levels decreased significantly. Among subcomponents of
   EGb761, kaemferol and quercetin reduced VSMC migration and increased
   caspase activity.
   Conclusions: EGb761 has a protective role in the development of
   atherosclerosis and is a potential therapeutic agent for preventing
   atherosclerosis.}},
DOI = {{10.1371/journal.pone.0020301}},
Article-Number = {{e20301}},
ISSN = {{1932-6203}},
ResearcherID-Numbers = {{Choi, Sung-hee/J-5689-2012
   Jang, Hak Chul/D-9637-2012
   Park, Kyong Soo/C-2265-2008
   }},
ORCID-Numbers = {{Park, Kyong Soo/0000-0003-3597-342X
   CHOI, SUNG HEE/0000-0003-0740-8116}},
Unique-ID = {{ISI:000291310600008}},
}

@article{ ISI:000292178600006,
Author = {Basciftci, Fatih and Hatay, Omer Faruk},
Title = {{Reduced-rule based expert system by the simplification of logic
   functions for the diagnosis of diabetes}},
Journal = {{COMPUTERS IN BIOLOGY AND MEDICINE}},
Year = {{2011}},
Volume = {{41}},
Number = {{6}},
Pages = {{350-356}},
Month = {{JUN}},
Abstract = {{Diabetes is a chronic and complex endocrine disease with metabolic
   disorders caused by the imbalance between secreted and needed insulin
   levels. In the present study, all probabilities were examined
   considering ten (10) indications (2(10)=1024 different cases) of
   diabetes using medical expert system (MES), and accordingly, a truth
   table was established. This table was simplified by the simplification
   method of logic functions. 15 main rules were obtained using
   minimization method for Boolean Functions (BFs). These rules established
   rules base of ES. Subsequently, the parameters of 768 patients were
   compared with this ES. Accuracy rate of the estimations of MES, created
   as a result these comparisons, was determined as 97.13\% in diabetes
   patients, 97\% in non-diabetes patients, 96.5\% in Type 1 patients,
   98.26\% in Type 2 patients and 97.44\% in pregnant patients. (C) 2011
   Elsevier Ltd. All rights reserved.}},
DOI = {{10.1016/j.compbiomed.2011.03.012}},
ISSN = {{0010-4825}},
Unique-ID = {{ISI:000292178600006}},
}

@article{ ISI:000290440200001,
Author = {Khadra, Anmar and Pietropaolo, Massimo and Nepom, Gerald T. and Sherman,
   Arthur},
Title = {{Investigating the Role of T-Cell Avidity and Killing Efficacy in
   Relation to Type 1 Diabetes Prediction}},
Journal = {{PLOS ONE}},
Year = {{2011}},
Volume = {{6}},
Number = {{5}},
Month = {{MAY 10}},
Abstract = {{During the progression of the clinical onset of Type 1 Diabetes (T1D),
   high-risk individuals exhibit multiple islet autoantibodies and
   high-avidity T cells which progressively destroy beta cells causing
   overt T1D. In particular, novel autoantibodies, such as those against
   IA-2 epitopes (aa1-577), had a predictive rate of 100\% in a 10-year
   follow up (rapid progressors), unlike conventional autoantibodies that
   required 15 years of follow up for a 74\% predictive rate (slow
   progressors). The discrepancy between these two groups is thought to be
   associated with T-cell avidity, including CD8(+) and/or CD4(+) T cells.
   For this purpose, we build a series of mathematical models incorporating
   first one clone then multiple clones of islet-specific and pathogenic
   CD8(+) and/or CD4(+) T cells, together with B lymphocytes, to
   investigate the interaction of T-cell avidity with autoantibodies in
   predicting disease onset. These models are instrumental in examining
   several experimental observations associated with T-cell avidity,
   including the phenomenon of avidity maturation (increased average T-cell
   avidity over time), based on intra-and cross-clonal competition between
   T cells in high-risk human subjects. The model shows that the level and
   persistence of autoantibodies depends not only on the avidity of T
   cells, but also on the killing efficacy of these cells. Quantification
   and modeling of autoreactive T-cell avidities can thus determine the
   level of risk associated with each type of autoantibodies and the timing
   of T1D disease onset in individuals that have been tested positive for
   these autoantibodies. Such studies may lead to early diagnosis of the
   disease in high-risk individuals and thus potentially serve as a means
   of staging patients for clinical trials of preventive or interventional
   therapies far before disease onset.}},
DOI = {{10.1371/journal.pone.0014796}},
Article-Number = {{e14796}},
ISSN = {{1932-6203}},
Unique-ID = {{ISI:000290440200001}},
}

@article{ ISI:000291188900008,
Author = {Chase, J. Geoffrey and Le Compte, Aaron J. and Suhaimi, Fatanah and
   Shaw, Geoffrey M. and Lynn, Adrienne and Lin, Jessica and Pretty,
   Christopher G. and Razak, Normy and Parente, Jacquelyn D. and Hann,
   Christopher E. and Preiser, Jean-Charles and Desaive, Thomas},
Title = {{Tight glycemic control in critical care - The leading role of insulin
   sensitivity and patient variability: A review and model-based analysis}},
Journal = {{COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}},
Year = {{2011}},
Volume = {{102}},
Number = {{2, SI}},
Pages = {{156-171}},
Month = {{MAY}},
Note = {{7th International Federation of Automatic Control Symposium on Modelling
   and Control in Biomedical Systems, Aalborg, DENMARK, 2009}},
Abstract = {{Tight glycemic control (TGC) has emerged as a major research focus in
   critical care due to its potential to simultaneously reduce both
   mortality and costs. However, repeating initial successful TGC trials
   that reduced mortality and other outcomes has proven difficult with more
   failures than successes. Hence, there has been growing debate over the
   necessity of TGC, its goals, the risk of severe hypoglycemia, and target
   cohorts.
   This paper provides a review of TGC via new analyses of data from
   several clinical trials, including SPRINT, Glucontrol and a recent NICU
   study. It thus provides both a review of the problem and major
   background factors driving it, as well as a novel model-based analysis
   designed to examine these dynamics from a new perspective. Using these
   clinical results and analysis, the goal is to develop new insights that
   shed greater light on the leading factors that make TGC difficult and
   inconsistent, as well as the requirements they thus impose on the design
   and implementation of TGC protocols.
   A model-based analysis of insulin sensitivity using data from three
   different critical care units, comprising over 75,000 h of clinical
   data, is used to analyse variability in metabolic dynamics using a
   clinically validated model-based insulin sensitivity metric (S(I)).
   Variation in S(I) provides a new interpretation and explanation for the
   variable results seen (across cohorts and studies) in applying TGC. In
   particular, significant intra- and inter-patient variability in insulin
   resistance (1/S(I)) is seen be a major confounder that makes TGC
   difficult over diverse cohorts, yielding variable results over many
   published studies and protocols. Further factors that exacerbate this
   variability in glycemic outcome are found to include measurement
   frequency and whether a protocol is blind to carbohydrate
   administration. (C) 2010 Published by Elsevier Ireland Ltd.}},
DOI = {{10.1016/j.cmpb.2010.11.006}},
ISSN = {{0169-2607}},
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:000291188900008}},
}

@article{ ISI:000291188900012,
Author = {Salzsieder, E. and Vogt, L. and Kohnert, K. -D. and Heinke, P. and
   Augstein, P.},
Title = {{Model-based Decision support in Diabetes Care}},
Journal = {{COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}},
Year = {{2011}},
Volume = {{102}},
Number = {{2, SI}},
Pages = {{206-218}},
Month = {{MAY}},
Note = {{7th International Federation of Automatic Control Symposium on Modelling
   and Control in Biomedical Systems, Aalborg, DENMARK, 2009}},
Abstract = {{The model-based Karlsburg Diabetes Management System (KADIS (R)) has

repository: newbiettn/paper-review

repo_name: newbiettn/paper-review