path: root/gnqa/paper1_eval/src/data/datasets/human/dataset_citizenscientist_diabetes_3.json

{
  "question": [
    "Does cycling reduce risk of diabetes?",
    "How can GeneNetwork assist in identifying genetic factors involved in diabetes?",
    "What specific tools within GeneNetwork are most useful for diabetes research, and how are they applied?",
    "What role does insulin play in the regulation of blood glucose levels?",
    " How does aging affect the risk of developing type 2 diabetes?"
  ],
  "answer": [
    "Yes, the text mentions that in the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity, which primarily included cycling and walking, had a 34% lower odds of developing diabetes over 6 years compared to the least active.",
    "GeneNetwork can assist in identifying genetic factors involved in diabetes by integrating comprehensive genetic information from Genome-wide association studies (GWAS) and Single Nucleotide Polymorphisms (SNP's) related to Type II Diabetes (T2D). It constructs a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. This network can help identify highly interconnected network",
    "GeneNetwork provides a powerful statistical platform for online network analyses and mapping. It allows researchers to model causal networks that link DNA differences to traits such as differences in expression, cell number, volumes, and behavior. This can be particularly useful in diabetes research for identifying genes with disease relevance and exploring their functional connections. Tools like DAVID and GARNET can be used within GeneNetwork to search for enriched KEGG pathways and identify enriched Gene Ontology categories. Additionally, GeneNetwork allows for the construction of protein-protein interaction networks, which can be useful in understanding the complex interactions involved in diabetes.",
    "Insulin plays a crucial role in the regulation of blood glucose levels. It is released by pancreatic -cells in response to increased glucose levels. Insulin facilitates the uptake of glucose, amino acids, and fatty acids by insulin-sensitive tissues, such as muscle and adipose tissue. It also suppresses the production of glucose in the liver. If insulin resistance is present,  cells increase insulin output to maintain normal glucose tolerance. However, if  cells are incapable of this task, plasma concentrations of glucose increase.",
    "Aging significantly increases the risk of developing type 2 diabetes. This is due to factors such as progressive insulin resistance, inactivity, and weight gain that often accompany advancing age. The risk of onset of type 2 diabetes increases exponentially with age, particularly after the age of 40. More than one in four Americans over the age of 65 years have diabetes. Aging is also associated with a redistribution of fat mass, which can lead to insulin resistance, a key factor in the development of type 2 diabetes."
  ],
  "contexts": [
    [
      "Physical ActivityIn the Da Qing Impaired Glucose Tolerance and Diabetes Study, incident diabetes decreased by 46% in the exercise group [103].In the nonrandomized Malm Feasibility Study in 260 middle-aged men with impaired glucose tolerance, the incidence of diabetes was 50% lower in the intervention group after 5 years [104].In the Finnish Diabetes Prevention Study, subjects with a change in moderate-to-vigorous leisure-time physical activity (LTPA) in the highest tertile were 49% to 65% less likely to develop diabetes than those in the lowest tertile [105].In the Coronary Artery Risk Development in Young Adults study (CARDIA) with over 15 years of follow-up, there was a significant 17% reduction of risk of incident hypertension for every 300-exercise unit increment in average physical activity [106].In the Atherosclerosis Risk in Communities (ARIC) study, the highest quartile of leisure activity (primarily cycling and walking) had a 34% lower odds of developing hypertension over 6 years compared to the least active [107].Thus, physical activity reduces the risk of developing diabetes and hypertension.The mechanism involves changes in body weight and glucose tolerance, as well as other factors [107].",
      "Conclusion:In this cohort of men with diabetes, low fitness level was associated with increased risk of CVD mortality within normal weight, overweight, and class 1 obese weight categories.",
      "In aggregate, these findings from the FHS make several important points.First, the incidence rate of diabetes is increasing.Second, because the relative risk of diabetes as a CVD risk factor has remained constant over time, the relative importance of diabetes with respect to CVD has increased.Finally, individuals with diabetes remain inadequately managed with regard to CVD risk factor levels.These findings highlight the importance of early identification of diabetes and a means to identify diabetes early in the life course to promote the early aggressive management of CVD risk factors.Another major remaining question is why the relative risk for diabetes as a CVD risk factor has failed to decrease over time.As described earlier, the rates of CVD among participants in the FHS have decreased; but this reduction has been outpaced by those without diabetes (Fox et al. 2004a).In terms of primary prevention, we can aim to reduce the burden of uncontrolled CVD risk factors, including incompletely treated hypertension, dyslipidemia, and participants with diabetes who continue to smoke (Preis et al. 2009a).Observational studies such as the FHS can help to explore rates of treatment and control for known modifiable risk factors.",
      "Physical activityNumerous epidemiologic studies show that increased physical activity reduces risk of diabetes, whereas sedentary behaviors increase risk.In the NHS (26), each 2-h/day increment of time spent watching television (TV) was associated with a 14% increase in diabetes risk.Each 2-h/day increment of standing or walking around at home was associated with a 12% reduction in risk.Each 1-h/day increment of brisk walking was associated with a 34% reduction in risk (Fig. 3).These results indicate a continuum in the relationship between physical activity levels and diabetes risk.Among sedentary behaviors (TV watching, sitting at work, and other sitting), prolonged TV watching was associated with the highest risk.PREVENTABILITY OF TYPESeveral randomized clinical trials have demonstrated that diabetes is preventable.One of the first diabetes prevention trials was conducted in Daqing, China (58).After 6 years of active intervention, risk was reduced by 31, 46, and 42% in the diet-only, exercise-only, and diet-plus-exercise groups, respectively, compared with the control group.In a subsequent 14-year follow-up study, the intervention groups were combined and compared with control subjects to assess how long the benefits of lifestyle change can extend beyond the period of active intervention (59).Compared with control subjects, individuals in the combined lifestyle intervention group had a 51% lower risk of diabetes during the active intervention period, and a 43% lower risk over a 20-year follow-up.DietExcessive caloric intake is a major driving force behind escalating obesity and type 2 diabetes epidemics worldwide, but diet quality also has independent effects.In the Nurses' Health Study (NHS), we found that the quality of fats and carbohydrates play an important role in the development of diabetes, independent of BMI and other risk factors (11).In particular, higher dietary glycemic load (GL) and trans fat are associated with increased diabetes risk, whereas greater consumption of cereal fiber and polyunsaturated fat is associated with decreased risk (Fig. 2).In a meta-analysis, we found that a 2 serving/day increment in whole-grain intake was associated with a 21% lower risk of diabetes (12).",
      "Evidence from randomized controlled trailsThe effi cacy of lifestyle changes in obesity and T2DM prevention has been established in numerous randomized controlled trails (RCTs).Several of them may, however, be considered of major importance due to their large sample sizes (i.e., 458-3234 individuals) and long-term duration (i.e., 3-6 years).The Chinese Da Qing diabetes prevention study was the fi rst to investigate the eff ect of 6-year lifestyle change on body weight and diabetes incidence in individuals with impaired glucose tolerance (IGT) ( Pan et al., 1997 ).Pan and co-workers (1997) reported 42 % reduction in diabetes incidence, although no signifi cant diff erence in body weight was present.Similar results were found in the Finnish Diabetes Prevention Study (DPS) and the US Diabetes Prevention Program (DPP).DPS and DPP independently reported reduction in diabetes incidence of 58 % accompanied by significant reduction in body weight (5-7 %) as a result of the lifestyle modifi cation ( Knowler et al., 2002 ;Tuomilehto et al., 2001 ).These fi ndings were also confi rmed in Japanese and Indian populations, reporting 67.4 % and 28.5 % reduction in diabetes incidence, respectively ( Kosaka et 2011) reported signifi cant reduction in body weight and diabetes incidence at 1, as well as, at 3 years during a lifestyle modifi cation program carried out in a primary healthcare setting among subjects with IGT.All large-scale interventions have been successful in preventing T2DM during the active intervention period.Remarkably when the eff ectiveness of the lifestyle modifi cation programs was assessed on the long-term after discontinuation of the intervention, diabetes risk still remained substantially reduced.In the Finnish DPS, for instance, at extended follow-up 3 years after the 4-year intervention period a substantial reduction in body weight and T2DM incidence was still present ( Lindstrom et al., This document was downloaded for personal use only.Unauthorized distribution is strictly prohibited.al., 2002 ;Kosaka et al., 2005 ;Lindstrom et al., 2003 ;Tuomilehto et al., 2001 ).In some studies although no or just minor weight loss was achieved, diabetes incidence was also reduced( Pan etal., 1997 ; Ramachandran et al., 2006 ).In addition, on the long term weight was partially or totally regained in all of the studies ( Knowler et al., 2009 ; Li et al., 2008 ; Lindstrom et al., 2006 ; Lindstrom et al., 2003 ).Despite this regain T2DM risk remained low or decreased further, thus the eff ect of lifestyle is unlikely to be solely due to body weight reduction.In support of this notion Pan et al. (1997) reported comparable decrease in T2DM incidence in the intervention group of Da Qing among overweight and lean individuals.",
      "Epidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure.Physical Activity and T2D RiskTraining studies show aerobic exercise enhances insulin action [43] and glucose metabolism [44] in healthy individuals and those at high risk of T2D.Exercise often normalizes plasma glucose levels by improving insulin sensitivity and glucose transportation [45].Exercise can also improve endothelial function, reduce inflammation, and beneficially affect the autonomic nervous system [46].Even in the absence of weight loss, exercise can enhance insulin sensitivity [9] and glycemic control [47].These findings are particularly relevant as they show regular exercise can be used effectively as a treatment for preventing T2D from developing in individuals with IFG/IGT and for improving insulin action in people with manifest diabetes.",
      "Previous studies of physical activity and risk of diabetes have been predom inantly cross-sectional investigations in high-risk populations.Indirect evidence from descriptive comparisons of NIDDM prevalence in rural vs urban populations in Western Samoa1112 and the South Pa cific12 have supported the hypothesis that higher levels of physical activity may be protective against NIDDM.However, other aspects of urban living, including differences in diet, could have accounted for the variation in diabetes risk.Crosssectional studies among Polynesians,13 Melanesian and Indian Fijians,1415 Mi- cronesians,15 Swedes,16 and Mauritians17 have also proposed an association of physical activity with reduced preva lence of NIDDM.The absence of an as sociation between physical activity and glucose intolerance, however, also has been observed.3334In one retrospective study, a reduced risk of diabetes was observed among women who engaged in regular sports in college compared with those who did not, but obesity was not controlled in the analysis.18To our knowledge, only two previous prospec tive studies of physical activity and in cidence of NIDDM have been reported, both supporting a protective effect of exercise.1920Our results in male physi cians are similar to our earlier findings in female nurses,20 suggesting that gen der does not appreciably modify the re lation between physical activity and NIDDM incidence.Objective.\\p=m-\\Toexamine prospectively the association between regular exercise and the subsequent development of non\\p=m-\\insulin-dependent diabetes mellitus (NIDDM).Design.\\p=m-\\Prospective cohort study including 5 years of follow-up.Participants.\\p=m-\\21 271US male physicians participating in the Physicians' Health Study, aged 40 to 84 years and free of diagnosed diabetes mellitus, myo- cardial infarction, cerebrovascular disease, and cancer at baseline.Morbidity follow-up was 99.7% complete.Main Outcome Measure.\\p=m-\\IncidenceofNIDDM.Results.\\p=m-\\Atbaseline, information was obtained about frequency of vigorous exercise and other risk indicators.During 105141 person-years of follow-up, 285 new cases of NIDDM were reported.The age-adjusted incidence of NIDDM ranged from 369 cases per 100 000 person-years in men who engaged in vigorous exer- cise less than once weekly to 214 cases per 100000 person-years in those exer- cising at least five times per week (P, trend, <.001).Men who exercised at least once per week had an age-adjusted relative risk (RR) of NIDDM of 0.64 (95% Cl, 0.51 to 0.82; P=.0003) compared with those who exercised less frequently.The age-adjusted RR of NIDDM decreased with increasing frequency of exercise: 0.77 for once weekly, 0.62 for two to four times per week, and 0.58 for five or more times per week (P, trend, .0002).A significant reduction in risk of NIDDM persisted after adjustment for both age and body-mass index: RR, 0.71 (95% Cl, 0.56 to 0.91; P=.006) for at least once per week compared with less than once weekly, and P, trend, .009,for increasing frequency of exercise.Further control for smoking, hypertension, and other coronary risk factors did not materially alter these associa- tions.The inverse relation of exercise to risk of NIDDM was particularly pronounced among overweight men.Conclusions.\\p=m-\\Exerciseappears to reduce the development of NIDDM even after adjusting for body-mass index.Increased physical activity may be a promising approach to the primary prevention of NIDDM.",
      "Type 2 diabetes can be prevented or delayed by lifestyle modification, including increased physical activity, beneficial dietary changes, and weight reduction (22,44).However, only Model adjusted for age, gender, group, baseline value of moderate-to-vigorous physical activity, and baseline values and changes in body weight and in intakes of energy and energy-adjusted saturated fat and fiber. *The median (range) of each tertile of change in moderate-to-vigorous physical activity is shown.Adjusted interaction between moderate-to-vigorous physical activity (3 groups) and the polymorphism (2 groups) on the risk of developing type 2 diabetes.a few studies have investigated the effects of such lifestyle interventions on insulin sensitivity and insulin secretion in persons with IGT (21,46).On the basis of the 4-yr follow-up study of the DPS with repeated frequently sampled intravenous glucose tolerance test (FSIGT), insulin sensitivity improved along with lifestyle changes, while insulin secretion remained virtually unchanged (46).Most other data also indicate that physical activity, diet, and weight loss primarily increase insulin sensitivity.Insulin resistance and the associated glycemic stress may exhaust -cells and impair their function.Regular physical activity may diminish glycemic stress by improving insulin sensitivity of target tissues (18).While the mechanisms of improved -cell function in response to lifestyle interventions are still largely unknown, several studies suggest that physical activity (5,11), diet (19,26), weight loss (45), or their combination (21) may directly improve the first-phase insulin secretion that is an indicator of the -cell function.GENETIC FACTORS AND LIFESTYLE interact in the development of type 2 diabetes.Physical activity, favorable dietary changes, and weight reduction were essential components of a success-ful lifestyle intervention in two large randomized controlled trials on the prevention of type 2 diabetes in high-risk individuals with impaired glucose tolerance (IGT), including the Finnish Diabetes Prevention Study (DPS) (44) and the Diabetes Prevention Program (DPP) (22).In the DPS, increased physical activity was associated with a decreased risk of type 2 diabetes independently of changes in diet and body weight.The individuals who increased their physical activity most (i.e., were in the upper third of the change) were 66% less likely to develop type 2 diabetes than those in the lower third (24).",
      "Aerobic activity, alone or in combination with diet, can reduce systolic blood pressure, reduce total cholesterol, raise HDL cholesterol, and improve endothelial function in overweight patients with young-onset type 2 diabetes. 47owever, any potential benefits to the cardiovascular disease risk profile are lost within 3-6 months after cessation of exercise training, and do not confer protection against later cardiovascular events. 47,121Additionally, reviews 49,121,122 of the limited number of studies done to date have not identified substantial or lasting benefits of doing aerobic exercise on glucose homoeostasis for patients who are obese with young-onset type 2 diabetes, unless accompanied by dietary intervention.",
      "Weight change is a complex outcome, as both the degree and pattern of weight change impact health.For example, in the Diabetes Prevention Program (DPP; described in more detail later), both short-and intermediate-term weight loss were associated with reduced diabetes risk and intermediate cardiometabolic risk factor levels, whereas weight cycling (defined as number of 5 lb [2.25 kg] weight cycles) raised diabetes risk, fasting glucose levels, insulin resistance, and systolic blood pressure.Initial (baseline to 1 month) and late (last 6 months of the 2-year intervention period) weight loss had no discernable impact of diabetes risk (26).Similar results have been reported in people with pre-existing diabetes who underwent lifestyle intervention as part of the Look AHEAD (Action for Health in Diabetes) trial (27).These studies point to alternative phenotypes that may be informative for genetics studies of weight loss/ maintenance/regain.",
      "Physical activity. Increased physical activity is an essential component of all effective lifestyle-based trials for the prevention of T2DM.Prospective evidence has shown that both aerobic exercise and resistance training independently have beneficial effects on preventing T2DM 64 .One study has shown that spending more time on moderateintensity and vigorous-intensity physical activity is beneficial for preventing insulin resistance, independent of time spent sedentary 65 .By contrast, another study found that time spent sedentary was associated with an increased risk of T2DM, regardless of physical activity 66 .",
      "Multiple interventions in adults with T2D have been evaluated for risk reduction and prevention, both in the short and the long term.A recent systematic review (69) reported that after active interventions lasting from 6 months to .6 years, relative risk reduction achieved from lifestyle interventions (39%) was similar to that attained from use of drugs (36%); however, only lifestyle interventions had a sustained reduction in risk once the intervention period had ended.Analysis of the postintervention follow-up period (;7 years) revealed a risk reduction of 28% with lifestyle modification compared with a nonsignificant risk reduction of 5% from drug interventions.",
      "Engagement in regular physical activity and increased physical fitness are recommended for the prevention and treatment of diabetes and other pathological conditions 5,18,19 .We recently demonstrated that four months of moderate physical training, besides being beneficial to glycemic control, was also effective in improving the redox homeostasis in diabetic patients, lowering the oxidant species production and/or increasing the endogenous antioxidant defenses 20 .In the present study, we aimed to analyse the effect of regular engagement in moderate physical training on telomere length, spontaneous and H 2 O 2 -induced DNA damage, and apoptosis in purified blood leukocytes derived from untrained and trained T2D subjects, compared to age-matched untrained and trained controls.In addition, we examined whether exercise training affected the transcriptional level of a set of genes involved in DNA repairs systems, cell cycle control, as well as antioxidants and defence systems, by comparing untrained and trained T2D patients."
    ],
    [
      "IntroductionComplex diseases, such as diabetes and obesity, result from the interaction of genetic and environmental factors [1][2][3].Approximately 170 gene loci have been robustly implicated in diabetes through genome-wide association studies [4].Studies with knockout mouse models have identified hundreds of genes that can act autonomously to regulate insulin levels (MP:0001560) [5].However, it is still elusive to understand the underlying mechanisms of how these loci or genes contribute to diseases.Network modeling methods have been developed based on the premise that complex diseases are often caused by perturbation to a sub-network of genes [1,[6][7][8][9][10][11][12][13][14].We have applied these methods to identify causal genes for diabetes-related traits in multiple experimental mouse crosses [13][14] and human populations [1].These analyses suggest that potentially many thousands of genes, under the right circumstances, can affect metabolic states.",
      "Genetic factors are known to play a role in T2D and an understanding of the genetic basis of T2D could lead to the development of new treatments (Frayling, 2007a,b;Frayling & Mccarthy, 2007;Frayling, 2008).With the increased prevalence of diabetes worldwide, the need for intensive research is of high priority.Sequencing of the human genome and development of a set of powerful tools has made it possible to find the genetic contributions to common complex diseases (Donnelly, 2011).Genome-wide association studies (GWAS) have been used to search for genetic risk factors for complex disease (Hindorff, Junkins et al., 2009;Hindorff, Sethupathy et al., 2009).Used in combination with the scaffold data of the human genome courtesy of the HUGO Project (2003) and the International HapMap Project (Thorisson et al., 2005), it is now possible to analyse the whole genome to identify genetic variants that contribute to common disease in a fast and efficient manner.",
      "GENE DISCOVERY IN T2DWhy?",
      "Candidate g ene a pproachThe molecular screening of candidate genes to search for genetic variants (either rare when the allele frequency is < 0.01, or common in the population tested) potentially associated with diabetes status (i.e. more frequent in individuals with T2DM) has so far been the most frequently used approach to tackle the genetic determinants of T2DM [61] .There are many reasons why specifi c genes may be candidates:  A gene may have a known or presumed biologic function in glucose homeostasis or energy balance in humans.",
      "Interactions in diabetes <p>An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.</p>",
      "Received: 7 May 2009 Accepted: 25 February 2010Published: 25 February 2010References1. Sieberts SK, Schadt EE: Moving toward a system genetics view of disease. Mamm Genome 2007, 18:389-401. 2. Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS,Argmann C, Schueler KL, Edwards S, Steinberg HA, Chaibub Neto E,Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C,Attie AD: A gene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility. Genome Res 2008,18:706-716. 3.",
      "Genome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions.",
      "Genome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known.",
      "Genome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.",
      "Figure5.Consideration of the human gene network boosts recovery of validated type 2 diabetes genes from GWAS analysis of 2000 patients and 3000 controls. (A,B) Plotted using the same conventions as in Figure4, analyzing WTCCC GWAS data (Wellcome Trust Case Control Consortium 2007) for type 2 diabetes alone and in combination with HumanNet and measuring performance as AUC (<5% FPR) for recovering the top 20 genes from a type 2 diabetes meta-analysis of 4549 cases and 5579 controls(Zeggini et al. 2008).As for Crohn's disease, consideration of the network boosts performance across a wide range of parameter values.Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes; CTNNB1 is well studied in connection with type 2 diabetes and BACH2 has been previously implicated in type 1 diabetes and celiac disease (e.g.,Cooper et al. 2008;Madu et al. 2009), but not type 2 diabetes.",
      "GenomicsDuring the past few decades, candidate gene approach with case-control study design has been most successful in understanding the genetic etiology of any complex disease.The method begins with selection of putative candidate gene based on its functional role in disease related metabolic pathway, followed by prioritizing single nucleotide polymorphisms (SNPs) that have functional consequences either by affecting the gene regulation or its product.Finally, the prioritized SNPs/variants are genotyped in a random sample of cases and controls and tested for their association with the trait.So far, a total of 1874 unique markers that belong to 421 genes were identified as associated with type 2 diabetes through this approach (Lim et al. 2010).However, an overwhelming inconsistency is observed in the patterns of their association with the disease, with exception to the polymorphisms that belong to TCF7L2, CAPN10, PPARG, KCNJ11, ABCC8, HNF1A, HNF4A, GCK, PC-1/ENPPI, IRS, PTPN1, and LMNA genes which showed much greater degree of consistency (Kommoju and Reddy 2011;Ali 2013).Not being satisfied with this approach, researchers shifted the focus to genome wide association studies (GWAS), which is an agnostic method of testing for association of all the SNPs identified in human genome project with a particular disease through chip based microarray technologies such as Illumina and Affymetrics.A large number of cases and controls are screened through this method and the SNPs with strong signal/high significance (pB10 -08 ) are considered to be disease susceptible/causing.Only these SNPs are further evaluated for their functional consequences.Through this approach, numerous polymorphisms have been identified as associated with type 2 diabetes and the SNPs of TCF7L2, HHEX, CDKN2A/2B, IGF2BP2, SLC30A8, CDKAL1, HMGA2, KCNQ11, and NOTCHADAM30 genes being the most replicated ones (www.genome.gov/gwastudies).The search results for type 2 diabetes associated genetic variants yielded 388 significant SNPs from 58 GWAS studies.However, many of these type 2 diabetes associated variants need to be functionally validated before attempting to understand their prospective clinical benefits.The TCF7L2 is the only gene which is hitherto functionally characterized as key transcription factor coding gene and involved in regulating the glucose homeostasis (Savic et al. 2011;Boj et al. 2012).As a key component of WNT signaling pathway, it is involved in pancreatic b-cell proliferation and in turn insulin secretion and action (Gupta et al. 2008).It was initially identified as associated with the disease through a genetic linkage study on the Icelandic population (Grant et al. 2006) and subsequently replicated in Danish (Grant et al. 2006), European (Scott et al. 2006) and US cohorts (Zhang et al. 2006) and currently known to be associated across the ethnic groups worldwide (Kommoju and Reddy 2011).Additionally, a 4kb haplotype block at 9p21.3 chromosomal region was found specific to and associated with type 2 diabetes (Silander et al. 2009).Harboring CDKN2A/CDKN2B genes with functional implications in cell proliferation pathway, this chromosomal region was observed to be associated with multiple complex diseases and needs detailed exploration for its potential as a therapeutic target in general and particularly with type 2 diabetes.However, the variants identified by GWAS were found to explain only 10% of variation in type 2 diabetes and most of those (more than 90%) are located in the non-coding region (Grarup et al. 2014;Scott et al. 2016).The search for rare variants with larger penetrance and functional significance is on through next generation and exome sequencing strategies (Jenkinson et al. 2016).",
      "One attractive methodology to circumvent the puzzle of choosing either a hypothesis-driven or an exploratory research may be the strategy of gene prioritization offered by the new bioinformatics tools based on the biological plausibility of a gene-disease association and on knowledge of the protein function. 6e propose an approach for expanding the selection of genes or loci of interest and prioritizing associations over GWAs related with genetic susceptibility to type 2 diabetes.The proposal profits from the recent initiatives of data sharing of the genome scan results that make the information publicly available as soon as they are generated and checked for quality.Both the DGI and the WTCCC are committed to embracing these principles as they made available all the phenotype-genotype data for type 2 diabetes.",
      "Background: Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D).However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes.There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.Methods: We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes.Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners.We then studied the relationship between these T2D genes and curated gene sets.Results: We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20.Using the T2D genetic network, we identified highly-interconnected network \"hub\" genes.By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories.We found that highlyinterconnected T2D disease network \"hub\" genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1.The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list.Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer.We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport.Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.Conclusions: Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors.T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks.The framework developed for T2D studies may serve as a guide for studying other complex diseases.ConclusionsLarge-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors.In this work, we determined that T2D candidate risk genes are located in higher concentration in certain parts of the genome, specifically in chromosome 20.Using the T2D genetic network, we identified  highly interconnected network \"hub\" genes.By incorporat-T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories.Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.Moreover, we demonstrate a computational framework built upon disease-specific data integration, Figure 2 T2D risk gene pathway interaction network.Here, an edge will be created between two pathways, if and only if the pathways involved three of more risk genes.Figure 3 T2D risk gene functional category crosstalk network.For this figure an edge will be created between two functional categories for all significant Gene Ontology catagories.To confirm the presence of molecular systems structures that may better explain missing heritability problems for T2D, we adopted a Systems Biology approach to studying T2D genetic risk gene networks as a whole rather than the risk genes individually.Prior to this study, several reports [10,11] examined genes implicated T2D differential expressions in affected tissues.In this study, we used T2Dassociated SNP information curated from the Type 2 Diabetes Genetic Association Database (T2DGADB), which integrated comprehensively reported SNPs, their odds ratios, population description, and all related metadata from various T2D GWAS performed worldwide [12].We further annotated individual SNPs collected from T2DGADB with information from the DbSNP database [13], including information such as nearby genes, Chromosomal location, gene functional class, and base changes.To create a model for T2D genetic risk gene molecular systems structure, we built a gene interaction network seeded by T2D risk genes collected from T2DGADB and expanded with high-confidence protein interaction data collected from the Human Annotated and Predicted Protein Interaction database (HAPPI) [14].We also ranked risk genes in the network according to these high confidence interactions.Results: We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20.Using the T2D genetic network, we identified highly-interconnected network \"hub\" genes.By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories.We found that highlyinterconnected T2D disease network \"hub\" genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1.The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list.Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer.We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport.Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.Conclusions: Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors.T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks.The framework developed for T2D studies may serve as a guide for studying other complex diseases.",
      "Genetic factors appear to play a role in determining an individual's risk of developing diabetes.It is hoped that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies, as well as help identify novel targets for future intervention.A substantial number of genetic loci, gene polymorphisms, and mutations have already been reported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturity onset diabetes of the young [MODY]), while others appear to be involved in response to antihyperglycemic agents.We have compiled the following glossary of genetic and genomic terms relating to diabetes, which we hope will prove a useful reference to researchers and clinicians with an interest in this disease.This is by no means an exhaustive list, but includes many of the genetic loci and variants that have been studied in association with diabetes.Gene encoding insulin-like growth factor 2 mRNA binding protein 2 (also known as IMP-2).SNPs in the gene have been associated with type 2 diabetes IFIH1",
      "To gain insights into how the linking nodes of our final network contribute to T2D biology, we used the DisGeNET database [37], which collates gene-disease information from public data as well as from literature via natural language processing tools.We focused on the 274 linking nodes included in our model to avoid circularity arising from using the seeds, and identified 92 (~33%) with known links to T2D (Additional file 1: Table S2).Examples include as follows: (a) NEUROD1 which encodes a transcription factor that is involved in the development of the endocrine cell lineage and has been implicated in monogenic diabetes [38], (b) PRKCB involved in insulin resistance [39] and (c) GNAS, implicated in beta-cell proliferation [40].For this last gene, mouse knockouts have been shown to produce phenotypes concordant with diabetes [41].These examples demonstrate the potential of these analyses to draw in \"linking\" nodes as related to T2D even when they are not located within genome-wide association signals.Background: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network. Results:We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment (p = 3.9  10  5 ) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion.Conclusions: These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction.The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.Background: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network."
    ],
    [
      "Data generated by these experiments are iteratively subjected to novelinformatics approaches, network analysis, and modeling to find important regulatory nodes, discover the emergent property of the system,and predict the systems behavior under various conditions. GEO, Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/); BIND,Biomolecular Interaction Network Database (http://www.unleashedinformatics.com/index.php?pg=products&refer=bind). GENETICSTHE TIDE HAS TURNEDTO RIGOROUS PHENOTYPINGThe classical forward genetic screen has been thesingle most powerful tool to conclusively identifycritical components of the circadian oscillator, and itscontribution in advancing the field of chronobiology cannot be overstated.",
      "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.GeneNetwork: A Toolbox for Systems GeneticsMegan K. Mulligan1, Khyobeni Mozhui2, Pjotr Prins1,2, Robert W. Williams11.Departmentof Genetics, Genomics, and Informatics, University of Tennessee Health ScienceCenter, Memphis, USA2.PreventiveMedicine, University of Tennessee Health Science Center, Memphis, USAAuthor ManuscriptAbstractThe goal of systems genetics is to understand the impact of genetic variation across all levels ofbiological organization, from mRNAs, proteins, and metabolites, to higher-order physiological andbehavioral traits.",
      "GeneNetwork is one ofeither generate or test ideas by reusing data that oftenan interlinked trio of sites built up by NIAAA (GeneWeaverhave been rescued from the classic literature. Below is a short list of both well-known and more esoteric and WebGestalt are the other two) to house extensiveresources, many of which have been supported by NIAAA, data for human, monkey, rat, mouse, and fruit fly.",
      "In the second part of this work the computed T2DM gene set has been used to identify biological networks on different layers of cellular information such as signaling and metabolic pathways, a comprehensive gene regulatory network and protein-protein interactions.Background: Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide.The main goal of these studies is the detailed analysis of a particular aspect of the disease.Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches.Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM).Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes.The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study.Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction ( 128) of yet barely characterized novel candidate genes.Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks.Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any -yet non-associated -gene. Conclusion:In our paper we have identified a core set of 213 T2DM candidate genes by a metaanalysis of existing data sources.We have explored the relation of these genes to disease relevant information and -using enrichment analysis -we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions.The web interface is accessible via http://t2dmgeneminer.molgen.mpg.de.",
      "We decided to pursue the first hypothesis and adapted a systems biology perspective.Rather than looking for significant aberrations in expression of individual insulin-signaling genes, we looked for significant aberrations in the collective expression of a set of insulin-signaling genes whose protein products form a connected protein-protein interaction network.This was accomplished using a simple methodology referred to as gene network enrichment analysis (GNEA).",
      "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.",
      "This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.",
      "Readersmay refer [42] for a comprehensive review on various availablesoftware tools. GeneNetWeaver (GNW) [43] is a Java-based reverse engineering tool for generating synthetic benchmark expression datasetsfrom gold standard DREAM challenge network. E. coli and Yeasttranscriptional regulatory networks are integrated as test case forbenchmark. Comparative assessment of inference algorithmsagainst DREAM challenge data can also be performed with thehelp GNW. Cytoscape [44] is a powerful tool most suitable forlarge-scale network analysis.",
      "Researchers, however, have thepossibility to fully explore the results by altering the thresholds on the open web resource. Although onlyprotein-coding genes were included in our analysis, the same approach can be applied to non-coding genes63to reveal their potential functions. Similarly, GeneBridge can also be utilized to identify novel gene-diseaseassociations based on known disease-associated genes from databases, such as the Human DiseaseOntology (DO) [207] or DisGeNET [208]. The GeneBridge toolkit could also be applied to large-scaleproteomics datasets after correcting for the background of all measured proteins.",
      "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Recent improvements toGeneNetwork have reinvigorated it, including the addition of data from 10 species, multi-omicsanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource forpredictive medicine and systems genetics, which is constantly being maintained and improved. Here, we give a brief overview of the process for carrying out some of the most commonfunctions on GeneNetwork, as a gateway to deeper analyses, demonstrating how a smallnumber of plausible candidate genes can be found for a typical immune phenotype.",
      "Genome Biol 8(2):R25Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H et al (2005)Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37(3):243253Ihaka R, Gentleman RC (1996) R: a language for data analysis andgraphics. J Comput Graph Stat 5:299314Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME et al (2008) Agene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility.",
      "We next constructed protein-protein interaction networks.To do this, we selected 76 genes known from monogenic forms of diabetes, obesity, and hypertension or GWAS hits (type 2 diabetes, obesity, and hypertension) for which the lead association lies within the protein-coding part of the gene (Table S3).",
      "To test this hypothesis, we used the Web-basedGeneNetwork databases that have been recently introducedto the scientific community and proved to be a powerful toolfor hypothesis-driven investigations (Chesler et al. 2003,2004; Wang et al. 2003). Researchers can take advantageof genetic diversity in panels of recombinant inbred mousestrains to use these databases for studies of the regulation ofgene expression and genetic mechanisms of complex traits. Our in silico investigation provided evidence for potentialfunctional relationships among the 21 DAT-associated proteins detected by mass spectrometry in this study.",
      "Construction and analysis of the T2D risk genes networkTo further sift the results and explore functional connections, we also mapped genes onto known gene sets.For this purpose, we used DAVID [22,23] to search for enriched KEGG [24] pathways.We also used GARNET [25] to identify enriched Gene Ontology categories and their relationships.",
      "Thereby such networks have the potential to beof importance in the emergence of precision medicine (Curtis, 2015; Desautels et al. , 2014;Glade Bender et al. , 2015; Jorgensen, 2015; Kummar et al. , 2015; Marquet et al. , 2015;Rubin, 2014) wherein therapeutic strategies need to be aligned with specific properties oftumors. Author ManuscriptMethodsGeneNetwork and WebGestaltGeneNetwork is an open access, online data analysis resource for systems biology andsystems genetics.",
      "GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing."
    ],
    [
      "Insulin ResistanceInsulin is a pleiotropic hormone that plays a pivotal role in the development of hypertension, diabetes, and the metabolic syndrome.The main metabolic actions of insulin are to stimulate glucose uptake in skeletal muscle and heart and to suppress the production of glucose and very low-density lipoprotein (VLDL) in the liver [66].Under fasting conditions, insulin secretion is suppressed, leading to increased glucose synthesis in the liver and kidneys (gluconeogenesis) and increased conversion of glycogen to glucose in the liver (glycogenolysis) [67].After a meal, insulin is released from pancreatic -cells and inhibits gluconeogenesis and glycogenolysis [67].Insulin stimulates the sympathetic nervous system (SNS) to increase cardiac output and the delivery and utilization of glucose in the peripheral tissues [68].Other metabolic effects of insulin include inhibition of glucose release from the liver, inhibition of the release of free fatty acids (FFAs) from adipose tissue, and stimulation of the process by which amino acids are incorporated into protein [67].",
      "Insulin Resistance in Type 2 DiabetesInsulin resistance is defined as impaired insulin-mediated glucose clearance into target tissues.Physiology studies many years ago showed most of the insulin-mediated clearance of a glucose load goes into skeletal muscle, plus the insulin response to the meal shuts down hepatic glucose production.We now know that the defect with insulin resistance is at both sites.In the fasting state, the degree of hyperglycemia is directly determined by the rate of glucose overproduction by the liver.With eating, failure of adequate insulin-mediated nutrient clearance into skeletal muscle combined with an attenuated halting of hepatic glucose production cause the raised postprandial glycemia.Reference ( 84) is an excellent review of the known pathophysiology from an investigator who performed many of the key studies.",
      "The present: the crucial role of  cells to glucose homoeostasis by feedback regulationThe importance of insulin resistance and -cell dysfunction to the pathogenesis of type 2 diabetes was debated for a long time; many thought that insulin resistance was the main abnormality in type 2 diabetes, and that inability to secrete insulin was a late manifestation. 5This notion changed with the fi nding that, as with most endocrine systems in human beings, a feedback loop operates to ensure integration of glucose homoeo stasis and maintenance of glucose concentration in a narrow range. 7his feedback loop relies on crosstalk between  cells and insulin-sensitive tissues (fi gure 1).Insulin released in response to -cell stimu lation mediates uptake of glucose, aminoacids, and fatty acids by insulin-sensitive tissues.In turn, these tissues feed back information to islet cells about their need for insulin.The mediator of this process has not been identifi ed, but probably includes integration between the brain and humoral system.If insulin resistance is present, as often happens in people with obesity,  cells increase insulin output to maintain normal glucose tolerance.However, if  cells are incapable of this task, plasma concentrations of glucose increase.Glucose metabolism is normally regulated by a feedback loop including islet  cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of -cell response.If insulin resistance is present,  cells maintain normal glucose tolerance by increasing insulin output.Only when  cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise.Although -cell dysfunction has a clear genetic component, environmental changes play an essential part.Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and -cell dysfunction, and the potential role of changes in the microbiome.Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of -cell function are needed.Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions.However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease.",
      "Molecular mechanisms of insulin resistance. Binding of insulin to its receptor activates insulin receptor tyrosine kinase and phosphorylation of a family of insulin receptor substrates (IRSs), especially IRS1 and IRS2 (REF.105) (FIG.6).These phosphorylated IRS proteins bind to and activate intracellular signalling molecules, most important of which is phosphatidylinositol 3-kinase (PI3K).PI3K promotes glucose transporter type 4 (GLUT4) translocation to the plasma membrane, resulting in glucose uptake into skeletal muscle, and phosphorylates and inactivates the transcription factor forkhead box protein O1 (FOXO1), altering transcription of downstream genes.Insulin also stimulates the RAS-mitogen-activated p rotein kinase (MAPK) pathway.Figure 4 | Insulin secretion in response to glucose.a | Characteristic insulin secretory response (reconstructed by deconvolution of plasma C-peptide levels) to oral glucose in patients with type 2 diabetes mellitus (T2DM) and in body mass index (BMI)-matched non-diabetic individuals.Note the higher fasting secretion rate, the initial blunted secretory response and the later catch-up phase (due to higher glycaemia).b | The insulin secretion rates of panel a are here plotted against the concomitant plasma glucose concentrations to show the deficit in glucose sensing in patients versus normal glucose-tolerant (NGT) controls.Actual experimental data have been averaged and interpolated to produce these graphs.Box 1 | Glucose homeostasisFollowing a meal, insulin secretion is stimulated and glucagon secretion is inhibited by the combined actions of hyperinsulinaemia and hyperglycaemia.Approximately 60-70% of insulin secretion is dependent on the release of the incretin hormones, including glucagon-like peptide 1 (GLP1) and gastric inhibitory polypeptide (GIP) by the L cells and the K cells in the gut, respectively.Collectively, the changes in glucose, insulin and glucagon levels suppress hepatic glucose production, stimulate muscle glucose uptake and inhibit lipolysis; the latter results in a reduction in the free fatty acid concentration in blood, which further enhances the effect of insulin on the liver and muscle.Type 2 diabetes mellitus is associated with major disturbances in all of the preceding physiological responses: insulin secretion is impaired; fasting plasma glucagon levels are increased and fail to suppress normally after a meal; basal hepatic glucose production is increased and fails to suppress normally after a meal; muscle glucose uptake is impaired; fasting plasma free fatty acid levels are increased and fail to suppress normally following a meal; and the post-meal rise in GLP1 and GIP is normal or modestly decreased.However, there is severe -cell resistance to the stimulatory effect of both GLP1 and GIP on insulin secretion.Insulin secretion.-cells integrate inputs from substrates (such as glucose, FFAs, arginine, fructose and amino acids), hormones and nerve endings to adjust insulin release in response to changing demands (for example, fasting-feeding cycles, exercise and stress) on a minuteto-minute basis in order to maintain normal blood glucose levels, and inter-individual differences affect this adjustment.For example, a lean, insulin-sensitive adult might need as little as 0.5 U of insulin to dispose of an oral load of 75 g of glucose over 2 hours, whereas an obese, insulin-resistant, glucose-intolerant person might require 45 U to perform the same task (~90-fold inter-individual difference).In vivo tests in humans using intravenous or oral glucose, arginine, sulfonylureas (antidiabetic drugs) or mixed meals have demonstrated impaired -cell function in overt T2DM.However, reliable quantitation of in vivo -cell dysfunction requires some form of modelling 78 .Absolute insulin secretion in response to an oral glucose challenge can be normal or even increased in T2DM (FIG.4a), except in long-standing, poorly controlled disease, in which absolute insulin secretion is reduced.However, when insulin secretion rates are plotted against the concomitant plasma glucose concentrations, patients with T2DM secrete substantially less insulin than non-diabetic controls (FIG.4b).This decline in -cell glucose sensing occurs along a continuum extending from normo glycaemia through prediabetes to decompensated diabetes in adults 79 and children 80 , and is a potent predictor of progression to diabetes independently of insulin resistance and classic phenotypic predictors 79 .Absolute insulin secretion is a positive antecedent of deteriorating glucose tolerance.Furthermore, the ability of -cells to respond to the rate of increase in plasma glucose concentration (rate sensitivity) is impaired in individuals with T2DM 79 .Antecedent hyperglycaemia and high levels of incretin hormones (GLP1 and GIP) potentiate glucosestimulated insulin release in healthy individuals.In patients with T2DM, glucose-mediated potentiation of insulin release is increased compared with normal glucose-tolerant individuals (owing to the hyperglycaemia); incretin potentiation, however, is severely compromised 81 .The incretin defect is not reversed by reducing the plasma glucose concentration 82 .",
      "The effect of insulin has also been investigated both in vivo and in vitro. In vivo, contradictory results were obtained depending onthe way of administration and the quantity ofinsulin used. For instance, the intraperitonealadministration of a pharmacological dose of insulin decreased expression of FBPase (PlagnesJuan et al. , 2008), but similar acute treatmentwith physiological dose exhibited opposite effect (Polakof et al. , 2010d). Inhibitory actionof insulin can nevertheless be observed afterlong-term infusion of physiological quantity ofinsulin (Polakof et al. , 2010d).",
      "However, a suggestion thatinsulin exerts partial control over gluconeogenesis isobserved since the activity of phosphoenolpyruvatecarboxyldnase in liver from younger diabetic mice isnot as greatly increased as it is in liver from olderdiabetics with blood sugar concentrations greater than250 mg/100 ml. P l a s m a insulin assay.The reasons for the ineffectiveness of this excesscirculating insulin in maintaining normal blood sugarconcentration and in regulating the rate of gluconeogenesis are obscure. A possibility, which cannot beexcluded, is the presence of insulin antagonists [23]. However, their presence seems unlikely in view of thepotent action of insulin in sustaining lipogenesis andin increasing glycolysis in these mice.",
      "The pathophysiological processes leading to type 2 diabetesGlucose, a monosaccharide, is the key carbohydrate of energy metabolism.The three major sources of circulating glucose in the human body are intestinal absorption, gluconeogenesis and glycogenolysis.Blood glucose homeostasis is regulated by gluco-regulatory hormones such as insulin, glucagon, amylin, glucagon-like peptide 1, glucose-dependent insulinotropic peptide, epinephrine, cortisol and growth hormone (Stephen et al. 2004).Insulin is the key regulatory hormone of blood glucose homeostasis with its excitatory action of stimulating glucose uptake and inhibitory actions on gluconeogenesis, glycogenolysis, proteolysis, lipolysis and ketogenesis (Sonksen and Sonksen 2000).Ever since the role of insulin in glucose homeostasis is understood, it has been the primary therapeutic target in type 2 diabetes patients (Tibaldi 2013).The major pathological mechanisms of type 2 diabetes are the defective insulin secretion due to dysfunctional pancreatic b-cells and impaired insulin action through insulin resistance (Lin and Sun 2010; Ashcroft and Rorsman 2012).",
      "Impaired b-cell function is considered a key factor in the pathogenesis of type 2 diabetes (T2D) driven by insulin resistance (1).Insulin secretion in response to an intravenous glucose stimulus is a two-phase process: the first peak of insulin secretion occurs rapidly within 5-10 min after the glucose infusion, followed by a second peak depending on the degree and duration of glucose stimulus (1).Although the insulin response to ingested glucose (e.g., from a meal) does not exhibit a clear biphasic shape under physiological conditions, an early insulin response with rapid elevations of portal and peripheral insulin concentrations has been observed (2,3).A previous study found that the plasma insulin response at 30 min after an oral glucose load was inversely associated with the 2-h plasma glucose concentrations in patients with impaired glucose tolerance (4).This implies that the early-phase insulin secretion is a marker for postprandial glucose homeostasis and plays a role in the development of T2D.",
      "IntroductionType 2 diabetes is characterised by an elevation in blood glucose in the fasting state and/or following a glucose challenge resulting from insulin resistance and insufficient compensatory insulin secretion by pancreatic beta islet cells.Insulin action, as the insulin sensitivity index (S I ), can be estimated from the frequently sampled IVGTT with minimal model.Other indices include the acute insulin response to glucose (AIR g , reflecting insulin secretion) and the disposition index (DI=S I AIR g , measuring overall glucose homeostasis and taking account of the hyperbolic relationship between S I and insulin secretion).Glucose effectiveness (S G ) represents an insulin-independent effect whereby glucose mediates its own disposal from plasma.Impairments in these insulin action and glucose metabolism indices are recognised as prediabetic phenotypes involving pathogenic development and pathogenetic processes of type 2 diabetes.Exercise training improves peripheral S I and S G in healthy human subjects [1], and significant improvements in S I , AIR g , DI and S G in response to 20 weeks of endurance exercise training have been observed and reported in the HERITAGE Family Study [2].Recent investigations in HERITAGE provide further evidence that physiological training responses vary appreciably from person to person, and these individual differences are influenced by genetic factors [3].",
      "(i) Removal of glucose from the blood is primarily achieved by insulin induction of glucose uptake into muscle.This involves insulin sensing and signalling within individual muscle cells, mobilisation of GLUT4 transporters to the cell membrane and conversion of glucose to glycogen for storage [31].Each of these processes has strict regulatory mechanisms that respond to more than just the amount of insulin the cells are exposed to (e.g.glycogen content, exercise, adrenaline, hypoxia, lipids, etc.). (ii) Glucose can be removed from the blood by adipose tissue and is also a fuel source for most cells in the body.At the same time endogenous glucose production in the liver is suppressed by insulin [32], but also by other nutrients (including glucose), and the liver is the primary site of insulin removal from the blood.Therefore there are at least three major organs that contribute directly to the level of glucose and insulin in the blood, and which work in concert to cope with variations in nutrient load or requirement, as well as to induce counterregulatory pathways to limit rebound in any given response.It is now known that many of the proteins involved in these actions work in a tissue-specific fashion, and that most of the intracellular molecular pathways involved have inherent redundancy (Fig. 2), with the ability to mask minor changes in the activity of the proteins involved [33,34]. (iii) Whole-body insulin resistance could arise from hepatic, muscle or adipose insulin resistance or combinations thereof.Glucose homeostasis depends in large part on production of appropriate quantities of insulin by pancreatic b-cells correctly timed around nutrient ingestion.In the evolution of an individual case of T2DM, it is generally considered that sensitivity to insulinmediated glucose disposal and insulin suppression of hepatic glucose production diminishes over time (e.g. as a result of increasing adiposity), with an initial compensatory increase in insulin secretion from b-cells to achieve glucose homeostasis.At this stage, which may be asymptomatic and prolonged, absolute insulin concentrations measured in plasma may be higher than the reference range.For an individual developing T2DM, a plot against time of total insulin secretion across a standard oral glucose tolerance test (OGTT) is therefore an inverted ''U''-shape as b-cells (teleologically) fail to maintain compensation [15].As compensation becomes less effective (''b-cell exhaustion''), even in the absence of a further deterioration of insulin sensitivity, either impaired glucose tolerance or impaired fasting glucose will develop before finally, the threshold is crossed for a diagnosis of T2DM (as defined by current WHO/ ADA glucose criteria).This trajectory of increase in insulin resistance, b-cell compensation and subsequent failure is nicely demonstrated in the Whitehall II study, a prospective follow up of London civil servants (Fig. 1) [16].In this model, insulin resistance plays an early (pre-diabetic) and important part in the development of T2DM, possibly even inducing b-cell failure due to the strain of prolonged compensation.Complex processes involved in insulin actionAs detailed earlier, clinical assessment of insulin sensitivity primarily relies on measurement of blood glucose and insulin, either in the fasted condition or under hormonal or nutrient ''clamp'' conditions.While the secretion of insulin is almost exclusively controlled by the functional state of the b-cell there are a large number of other tissues involved in maintaining proper response to changes in nutrients such as glucose.In addition there are multiple counter-regulatory mechanisms in the body to cope with changes in hormonal and nutrient exposure.In other words, mammals have evolved to keep a very tight control on blood glucose concentration and it is highly likely that multiple molecular problems would have to occur simultaneously to alter whole body insulin sensitivity significantly.",
      "Pathophysiology and major risk factorsWhen the feedback loops between insulin action and insulin secretion do not function properly, the action of insulin in insulin-sensitive tissues such as liver, muscle and adipose tissue (insulin resistance in T2DM) and insulin secretion by pancreatic islet -cells (-cell dysfunction in T2DM) are affected, which results in abnormal blood levels of glucose 37 (FIG.2).In T2DM, insulin resistance contributes to increased glucose production in the liver and decreased glucose uptake in muscle and adipose tissue at a set insulin level.In addition, -cell dysfunction results in reduced insulin release, which is insufficient for maintaining normal glucose levels 38 .Both insulin resistance and -cell dysfunction occur early in the pathogenesis of T2DM, and their critical importance has been verified longitudinally in Pima Indian people progressing from normal glucose tolerance to impaired glucose tolerance to T2DM 39 .Figure 2 | Pathophysiology of hyperglycaemia in T2DM.Insulin secretion from the -cells in the pancreas normally reduces glucose output by the liver and increases glucose uptake by skeletal muscle and adipose tissue.Once -cell dysfunction in the pancreas and/or insulin resistance in the liver, skeletal muscle or adipose tissue occur, hyperglycaemia develops, leading to an excessive amount of glucose circulating in the blood.The various factors listed at the top affect insulin secretion and insulin action.T2DM, type 2 diabetes mellitus.",
      "The role for pro-inflammatory cytokines in regulating insulin action and glucose homeostasis and their function in T2DM has been suggested by several lines of evidence."
    ],
    [
      "Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity.Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease.However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence 1 .Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity 2,3 , diabetes 4 and cardiovascular disease 5 .",
      "Diet, Nutrition, and Type 2 DiabetesObesity is pathophysiologically associated with the development of type II diabetes [199,200].Oxidative stress and inflammation, metabolic impairment and accelerated aging on both the micro-and macrocellular level contribute to the pathogenesis of metabolic diseases [201,202].",
      "Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.",
      "IntroductionThe aetiology of type 2 diabetes is poorly defined: several studies indicate that the disease results from a combination of genetic susceptibility and external risk factors [1].According to this multifactorial model, genetically predisposed subjects will not necessarily develop overt disease unless they are also exposed to particular environmental factors [2].Important risk factors for the development of type 2 diabetes include a family history of diabetes, increased age, hypertension, lack of physical exercise, and obesity [1].",
      "T ype 2 diabetes, though poorly understood, is known to be a disease characterized by an inadequate beta-cell response to the progressive insulin resistance that typically accompanies advancing age, inactivity, and weight gain. 1 The disease accounts for substantial morbidity and mortality from adverse effects on cardiovascular risk and disease-specific complications such as blindness and renal failure. 2 The increasing global prevalence of type 2 diabetes is tied to rising rates of obesity 2 -in part a consequence of social trends toward higher energy intake and reduced energy expenditure.However, the mechanisms that underlie individual differences in the predisposition to obesity remain obscure.T ype 2 diabetes, though poorly understood, is known to be a disease characterized by an inadequate beta-cell response to the progressive insulin resistance that typically accompanies advancing age, inactivity, and weight gain. 1 The disease accounts for substantial morbidity and mortality from adverse effects on cardiovascular risk and disease-specific complications such as blindness and renal failure. 2 The increasing global prevalence of type 2 diabetes is tied to rising rates of obesity 2 -in part a consequence of social trends toward higher energy intake and reduced energy expenditure.However, the mechanisms that underlie individual differences in the predisposition to obesity remain obscure.Failure to understand the pathophysiology of diseases such as type 2 diabetes and obesity frustrates efforts to develop improved therapeutic and preventive strategies.The identification of DNA variants influencing disease predisposition will, it is hoped, deliver clues to the processes involved in disease pathogenesis.This would not only spur translational innovation but also provide opportunities for personalized medicine through stratification according to an individual person's risk and more precise classification of the disease subtype.In this article, I consider the extent to which these objectives have been realized.",
      "Although the etiology of T2D has not been fully established, a number of risk factors are well defined.According to the ADA [22], the risk of developing T2D is associated with age (increased risk at 45 years), overweight/obesity, and lack of PA.T2D is more common in individuals with a family history of the disease, in certain ethnic groups (e.g., African-Americans, Hispanic-Americans, Native Americans, Asian-Americans, and Pacific Islanders), and in individuals with hypertension (140/90 mmHg in adults), dyslipidemia (high density lipoprotein cholesterol [HDL-C] 35 mg/dL (0.90 mmol/L) and/or a triglyceride level 250 mg/dL (2.82 mmol/L)), IFG, IGT, a history of vascular disease or gestational diabetes, or polycystic ovary syndrome.In addition, a range of common genetic variants are also known to raise the risk of T2D [23][24][25], of which some may interact with lifestyle factors to modify the risk of the disease [26].Several examples are provided below.",
      "Background: Type 2 diabetes mellitus is an important risk factor for Alzheimer disease and is more prevalent in elderly minority persons compared with non-Hispanic white persons.",
      "Age. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 .",
      "Age also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is 40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4).",
      "Type 2 diabetes incidence is increasing in youth, especially among the racial and ethnic groups with disproportionately high risk for developing type 2 diabetes and its complications: American Indians, African Americans, Hispanics/Latinos, Asians, and Pacific Islanders (9).Older age is very closely correlated to risk for developing type 2 diabetes.More than one in four Americans over the age of 65 years have diabetes, and more than half in this agegroup have prediabetes (9).The prevalence of type 2 diabetes in the U.S. is higher for males (6.9%) than for females (5.9%) (15).Independent of geography, the risk of developing type 2 diabetes is associated with low socioeconomic status.Low educational level increases risk by 41%, low occupation level by 31%, and low income level by 40% (16).",
      "The aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes.",
      "The prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults.The prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults.Although drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes.ComplicationsEarlier onset of type 2 diabetes is associated with a greater lifetime risk of diabetes-associated complications. 98vidence from several cross-sectional studies [99][100][101][102] has suggested that the burden of diabetes complications is greater for people with young-onset type 2 diabetes than for people with type 1 diabetes or later-onset type 2 diabetes.Based on a modelling study of a hypothetical cohort of adolescents and young adults in the USA, 99 overall life expectancy among patients diagnosed with type 2 diabetes Review at 20-40 years is reduced by 14 years in men and 16 years in women compared with people without diabetes.Summary and future research directionsAlthough it is tempting to extrapolate the disease course of type 2 diabetes in young people as just an earlier and more rapid form of type 2 diabetes in older adults, distinctive differences are evident.The young-onset phenotype has a stronger family history, a greater association with obesity, early loss of both first and second phases of insulin secretion alongside often severe insulin resistance, early onset and rapid progression of microvascular and macrovascular complications, and poor sustainability of responsiveness to oral glucose-lowering therapies, frequently neces sitating early introduction of insulin.In a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis",
      "T ype 2 diabetes is a major risk factor for cardiovascular disease (CVD) and other age-related ailments and affects 200 million people worldwide (1).The prevalence of type 2 diabetes differs across regions and ethnicities, being higher in African-American, Asian, Native-American, and Hispanic populations.In addition to the classical disease biomarkers, type 2 diabetes patients exhibit significantly elevated oxidative DNA damage, as measured by concentrations of 8-hydroxydeoxyguanosine (8-OHdG) or 8-hydroxyguanosine (8-OHG) in leukocytes (2) or urine (3)such that their use as biomarkers in the diagnosis of the disease has been considered (3).Mitochondria control both energy metabolism and reactive oxygen species (ROS) production (4 -6).Thus, mitochondrial dysfunction may contribute to the development of type 2 diabetes (4).Furthermore, diabetic hamsters treated with inhibitors of advanced glycation end products (AGEs) showed reduced oxidative stress and restored pancreatic -cell function (7).However, the mechanism underlying the development of type 2 diabetes, how that mechanism relates to DNA damage, and how type 2 diabetes increases the risk of CVD are not well understood."
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