{
  "question": [
    "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?",
    "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?",
    "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?",
    "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?",
    "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?"
  ],
  "answer": [
    "Recent advancements in multi-omics approaches, including proteomics and metabolomics, allow for a comprehensive characterization of metabolic changes associated with Type 2 diabetes. These tools can identify and measure thousands of metabolites in a given biological sample, providing insights into the disease's development and progression. They can also identify potential early biomarkers for the disease and its comorbidities. Furthermore, these approaches can help understand the body's responses to environmental and lifestyle factors, and the interactions between genetic and environmental factors that produce changes in the body's biochemical profile. This can aid in early diagnosis, therapy monitoring, and understanding the disease's pathogenesis.",
    "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified novel diabetic loci near several genes. These include JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2, UBASH3A, BACH2, AGMO, GDAP1, PTF1A, SIX3, ALDH2, NKX6-3, ANK1, and a microRNA cluster. Additionally, a locus near the LMO7 gene on 13q22 and another near the EFR3B gene on 2p23 were identified. A novel signal was also detected near AGMO.",
    "Epigenetic modifications like DNA methylation and histone modification can alter the expression of diabetes-related genes without changing the underlying DNA sequence. DNA methylation involves the addition of a methyl group to a cytosine within cytosine-phosphate-guanine (CpG) dinucleotides, which can regulate gene expression. Histone modifications, on the other hand, involve changes to the proteins around which DNA is wound, affecting the accessibility of genes for transcription. These modifications can be influenced by factors such as hyperglycemia, inflammation, and oxidative stress, leading to changes in gene expression that contribute to diabetes and its complications.",
    "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes. It has been observed that alterations in the gut microbiome can precede the onset of Type 1 Diabetes (T1D). Dysbiosis of gut microbiota, characterized by an imbalance in the microbial community, can contribute to insulin resistance and the pathogenesis of T2D. The gut microbiome can influence glucose metabolism and insulin sensitivity, and changes in its composition can affect the development and progression of diabetes. Certain gut microbiota can improve glucose homeostasis and leptin sensitivity, potentially offering therapeutic targets for diabetes prevention and management. However, the interactions between host genetics, metabolism, and the immune system in shaping the microbiome and predilection to disease are still being explored.",
    "Machine learning algorithms have shown significant effectiveness in integrating genomic data to predict individual risk and progression of diabetes. The most commonly used algorithms are Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Trees (DT), with SVM being the most successful. The prediction accuracy of these algorithms is often above 80%. Recurrent Neural Network (RNN) models have also been used to predict type 2 diabetes with promising results. However, the effectiveness can vary depending on the specific algorithm used, the quality of the data, and the number of features or attributes used in the model."
  ],
  "contexts": [
    [
      "\t\n\nMechanistic and translational studies that focus on the characterisation of archetypes are likely to be more tractable.For each of the component pathways, we should seek to deepen our understanding of the molecular and physiological machinery responsible for homeostatic control, and of the specific genetic and environmental factors that 'push' individuals towards diabetes.We should aim to identify biomarkers that serve as robust readouts for each of those processes.We already have some examples of these (e.g.islet antibodies, urinary C-peptide) but access to increasingly powerful 'omic' readouts (transcriptomics, proteomics, metabolomics) brings the promise of others [21].We should aim to determine the extent to which the various pharmacological and behavioural interventions that are available influence diabetes progression and management in the different archetype groups.In doing so, we will determine the extent to which we can expect to optimise prevention and therapy on the basis of this improved diagnostic precision.Alternatively, we may find that many treatments work fairly well irrespective of individual pathology, since, to reverse the diabetic phenotype, it may be sufficient to shift enough of the contributing pathways in a beneficial direction.\t\nThe current focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging and other domains will extend our diagnostic and prognostic capabilities, and enable more effective targeting of current and future preventative and therapeutic options.The clinical benefits of this approach are already being realised in rare diseases and cancer but the impact on management of complex diseases, such as type 2 diabetes, remains limited.This may reflect reliance on inappropriate models of disease architecture, based around rare, highimpact genetic and environmental exposures that are poorly suited to our emerging understanding of type 2 diabetes.This review proposes an alternative 'palette' model, centred on a molecular taxonomy that focuses on positioning an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression.This model anticipates that many individuals with diabetes will have multiple parallel defects that affect several of these processes.One corollary of this model is that research efforts should, at least initially, be targeted towards identifying and characterising individuals whose adverse metabolic trajectory is dominated by perturbation in a restricted set of processes.",
      "\t\n\nAs discussed earlier, these high-throughput approaches are already being implemented in diabetic complications research.They have been complemented with systems biology and systems genetics efforts to effectively identify new players in and drug targets for diabetic complications [105].There are also ongoing efforts to systematically profile epigenetic marks in tissues, cells and archived genomic DNA from various clinical trials.The major challenge, however, is expected to be in the analysis of the ensuing large datasets, the complexity of bioinformatics/biostatistics and in silico modelling.If these hurdles can be overcome, these efforts are likely to yield novel insights into epigenome variations linked with diabetic complications.",
      "\t\n\nGriffin JL, Vidal-Puig A. Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding?Physiol Genomics 34: 1-5, 2008.First published April 15, 2008; doi:10.1152/physiolgenomics.00009.2008.-Metabolomicsaims to profile all the small molecule metabolites found within a cell, tissue, organ, or organism and use this information to understand a biological manipulation such as a drug intervention or a gene knockout.While neither mass spectrometry or NMR spectroscopy, the two most commonly used analytical tools in metabolomics, can provide a complete coverage of the metabolome, compared with other functional genomic tools for profiling biological moieties the approach is cheap and high throughput.In diabetes and obesity research this has provided the opportunity to assess large human populations or investigate a range of different tissues in animal studies both rapidly and cheaply.However, the approach has a number of major challenges, particularly with the interpretation of the data obtained.For example, some key pathways are better represented by high concentration metabolites inside the cell, and thus, the coverage of the metabolome may become biased towards these pathways (e.g., the TCA cycle, amino acid metabolism).There is also the challenge of statistically modeling datasets with large numbers of variables but relatively small sample sizes.This perspective discusses our own experience of some of the benefits and pitfalls with using metabolomics to understand diseases associated with type 2 diabetes.NMR spectroscopy; mass spectrometry; obesity; functional genomics WHILE IT IS DIFFICULT TO DATE the start of any field this is particularly true of -omic technologies.The desire to profile a large number of entities involved in any tier of a biological system has been a common thread in biology.The field of metabolomics is no exception to this statement.While the term metabolomics (23) and the related term metabonomics (22) were coined in the late 90s, it is difficult to distinguish some of the work conducted now under the umbrella of metabolomics from much earlier studies involving largescale profiling of metabolites by mass spectrometry (for example Refs.16,30) and NMR spectroscopy (for example Refs.3,5).Indeed many of the basic processes that occur in current metabolomic laboratories would not be that dissimilar to work carried out by the pioneers of metabolic research who gave their names to the various pathways we study.In this brief article we discuss some of the benefits modern metabolomic approaches provide to functional genomics, with particular reference to diabetes and the metabolic syndrome, and outline some of the challenges the field faces if it is to develop into a mature technology.",
      "\t\nClinical and epidemiological metabolomics provides a unique opportunity to look at genotypephenotype relationships as well as the body's responses to environmental and lifestyle factors.Fundamentally, it provides information on the universal outcome of influencing factors on disease states and has great potential in the early diagnosis, therapy monitoring, and understanding of the pathogenesis of disease.Diseases, such as diabetes, with a complex set of interactions between genetic and environmental factors, produce changes in the body's biochemical profile, thereby providing potential markers for diagnosis and initiation of therapies.There is clearly a need to discover new ways to aid diagnosis and assessment of glycemic status to help reduce diabetes complications and improve the quality of life.Many factors, including peptides, proteins, metabolites, nucleic acids, and polymorphisms, have been proposed as putative biomarkers for diabetes.Metabolomics is an approach used to identify and assess metabolic characteristics, changes, and phenotypes in response to influencing factors, such as environment, diet, lifestyle, and pathophysiological states.The specificity and sensitivity using metabolomics to identify biomarkers of disease have become increasingly feasible because of advances in analytical and information technologies.Likewise, the emergence of high-throughput genotyping technologies and genome-wide association studies has prompted the search for genetic markers of diabetes predisposition or susceptibility.In this review, we consider the application of key metabolomic and genomic methodologies in diabetes and summarize the established, new, and emerging metabolomic and genomic biomarkers for the disease.We conclude by summarizing future insights into the search for improved biomarkers for diabetes research and human diagnostics.\t\n\nClinical and epidemiological metabolomics provides a unique opportunity to look at genotypephenotype relationships as well as the body's responses to environmental and lifestyle factors.Fundamentally, it provides information on the universal outcome of influencing factors on disease states and has great potential in the early diagnosis, therapy monitoring, and understanding of the pathogenesis of disease.Diseases, such as diabetes, with a complex set of interactions between genetic and environmental factors, produce changes in the body's biochemical profile, thereby providing potential markers for diagnosis and initiation of therapies.There is clearly a need to discover new ways to aid diagnosis and assessment of glycemic status to help reduce diabetes complications and improve the quality of life.Many factors, including peptides, proteins, metabolites, nucleic acids, and polymorphisms, have been proposed as putative biomarkers for diabetes.Metabolomics is an approach used to identify and assess metabolic characteristics, changes, and phenotypes in response to influencing factors, such as environment, diet, lifestyle, and pathophysiological states.The specificity and sensitivity using metabolomics to identify biomarkers of disease have become increasingly feasible because of advances in analytical and information technologies.Likewise, the emergence of high-throughput genotyping technologies and genome-wide association studies has prompted the search for genetic markers of diabetes predisposition or susceptibility.In this review, we consider the application of key metabolomic and genomic methodologies in diabetes and summarize the established, new, and emerging metabolomic and genomic biomarkers for the disease.We conclude by summarizing future insights into the search for improved biomarkers for diabetes research and human diagnostics.\t\n\nIn this brief review, we consider recent applications of metabolomic and related technologies in diabetes together with their use in relation to clinical diagnostics.Technical details of the methodologies involved and their use in basic diabetes research have been covered in several excellent articles and reviews (1,3).",
      "\tnovEl \"-omics\" TEcHnologiEs\n\nThe number of scientific articles on transcriptomics, proteomics, and metabolomics has been increasing substantively over the state art state art past 10-15 years.The accumulation of information from novel \"-omics\" technologies comes with substantial hope and expectations that these hypothesis-free approaches will yield novel insights into many disease processes and that these insights will eventually translate into clinical applications that will pave the way from current medical routine to the ideal of personalized medicine.With regard to T2D and CVD, the use of data from transcriptomics, proteomics, and metabolomics studies for their predictive potential is still at a very early stage.Here, we aim to provide an overview of studies that are representative of current developments in this research field.",
      "\tOther 'omics' tools\n\n Given the current epidemic status of T2D, the need for the hour is a deeper understanding of associated pathological mechanisms, for timely intervention. To realize this objective, a range of novel tools and techniques need to be integrated in diabetes research, as no one technique is capable of providing the solution by itself.Epigenomics, transcriptomics, proteomics, metabolomics, and computational biology are some tools of the proposed 'omics' toolbox which may contribute to the field of T2D research.\tReview Siddiqui & Tyagi\n\nThe goal of personalized treatment and care for diabetes can be realized by integrating patient-specific knowledge with data from 'omics' technologies.Advances in genomics (including epigenomics), transcriptomics, proteomics and metabolomics may not only help in identifying, assessing and quantitating individual disease risk early on, but will also be beneficial in understanding the specific responses to drug therapy and lifestyle interventions.This can be further complemented with patient information on their economic status, ease or difficulty of access to healthcare (more of a challenge in developing countries), environment (e.g., exposure to high pollution levels, work culture, social structure among others) and lifestyle (e.g., smoking, physical activity, eating preferences among others).An evidence-based therapy, which is implemented timely and incorporates such personal values, circumstances and data, can be more effective in managing diabetes at an individual level.Although the 'omics' revolution has been more successful in providing insights into monogenic diseases than polygenic disorders, its potential in expanding knowledge of genetic determinants influencing diabetes susceptibility and treatment cannot be overlooked.In diabetes research, omics tools have proven their worth in identifying not only susceptibility genes but also biological markers of disease pathology, thereby adding to the understanding of the disease process.\t\n\nSince data from any one tool is insufficient in providing a comprehensive picture, data from all 'omics' tools (genomics, transcriptomics, proteomics, metabolomics among others) can be used in a systems biology approach for a better understanding at tissue or organ system level.Systems biology integrates the given information into interaction networks [74].These networks assess both functional interactions and mathematical correlations between given data in a biological setting and provide a broader picture.Jain et al. [75] have demonstrated the use of a systems biology approach for uncovering genome to phenome correlations in T2D by identifying pathways known to be associated with disease pathology.Although the field of systems biology holds promise, it is still in its nascent stage and requires extensive work to be able to map diseases in complex tissues and organ systems.",
      "\tConclusions and Future Perspectives\n\nCurrent approaches such as transcriptome and proteome profiling, as well as molecular genetics, using various cell lines, animal models and human samples have greatly facilitated the understanding of the mechanism(s) relevant to the progression of diabetic nephropathy.Based on the data generated by using these techniques, the newly discovered biomarkers could serve as therapeutic targets for the amelioration of diabetic nephropathy, which certainly contribute to the reduction in mortality and morbidity in chronic kidney disease patients that progress to ESRD.In addition to transcriptome and proteome approaches, the future trends for the identification of the biomarkers and therapeutic target genes could include genome-scale DNA methylation profiling [75].The emerging role of epigenome control of the cancer cells, germ cells and pluripotent stem cells has been emphasized in the transcriptional regulation of various genes that receive sustained long-term injury for years and decades.Intensive long-term versus conventional short-interval symptomatic therapy seems to have remarkable beneficial effects on the risk of cardiovascular disease in patients with type 1 diabetes and this suggests that there may be alterations in the genomic DNA-or histonemethylation pattern which may be linked to the long-term 'metabolic memory' for the progression of vascular complications of diabetes [76].Such a methylation-related profiling would certainly advance the field, especially with respect to development of new biomarkers and various therapeutic strategies.In addition to the delineation of epigenome control of the genes, metabolic phenotyping using 1H spectroscopy [77] and lectin microarray [78] for the glycan profiling would also promote the identification of the new biomarkers of diabetic nephropathy.Finally, integration of the information from different sources using system biology approaches would be an important step in data-mining for the identification of relevant genes that are pertinent to the diagnosis and therapy for diabetic nephropathy.",
      "\tNovel biomarkers from '-omics' technologies as potential components of risk models\n\nDespite moderate or even good model accuracy in some studies (Table 1, ESM Table 2), current prediction algorithms leave room for improvement and raise the question of whether novel biomarkers could be clinically useful, particularly if they could improve risk models that already contain measures of glycaemia.The range of molecules that could serve as potential biomarkers of diabetes risk includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites, cellular markers and metabolic waste products [39].Owing to current advances in '-omics' technologies, such as genomics, transcriptomics, proteomics and metabolomics, the number of candidate biomarkers keeps growing; however, only a small proportion of these has been investigated with reference to their potential to improve the prediction of type 2 diabetes.",
      "\t\n\nThe so-called omics (eg, metabolomics, lipidomics, proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way.The fi ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to diff erent glucose-lowering therapies.Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes. 84,85Furthermore, patients with high concentrations of specifi c six-carbon sugars, aminoacids, and fatty acids, and low concentrations of other aminoacids and fatty acids, had an increased risk of developing type 2 diabetes over a 7 year follow-up. 86hether all or some of these substrate markers are associated with genetic determinants, dietary factors, or the actions of gut microbes has not been established.",
      "\tMetabolomics and novel circulating biomarkers\n\nMetabolomics is a comprehensive characterization of metabolic changes connected to disease development and progression.High sensitivity and resolution of mass spectrometry achieved with liquid or gas chromatography allows the detection and quantification of thousands of metabolites.An alternative method to quantify metabolites is the high-throughput serum nuclear magnetic resonance platform, but the number of metabolites identified using this method is substantially lower compared with mass spectrometry [22].By using high throughput technologies, metabolomics allows the identification and measurement of metabolites recognizable in a given biological sample.Identification of small biomolecules (metabolites) makes it possible to find early biomarkers for a disease of interest, including T2D and its comorbidities.A recent systematic review and meta-analysis covering the years from 2008 to 2017 included 14 studies and 4,592 individuals with T2D and 11,492 without T2D [23].Their report noted a 1.89-, 1.63-, and 1.87-fold higher risk of T2D associated for leucine, alanine, and oleic acid, respectively, whereas lysophosphatidylcholine C18:0 and creatinine were associated with 20% and 37% decreased risk of T2D, respectively.Our 4.6-year follow-up study of the METSIM cohort included 5,181 participants having metabolomics data available for twenty amino acids at baseline.Five amino acids (tyrosine, alanine, isoleucine, aspartate and glutamate) were significantly associated with a decrease in insulin secretion and an increased risk of incident T2D after adjustment for confounding factors [24].All essential amino acids, and especially branch-chain amino acids, stimulate insulin secretion and GLP-1 release [25].The mechanisms of reduced insulin secretion of five amino acids in our study remains to be determined but could be explained, at least in part, by glucagon regulation [26,27].Interestingly, a recent study demonstrated a causal relationship between the gut microbiome, short-chain fatty acids and metabolic diseases.The host-genetic-driven increase in gut production of the fecal short-chain fatty acid butyrate was significantly associated with improved insulin response after an OGTT, and another short-chain fatty propionate, was causally related to an increased risk of T2D in the MR.These data provide evidence of a causal effect of the gut microbiome on metabolic traits [28].The metabolomics approach has limitations in the identification of metabolites for the risk of T2D.There is no consensus on how to standardize metabolomics results, making it difficult to compare the findings across different studies.Additionally, protocols and statistical approaches may differ, and instrumentation can yield varied sets of detectable metabolites [29].Despite these potential limitations, studies applying metabolomics have the potential to identify a unique set of metabolites predictive of T2D.",
      "\tRecent advances in mass spectrometry have expanded the scope and reliability\nof proteomics and metabolomics measurements. These tools are now capable of identifying thousands of factors driving diverse\nmolecular pathways, their mechanisms, and\nconsequent phenotypes and thus substantially contribute toward the understanding of\ncomplex systems. RATIONALE: Genome-wide association stud-\n\nies (GWAS) have revealed many causal loci\nassociated with specific phenotypes, yet the\nidentification of such genetic variants has\nbeen generally insufficient to elucidate the\nmolecular mechanisms linking these genetic\nvariants with specific phenotypes. A multitude\nof control mechanisms differentially affect\nthe cellular concentrations of different classes of biomolecules.",
      "\tConclusion\n\nOur study represents the first multi-platform approach to the metabolome-wide analyses of diabetes in a general population.The identification of biomarkers allowing prediction of disease progression and its complications from such studies would be certainly beneficial.However, for the caveats discussed above, we feel that this study should be considered as a pilot for future work.One major finding of our work is the identification of a series of known, and also some novel, deregulated metabolites that associate with diabetes under sub-clinical conditions in the general population.These metabolites have been discovered by integrative metabolomics applying different platforms including nuclear magnetic resonance (NMR) and mass spectrometry (MS).Out of the multitude of metabolites measured, a holistic view of differences reflecting global variations in pathophysiology emerges from our study.The coverage of the metabolome's diversity allows the detection of systemic metabolic imbalances, thereby providing a disease-specific picture of human physiology (Figure 3).A pronounced increase in the sample size in future studies will likely allow for further detection of other metabolites of unrecognized associations with diabetic pathways.Finally, our study shows how functional metabolomics can contribute to obtaining a more sophisticated classification of the disease as well as rational optimization of diagnostic and treatment options, as recently suggested by Bain et al. [4].\t\n\nThe principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established.It is not our goal here to show this once again.The questions we ask are rather ''How well are different approaches suited to attain this goal? ''and ''What are optimal settings under which such studies can be successful? ''.Others have already investigated these questions before [16,17,18].However, we believe that this topic is much too complex than to be answered fully in a single study.For instance, the work described in the recent paper in this journal by Lanza et al. [19] covers only a small patient group of 7 cases and 7 controls.Our study, in contrast is based on 40 cases and 60 controls from an epidemiological cohort.Work reviewed recently by Madsen et al. [20] overlaps to some extent with our study, but none of them address aspects related to sub-clinical signals in a general population.Our focus is on participants from epidemiological studies rather than on patients under clinical conditions.Herein, we identify a series of differentially ''expressed'' metabolites that associate with diabetes under sub-clinical conditions in the general population.This question has not been addressed to this extent by any published paper.In particular, we see our work as a pilot that bears the potential of being scaled up to much larger sample sizes, since population studies such as KORA eventually provide access to much larger sample sizes, taken under rigorous standardized blood sample collection conditions in dedicated study centers (e.g.overnight fasting, standard protocol for serum and plasma preparation, storage in liquid nitrogen until measurement).These kinds of samples generally have not been available from clinical studies until recently.It is in this light that we provide here a proof of concept that metabolomics can uncover key metabolites differing in a control and a type 2 diabetic group.",
      "\t\n\nCurrent technologies, such as metabolomics, proteomics, and genomics contribute to the development of a plethora of new biomarkers.In the case of DM, biomarkers may reflect the presence and severity of hyperglycemia or presence and severity of the related complications in diabetes [23].",
      "\t\n\nMetabolomics studies allow metabolites involved in disease mechanisms to be discovered by monitoring metabolite level changes in predisposed individuals compared with healthy ones (Shaham et al, 2008;Newgard et al, 2009;Zhao et al, 2010;Pietilainen et al, 2011;Rhee et al, 2011;Wang et al, 2011;Cheng et al, 2012;Goek et al, 2012).Altered metabolite levels may serve as diagnostic biomarkers and enable preventive action.Previous cross-sectional metabolomics studies of T2D were either based on small sample sizes (Shaham et al, 2008;Wopereis et al, 2009;Zhao et al, 2010;Pietilainen et al, 2011) or did not consider the influence of common risk factors of T2D (Newgard et al, 2009).Recently, based on prospective nested case-control studies with relative large samples (Rhee et al, 2011;Wang et al, 2011), five branched-chain and aromatic amino acids were identified as predictors of T2D (Wang et al, 2011).Here, using various comprehensive largescale approaches, we measured metabolite concentration profiles (Yu et al, 2012) in the population-based (Holle et al, 2005;Wichmann et al, 2005) Cooperative Health Research in the Region of Augsburg (KORA) baseline (survey 4 (S4)) and follow-up (F4) studies (Rathmann et al, 2009;Meisinger et al, 2010;Jourdan et al, 2012).The results of these crosssectional and prospective studies allowed us to (i) reliably identify candidate biomarkers of pre-diabetes and (ii) build metabolite-protein networks to understand diabetes-related metabolic pathways."
    ],
    [
      "\t\nAims/hypothesis Genome-wide association studies (GWAS) for type 2 diabetes have uncovered >400 risk loci, primarily in populations of European and Asian ancestry.Here, we aimed to discover additional type 2 diabetes risk loci (including Africanspecific variants) and fine-map association signals by performing genetic analysis in African populations.Methods We conducted two type 2 diabetes genome-wide association studies in 4347 Africans from South Africa, Nigeria, Ghana and Kenya and meta-analysed both studies together.Likely causal variants were identified using fine-mapping approaches.Results The most significantly associated variants mapped to the widely replicated type 2 diabetes risk locus near TCF7L2 (p = 5.3  10 13 ).Fine-mapping of the TCF7L2 locus suggested one type 2 diabetes association signal shared between Europeans and Africans (indexed by rs7903146) and a distinct African-specific signal (indexed by rs17746147).We also detected one novel signal, rs73284431, near AGMO (p = 5.2  10 9 , minor allele frequency [MAF] = 0.095; monomorphic in most non-African populations), distinct from previously reported signals in the region.In analyses focused on 100 published type 2 diabetes risk loci, we identified 21 with shared causal variants in African and non-African populations.Conclusions/interpretation These results demonstrate the value of performing GWAS in Africans, provide a resource to larger consortia for further discovery and fine-mapping and indicate that additional large-scale efforts in Africa are warranted to gain further insight in to the genetic architecture of type 2 diabetes.",
      "\t\n\nIn 2008, to increase the power of identifying variants with modest effects, a meta-analysis of three GWAS, including Diabetes Genetics Initiative (DGI), Finland-United States Investigation of NIDDM Genetics (FUSION), and Wellcome Trust Case Control Consortium (WTCCC), were conducted.This study detected at least six previously unknown loci that reached genome-wide significance for association with T2D ( < 5  10 8 ), with the loci being JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, THADA, ADAMTS9, and NOTCH2 [19].Genetic variants in JAZF1, CDC123-CAMK1D, TSPAN8-LGR5, and THADA have been reported to affect pancreatic -cell functions [59,60].",
      "\t, for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9\n\nGenome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P  5.0  10 -14 ), CDC123-CAMK1D (P  1.2  10 -10 ), TSPAN8-LGR5 (P  1.1  10 -9 ), THADA (P  1.1  10 -9 ), ADAMTS9 (P  1.2  10 -8 ) and NOTCH2 (P  4.1  10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.\t\n\nBy combining three GWA scans involving 10,128 samples (enhanced through imputation approaches) and undertaking largescale replication in up to 79,792 additional samples, we identified six additional loci that apparently harbor common genetic variants influencing susceptibility to T2D.These findings are consistent with a model in which the preponderance of loci detectable through the GWA approach (using current arrays and indirect LD mapping) have modest effects (ORs between 1.1 and 1.2).Given such a model, our study (in which we followed up only 69 signals out of over 2 million meta-analysed SNPs) would be expected to recover only a subset of the loci with similar characteristics (that is, those that managed to reach our stage 1 selection criteria).Further efforts to expand GWA metaanalyses and to extend the number of SNPs taken forward to largescale replication should confirm additional genomic loci, as should targeted analysis of copy number variation.However, the present data provide only crude estimates of the overall effect on susceptibility attributable to variants at these loci.The effect of the actual common causal variant responsible for the index association (once identified) will typically be larger, and many of these loci are likely to carry additional causal variants, including, on occasion, low-frequency variants of larger effect: three genes with common variants that influence risk of T2D were first identified on the basis of rare mendelian mutations (in KCNJ11, WFS1 and HNF1B).Regardless of effect size, these loci provide important clues to the processes involved in the maintenance of normal glucose homeostasis and in the pathogenesis of T2D.\t\n [3][4][5]7,10 , for the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium 9Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D) [1][2][3][4][5][6][7][8][9][10][11] .Established associations to common and rare variants explain only a small proportion of the heritability of T2D.As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and B2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975.We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P  5.0  10 -14 ), CDC123-CAMK1D (P  1.2  10 -10 ), TSPAN8-LGR5 (P  1.1  10 -9 ), THADA (P  1.1  10 -9 ), ADAMTS9 (P  1.2  10 -8 ) and NOTCH2 (P  4.1  10 -8 ) gene regions.Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.",
      "\t\nDiabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D).The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci.To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ,2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls.Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance.The most significantly associated SNP (rs539514, P = 5.66610 211 ) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22.The second most significantly associated SNP (rs478222, P = 3.50610 29 ) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A.The third most significantly associated SNP (rs924043, P = 8.06610 29 ) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2.These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.",
      "\t\nOBJECTIVE-Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component.To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject-parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals.RESEARCH DESIGN AND METHODS-From our existing datasets, we selected 982 markers with P  0.05 in both GWA cohorts.Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P  0.05 in this third cohort.Among these, six single nucleotide polymorphisms in five novel loci also had P  0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Dia-betes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects.RESULTS-Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P  2.33  10 8 ) and BACH2 (1.13; combined P  1.25  10 6 ).CONCLUSIONS-Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes.The two genes at these respective loci, UBASH3A and BACH2, are both biologically relevant to autoimmunity.",
      "\t\n\nGenome-wide association studies (GWAS) have recently revealed many novel SNPs associated with type 2 diabetes.These include SNPs located in the regions near TCF7L2, HHEX-IDE, EXT2, FTO, SLC30A8, IGF2BP2, CDKAL1, and CDKN2A-CDKN2B [8][9][10][11][12][13].A second phase of studies identified many additional variants, including those near JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, CDC123-CAMK1D, and KCNQ1 [14,15].The two genes in which common variants were previously convincingly associated with type 2 diabetes, PPARG and KCNJ11, were also identified in these GWAS [12,16,17].More recently, numerous other SNPs have been identified in additional GWAS and meta-analyses [18].",
      "\t\n\n. A genome-wide association study identifies novel risk Loci for Type 2 diabetes.Nature 445(7130), 881-885 (2007).31 The Wellcome Trust Case Control Consortium.Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.Nature 447, 661-678 (2007).Twelve Type 2 diabetes susceptibility loci identified through large-scale association analysis.Nat.Genet.42(7), 579-589 (2010).33 SIGMA Type 2 Diabetes Consortium, Williams AL, Jacobs SB, Moreno-Macas H, Huerta-Chagoya A et al.Sequence variants in SLC16A11 are a common risk factor for Type 2 diabetes in Mexico.Nature 506(7486), 97-101 (2014).34 Ma RC, Hu C, Tam CH et al.Genome-wide association study in a Chinese population identifies a susceptibility locus for Type 2 diabetes at 7q32 near PAX4.Diabetologia 56(6), 1291-1305 (2013).35 Hara K, Fujita H, Johnson TA et al.Genome-wide association study identifies three novel loci for Type 2 diabetes.Hum.Mol.Genet.23(1), 239-46 (2014).36 Palmer ND, McDonough CW, Hicks PJ et al.A genomewide association search for Type 2 diabetes genes in African Americans.PLoS ONE 7(1), e29202 (2012).37 Hanson RL, Muller YL, Kobes S et al.A genome-wide association study in American Indians implicates DNER as a susceptibility locus for Type 2 diabetes.Diabetes 63(1), 369-376 (2014).",
      "\t\n\nFigure 1 illustrates the metaanalysis of risk estimates for six of the loci (CDKAL1, CDKN2A/B, HHEX, IGF2BP2, SLC30A8, and KCNQ1), using data from published studies in East Asia, including Chinese populations from China (9, 20 -23) and Hong Kong (10) as well as Korean (7,10,24) and Japanese (6,7,25,26) populations.In essence, the metaanalysis showed that these six diabetes susceptibility loci identified through GWAS are associated with T2DM in populations across Asia.",
      "\t\n\nNovel T2D-associated loci driven by common variants.Beyond the detailed characterization of the known T2D-associated regions, we also identified seven novel loci, among which, five were driven by common variants with modest effect sizes (1.06 < OR < 1.12; Table 1, Fig. 2, Supplementary Fig. 6 and 7).",
      "\t\n\nA meta -analysis of three GWA scans followed by a large -scale replication (Diagram consortium including more than 50 000 individuals in total) has identifi ed additional susceptibility loci for T2DM, with OR ranging from 1.09 to 1.15, near six genes: JAZF1 , CDC123 -CAMK1D , TSPAN8 -LGR5 , THADA , ADAMTS9 and NOTCH2 [174] .Variants at JAZF1 , CDC123 -CAMK1D and TSPAN8 -LGR5 are associated with small alterations in insulin secretion, whereas the mechanisms linking the other loci to T2DM remain to be clarifi ed [175] .In each GWA scan, other loci showed signifi cant associations with T2DM, but were not fol-with Mendelian forms of diabetes, such as MODY, which are caused by rare mutations in the coding sequence resulting in signifi cant amino acid substitutions or truncated proteins, leading to hyperglycemia even in the absence of other diabetogenic exposures.",
      "\tZeggini, E., Scott, L.J. , Saxena, R., Voight, B.F., Marchini, J.L. , Hu, T., de\nBakker, P.I. , Abecasis, G.R. , Almgren, P., Andersen, G., et al. 2008. Metaanalysis of genome-wide association data and large-scale replication\nidentifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40: 638645. Zielenski, J., Corey, M., Rozmahel, R., Markiewicz, D., Aznarez, I., Casals, T.,\nLarriba, S., Mercier, B., Cutting, G.R. , Krebsova, A., et al. 1999. Detection\nof a cystic fibrosis modifier locus for meconium ileus on human\nchromosome 19q13. Nat. Genet. 22: 128129.",
      "\t\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6  10 8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci.\t\n\nMeta-analyses across populations provide further opportunities to detect loci with shared risk alleles.Meta-analysis of 17 418 T2D cases and 70 298 controls from European, African-American, Hispanic-Latino, and Asian studies using a gene-based CardioChip array was first to identify the BCL2 locus for T2D (26).A recent genome-wide trans-ancestry meta-analysis of 26 488 T2D cases and 83 964 controls from European, East Asian, South Asian and Mexican ancestry, with follow-up in an additional 21 491 T2D cases and 55 647 controls of European ancestry, identified seven new T2D loci (48).The trans-ancestry part of this latter study was performed using variants imputed based on genotype data from the International HapMap Project (49), and follow-up was limited to variants available in Metabochip-typed datasets, suggesting that future trans-ancestry meta-analyses incorporating data imputed to denser reference panels will identify additional loci.",
      "\t\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values 10 4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes.",
      "\t\n\nTo identify common type 2 diabetes susceptibility variants, large-scale genome-wide association studies (GWAS) have been conducted in white individuals, yielding more than 60 genetic loci to date [5,6].Although many of these regions have been successfully replicated in Asian populations [7][8][9][10][11], discrepancies in allelic frequencies and effect sizes have demonstrated that interethnic differences exist.GWAS conducted in Japanese individuals [12,13], as well as meta-analyses of GWAS in South Asian [14] and East Asian [15] groups, have revealed additional variants not detected in GWAS with white individuals, with several signals, including KCNQ1, later replicated in many populations [12,13].Previous GWAS in Chinese suggested several loci but lacked large-scale replication [16][17][18].\tDiscussion\n\nThis study reports a meta-analysis of GWAS for type 2 diabetes in a Chinese population, and has identified a novel diabetes-associated locus.Furthermore, we replicated the association in additional East Asian samples, and found an association in samples of European descent.In addition to the multiethnic samples used in our study, our study also benefits from a detailed phenotyping of the Chinese samples, which allowed additional analyses of the effect of the risk variant on clinical traits and the course of disease to be carried out.",
      "\tIdentification of type 2 diabetes loci in 433,540 East Asian individuals\n\nMeta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D) 1,2 ; however, most of these loci have been identified in analyses of individuals with European ancestry.Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals.In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D.Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes.Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells 3 .At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues [4][5][6] .Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.",
      "\t\n\nTo contend with the stringent significance thresholds that account for the number of independent tests performed across the genome, identification of additional T2D susceptibility loci required larger population samples, which was achieved by combining existing GWA studies in meta-analyses.The Diabetes Genetics Replication And Meta-analysis (DIAGRAM, http://www.diagram-consortium.org/) consortium carried out the first meta-analysis for T2D (Zeggini et al. 2008) of three GWA studies of European-descent individuals, including ~4500 cases and 5500 controls.Differences in the genotyping platforms used for individual GWA studies were overcome by imputation using a common variant set based on haplotype structure of densely characterized reference samples in HapMap (Consortium IH 2005) and extended the analysis to ~2.2 million SNPs across the genome  2.1) for each locus listed on the y-axis.Loci are sorted by descending order of per-allele effect size within each year.Colors highlight the discovery study approach: red, candidate gene; yellow, large-scale association; blue, genome-wide association; dark blue, genome-wide association meta-analysis; sky blue, genome-wide meta-analysis with Metabochip follow-up; green, genome-wide meta-analysis of glycemic traits; pink, genome-wide sex-differentiated meta-analysis with larger effects in women; brown, genome-wide sex-differentiated meta-analysis with larger effects in men; hacky, genome-wide meta-analysis in lean/ obese; gray, whole-exome sequencing.For loci with sex differentiation, the effect size for the sex with larger effect is presented.X-axis lists loci names, labeled by the gene names within region.Yaxis shows odds ratio for T2D observed at a given locus.Loci are split by the year of discovery and are ordered from top to bottom by the decreasing OR on T2D risk within each year.Shadow is used for loci from studies with discovery including non-European individuals The DIAGRAM consortium published two further meta-analyses, each based on increasingly larger case-control samples from European populations.The first combined discovery data from 21 GWA studies in up to 8130 individuals with T2D and 38,987 controls all imputed to a HapMap 2 reference panel, followed by large-scale replication in 34,412 cases and 59,925 controls where 13 (11 novel) out of 23 autosomal signals were confirmed (Tables 2.1 and 2.2) (Voight et al. 2010).This meta-analysis was the first to examine T2D associations on chromosome X (taking X-inactivation into account) and identified an association at DUSP9 with a large effect on T2D risk (OR  1.27, Table 2.2; Fig. 2.1) (Voight et al. 2010).The second meta-analysis, in addition to dramatically increasing the sample size (34,840 cases and 114,981 controls), implemented a novel cost-effective strategy for large-scale replication based on the CardioMetabochip (Metabochip), an Illumina iSelect genotyping array.Metabochip, which was designed through collaboration between six GWA consortia studying metabolic and atherosclerotic/ cardiovascular diseases and traits (Voight et al. 2012), permitted follow-up of ~66,000 putative signals for cardiometabolic phenotypes (~5000 of which were selected for T2D) (Morris et al. 2012).The Metabochip array also contained approximately 120,000 SNP probes to fine map 257 established loci in an attempt to identify causal T2D susceptibility variants.The DIAGRAM meta-analysis with Metabochip follow-up established T2D associations at 10 loci (Tables 2.1 and 2.2), including two at CCND2 and GIPR with larger effects on T2D risk in males and females, respectively (Morris et al. 2012).Among previously established T2D loci, sex differentiation in effect size has been shown for KCNQ1, DGKB, and BCL11A (larger effects in males) and GRB14 (larger effects in females)."
    ],
    [
      "\t\n\nThe identification of affected methylation sites is important because it provides evidence that a particular gene is susceptible to being modified by exposure to maternal diabetes.The direction of change is also important because it suggests that the expression and therefore the function of this gene is likely being modified in an inverse manner if the methylation change occurs in promoters or enhancers; however, the epigenome can also be influenced by other factors (such as microRNA and histone modifications), and as such, the direction of DNA methylation change observed in the overlapping genes in our stud may not be as important relative to the fact that the epigenome of a particular gene is susceptible to being altered.",
      "\t\n\nIn addition to changes following exposure to intra-uterine hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia.For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor-g (PPARg) coactivator-1a gene (PPARGC1A) in diabetic islets (Ling et al., 2008).Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients, and correlated with mitochondrial content (Barr es et al., 2009).Epigenetic changes have also been suggested to be responsible for the \"legacy effect\" of reduced risk of vascular complications after a period of sustained tight glucose control, or \"metabolic memory\" of transient hyperglycaemia and increased risk of diabetic vascular injury (Pirola et al., 2010).Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood monocytes of diabetic patients (Miao et al., 2007).In a series of landmark experiments, it was shown that endothelial cells exposed to short-term hyperglycaemia had persistently increased expression of the NF-kB active subunit p65, and was associated with increased promoter H3K4me1 and occupancy by the histone monomethyltransferase SET7/9.In addition, transient hyperglycaemia was also associated with sustained reduction of H3K9 methylation on the NF-kB p65 promoter, as well as recruitment of lysine-specific demethylase (LSD1) (El-Osta et al., 2008;Brasacchio et al., 2009).LSD1 has also been found to regulate H3K4 methylation in vascular smooth muscle cells in hyperglycaemic conditions, and may mediate the vascular inflammation (Reddy et al., 2008).Other epigenetic mechanisms including microRNAs and long noncoding RNAs have also been implicated in the pathogenesis of diabetic complications (Kato et al., 2014).",
      "\tEpigenetic histone modifications and diabetic complications\n\nExciting recent research has demonstrated a role for epigenetic histone modifications in diabetes and its complications.HATs and HDACs have been found to play important roles in the regulation of several key genes linked to diabetes as reviewed by Gray and De Meyts (46).\t\n\nFigure 3: Scheme for the role of epigenetic mechanisms downstream of hyperglycemia in leading to diabetic complications.Diabetic conditions or hyperglycemia can activate several signal transduction pathways and transcription factors that can lead to sustained expression of pathological genes in the nucleus by co-operating with epigenetic factors.This can occur via a loss of repression and a corresponding gain in activation pathways leading to long-lasting epigenetic changes through gene promoter histone lysine modifications near key transcription factor binding sites or other important chromatin regions.Depending on the specific lysineresidue that is methylated, histone lysine methylation is associated with either gene activation (H3K4me) or repression (H3K9me).Modifications at other lysine residues may also be involved.These associations are further complicated by the gene location modified, either promoter or coding region, and the degree of methylation, all of which can affect accessibility of chromatin and transcriptional outcomes.These epigenetic modifications can be maintained through cell division via mechanisms that are not yet clearly understood but may include DNA methylation as well as transmission of histone lysine methylation marks.The persistence of these epigenetic changes might explain the metabolic memory phenomenon responsible for the continued development of diabetic complication even after glucose control has been achieved.\t\n\nFigure 2: Model for epigenetic regulation of pathological gene expression in diabetes via changes in chromatin histone modifications.Post translational modifications on the Nterminal histone tails in chromatin play essential roles in gene regulation and are regulated by various chromatin modifiers.Histone lysine methyltransferases (HMTs) and lysine demethylases (KDMs) regulate histone lysine methylation (Kme), while histone acetyltransferases (HATs) and histone deacetylases (HDACs) control histone acetylation (Ac).In the proposed model shown, various chromatin modifiers maintain sufficient levels of repressive histone marks to maintain strict control of pathologic gene expression under normal conditions;these would include methylation of H3K9 and demethylation of H3K4 in addition to deacetylation by HDACs.However, under diabetic conditions, including hyperglycemia, the\t\n\nHowever, much less is known about DNA methylation in diabetes.A recent report has shown that the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes demethylated as the cells differentiate into insulin expressing cells, and both the human and mouse insulin promoters were specifically demethylated in pancreatic beta cells suggesting epigenetic regulation of insulin expression (81).In the agouti mouse, DNA methylation and expression of the agouti gene can affect the tendency to develop obesity and diabetes (103).\t\n\nmodifications have also been found to play an important role in altering gene expression patterns associated with various diseases(91).Clinical as well as experimental studies with animal and cells models have clearly demonstrated the deleterious effects of hyperglycemia and the importance of maintaining good glucose control to prevent the onset or severity of diabetic complications.In addition, evidence shows that hyperglycemia can induce epigenetic changes to the chromatin structure via activation of various factors and signaling pathways.This has implicated specific key HMTs and KDMs related to active and repressed chromatin states and has demonstated epigenetic regulation of key inflammatory genes in vascular cells.It is highly likely that other HMTs and KDMs, DNA methylation and related chromatin factors are also involved in epigenetic changes induced by elevated glucose in multiple target organs and cells Epigenetic Mechanisms in Diabetic Complications 25 and contribute to metabolic memory of several debilitating diabetic complications (Figure3).However, diabetes is much more complicated than a simple state of hyperglycemia.It is associated with several risk factors and, in particular T2D involves insulin resistance, obesity, dyslipidemia, environmental factors, nutrition, lifestyles and genetics, in addition to hyperglycemia.Each of these risk factors could in itself induce epigenetic changes to the chromatin structure ultimately altering gene expression patterns in conjunction with elevated glucose in various target tissues including kidney, heart, liver, retina, nervous system, muscle, blood vessels and blood cells.Alarming estimates indicate that the rates of diabetes, metabolic syndrome and associated complications are rapidly increasing and therefore additional strategies to curb these trends are needed.With respect to diabetic nephropathy, it is imperative to conduct further exploration into the epigenetic causes and related treatment options, given the widespread prevalence, and the rapid transition to ESRD despite the available therapies.Such information can complement the currently available and new genetic and molecular data to begin the development of personalized medicine for diabetic nephropathy(136) and other complications.Well defined cell and animal models with and without treatments with standard diabetes drugs, antioxidants and related interventions will further our understanding of diabetic complications and metabolic memory and how they might be prevented.Epigenetic drugs such as inhibitors of DNA methylation, HATs and HDACs, and some histone demethylases are already being evaluated for cancer and other diseases(2,129,131).Currently available drugs for diabetic complications(18) could be tested for their potential ability to alter epigenetic marks.In recent years, there has been significant progress in the fields of epigenetics and epigenomics mainly due to increased understanding of basic molecular mechanisms and Epigenetic Mechanisms in Diabetic Complications 26 remarkable advances in powerful genome-wide technologies, instrumentation and bioinformatics software.Thus massive parallel next generation sequencing and ChIP-sequencing have been used to simultaneously map several histone marks and DNA methylation in human adult and stem cells and have demonstrated associations with distinct cell and development states and gene",
      "\tHISTONE PTMS AND DIABETES\n\nHistone PTMs regulate chromatin structure and gene expression by recruiting chromatin remodeling proteins, transcription co-activators, and co-repressors. 26Emerging evidence shows the involvement of key histone PTMs in the regulation of genes associated with the pathogenesis of diabetes.Regulation of insulin gene expression as well as its secretion from islets in response to changing glucose levels is a key process in glucose homeostasis, one that is dysregulated in diabetes.Studies show that the islet-specific TF Pdx-1 can modulate this process of insulin regulation through epigenetic mechanisms. 59In response to increased glucose conditions, Pdx1 recruits co-activator HATs p300 and CBP and a HMT SET7/9 (SET7), which increases activation marks H3/H4Kac and H3K4me2, respectively, at the insulin promoter to promote open chromatin formation accessible to transcription machinery and enhance insulin transcription. 59,60In contrast, under low glucose conditions, Pdx1 recruits corepressors HDAC1 and HDAC2, promoting chromatin compaction and inhibition of insulin expression. 59nterestingly, Pdx-1 also controls the islet-specific expression of SET7 by direct interaction with its promoter. 60Genome-wide mapping of HK4me1, H3K4me3, H3K79me2 in islets revealed several isletspecific promoters and enhancers.Furthermore, several regulatory elements located near diabetes-susceptible loci showed allele-specific differences in their activity. 61Another study also mapped open chromatin regions in islets and identified associations of allele-specific differences in enhancer activity with genetic variations near diabetes-susceptible loci, 62 further highlighting how genetic variations in noncoding regions might affect chromatin structure in diabetes.Histone PTMs along with DNAme also were found to play an important role in epigenetic regulation of Pdx1 and insulin expression in islets of diabetic offspring from intrauterine growth restriction rats, suggesting that histone PTMs can be affected by maternal malnutrition. 34dipogenesis plays an important role in the pathogenesis of metabolic abnormalities and is tightly controlled by the transcription factors CCAAT/ enhancer binding protein (C/EBP)  and peroxisome proliferator activated receptor (PPAR).Dynamic changes in histone PTMs and recruitment of the corresponding modifiers can regulate C/EBP and PPAR-induced gene expression involved in adipocyte differentiation. 63,64Interestingly, epigenetic inactivation of PPAR has been shown in adipocytes from T2D animals, 65 further supporting a role for epigenetic processes in adipocyte dysfunction and T2D.Another study reported increased predisposition to obesity and metabolic syndrome in mice deficient in Jhdm2a, a H3K9me2 demethylase, showing that deficiency in key histone-modifying enzymes might contribute to metabolic abnormalities. 66Overall, these studies highlight how alterations in chromatin structure can contribute to diabetes development.This is clearly a research area likely to show increased activity in the upcoming years.It is possible that epigenetic changes that contribute to the pathology of diabetes also directly or indirectly can affect target organs leading to complications.",
      "\tDNA or Histone Modifications\n\nNew research investigations have addressed the link between epigenetic factors, type 2 DM and CVD.Hyperglycemia, for example, can induce epigenetic changes that lead to the overexpression of genes implicated in vascular inflammation.In particular, hyperglycemia has been shown to activate the NF-kB signaling pathway in cultured THP-1 monocytes, leading to the production of MCP-1 and other inflammatory factors, and to the expression of adhesion molecules in endothelial cells, providing a plausible molecular mechanism for endothelial dysfunction and atherosclerosis (107).On the other hand, clinical studies have demonstrated that early intensive control of glycemia in diabetic patients is crucial to prevent chronic micro-and macrovascular complications, reinforcing the notion that glycemia may have a longstanding influence on clinical outcomes, a phenomenon called \"metabolic memory\" (108).",
      "\t1.5) DNA or Histone Modifications\n\nWe discovered a connection between an epigenetic factor of T2DM and CVD in new research investigations.For instance, hyperglycemia can cause epigenetic alterations that result in the enhanced expression of genes that contribute to vascular inflammation.In particular, it has been demonstrated that hyperglycemia activates the NF-kB signalling pathway in cultured THP-1 monocytes, producing MCP-1 and other inflammatory factors as well as causing endothelial cells to express adhesion molecules.This finding suggests a possible molecular mechanism for endothelial dysfunction and atherosclerosis. [24]On the other hand, clinical investigations have supported the idea that glycemia may have a longlasting impact on clinical outcomes, a condition known as \"metabolic memory,\" by demonstrating that early intensive control of glycemia in diabetes patients is critical to avoid chronic micro-and macrovascular challenges.In aortic endothelial cells, it has been proven that exposure to hyperglycemia corresponds with the opposite acetylation of the histone H3K9/K14 and altered pattern of addition of methyl group to DNA, assisting an epigenetic role for hyperglycemia.Following the temporarily elevation of levels of glucose, numerous histone lysine alterations have also been reported.They could be in charge of the RELA gene's ongoing transcriptional activation, which produces the p65 subunit of NF-kB, even when endothelial cells were later exposed to regular glucose concentrations.Overall, this action caused some target genes associated to endothelial dysfunction to become transcriptionally active, while as a result, other target genes become transcriptionally repressed.ICAM, HMOX1, MCP-1, SLC7A11, MMP10, and MMP1 genes' enhanced expression may also be caused by acetylation or hyperacetylation. [25]However, besides glucose toxicity, plenty of other physiological and pathological mechanisms that might have been involved in hyperglycemia and caused epigenetic modifications to have also been reported.These include ROS, PKC stimulation, and AGEs.Therefore, hyperglycemia is not the only factor that can cause epigenetic modifications.Notably, the CpG decreased intensity of methylation of the p66Shc inducer and a rise in H3 histone acetylation can both be considerably induced by ROS production.So, elevated concentrations of p66Shc, a mitochondrial adaptor that regulates a balance of redox in the cells, and meaningful activation of PKC are related to ROS-induced epigenetic alterations, sustaining endothelial dysfunction and vascular impacts.Additional research has examined the relationships between epigenetic changes and the risk of CVD for cardio-metabolic phenotypes like unusual weight gain, imbalance of lipids, impaired insulin sensitivity, inflammation, and high blood pressure.In a new analysis, histone deacetylases (HDACs) behavior and expression in connection to serum glucose, inflammation, and impaired insulin sesitivity in patients with type 2 DM were measured using peripheral blood mononuclear cells.HDAC3 activity and expression were induced by low-grade long-term inflammation and insulin resistance, and they correlated favourably with circulating levels of TNF-, IL-6, and other proinflammatory markers and adversely with Sirt1 expression. [26]Numerous studies have shown a connection between the addition of methyl group to DNA and the probability of cardiovascular disease.Elevated concentrations of methylation were seen in the predisposing haplotype rs8050136 of the FTO gene, a well-known gene linked to a greater risk of becoming obese and cardiovascular diseases; a similar technique has been proposed for the rs9939609 diversity.IGF2 methylation and changes to the lipid profile were linked in an additional candidate gene analysis of obese individuals.An epigenetic marker of metabolic risk, IGF2 higher intensity of methylation was specifically related to greater triglyceride/HDL cholesterol ratios.Some other investigations that merged genome-wide transcriptome and addition of methyl group to CpG profiling by array observed that insulin-resistant patients' adipose tissue had many more differentially methylated predicted sites than controls, including genes associated in signal transduction and the interaction with principal receptors to bind to the extracellular matrix.been discovered to be heavily and impartially related with impaired insulin sensitivity, were also found to have modified methylation.Furthermore, it has been demonstrated that the addition of methyl group of the PPAR promoter contributes to the division of the adipose tissue macrophages in obese mice from an anti-inflammatory (M2) to a proinflammatory (M1) phenotypic expression.Ultimately, there is scientific proof that modifications in the antenatal environment's impacts on epigenetic modifications may affect the risk of Myocardial infarction. [27]",
      "\tSummary\n\nIncreasing evidence shows that, besides the well-described biochemical mechanisms, epigenetic mechanisms might also participate by fine-tuning gene expression to modulate the aetiology of diabetic complications.Persistence of epigenetic modifications triggered by diabetic stimuli could be one of the key mechanisms underlying metabolic memory.However, the involvement of many epigenetic factors and mechanisms involved in the regulation of the modifications by upstream signal transduction pathways remains unknown.However, this is a rapidly expanding and dynamic field and it is likely that other epigenetic factors related to diabetic complications will soon be uncovered.Epigenomics may also aid in determining the functional roles of complications-associated genetic variants.It would be worthwhile to assess whether lifestyle modifications such as exercise and healthy diets can reduce diabetic complications by altering epigenetic marks.A recent study showed the beneficial effects of exercise on epigenetic marks related to diabetes [106].Because epigenetic changes are potentially reversible in nature, combination therapies with epigenetic drugs (epidrugs) [38] and antagomirs (miRNA inhibitors) [8] could be considered to complement the current treatments for complications.However, there are also key challenges.Since epigenetic patterns are cell specific, data from heterogeneous tissue samples and biopsies could be difficult to interpret.Furthermore, apart from hyperglycaemia, other factors associated with diabetes, including insulin resistance, obesity, dyslipidaemia, environment, lifestyles and genetics, can work independently or co-operatively to also promote epigenetic changes in various affected target tissues.",
      "\t\n\nEmerging evidence suggests an epigenomic link to T2D development.Reversible epigenetic changes such as histone modifications and DNA methylation may occur during intrauterine development and are believed to have long-term effects on offspring health and survival, including manifestation of disease phenotypes such as obesity or diabetes later in life [59,60].Environmental and nutritional stimuli influence future science group Genetics, genomics & personalized medicine in Type 2 diabetes: a perspective on the Arab region Review  [61].Epigenetic regulation of genes may be responsible for the observed difference in T2D risk and drug response between individuals [62,63].Epigenomics may not only shed light on the environmental (including diet and lifestyle) effect on T2D susceptibility in individuals but epigenetic markers may also help identify those at risk well before disease manifestation.Gene-expression analysis or transcriptomics is used for studying the expression profile of genes.A comparative analysis of expression states of genes between healthy and diseased cells can identify those implicated in disease pathology.The changes in expression of disease susceptibility genes can be monitored during different stages of a disease and help in disease prognosis.Similarly, a comparative expression profile for treated and untreated samples can help identify changes in gene-expression upon treatment with a particular drug.This makes geneexpression analysis an important tool for elucidating the role of genes in different biological states, for identifying potential targets for drug intervention and for biomarker selection to be used in disease diagnosis.In diabetes, gene-expression profiling has been used for establishing differential expression of inflammatory genes [64], for studying the effects of insulin treatment in skeletal muscle [65] and more recently for correlating insulin resistance and an altered lipid profile in peripheral blood [66].",
      "\tDNA Methylation and Diabetic Kidney Disease\n\nEpigenetic imprinting is thought to be important for determining the predisposition for chronic and latent diseases, like DKD [5].We have previously shown that exposure of microvascular endothelial cells to hyperglycaemia is able to induce changes in DNA methylation on genome wide ChIP-Seq, Fig. 1 The histone code.The specific site, type, extent and diversity of post-translational modifications histone proteins leads to specific signalling effects, including the repression (red signal) or activation (green signal) of gene expression leading to changes in gene expression, including activation of pro-inflammatory pathways implicated in diabetic complications such as DKD [5, 12, 13].Studies in the zebrafish also demonstrate that hyperglycaemia-induced DNA methylation changes.Diabetes is also induces aberrant DNA methylation in the proximal tubules of the kidney, including key targets implicated in glucose metabolism and transport, leading to a resistance to the effects of pioglitazone [14].However, an elevated glucose level is not the only factor that leads to maladaptive epigenetic modifications in diabetes.DNA methylation can also be influenced by reactive oxygen species, both directly through oxidative modification DNA preventing methylation and indirectly through its effects on methylation writing/erasing enzymes [15].Many other factors including hypoxia, inflammation, cytokines and growth factors, drugs, nutrition and even physical activity can modify epigenetic profiles [16,17]; the sum of which and their interactions being the key determinant of the resulting phenotype.\tHistone Modifications and Diabetic Kidney Disease\n\nPost-translational modification of nucleosomal histones are among the best characterised of epigenetic modifications with respect to diabetes and are clearly implicated in the induction in the expression of genes implicated in DKD [8,24].For example, following exposure to glucose there is persistent transcriptional upregulation of expression of the proinflammatory mediator NF-B (p65; Rel (A)) in vitro and in vivo.This is specifically associated with monomethylation of H3K4 adjacent to the p65 proximal promoter, such that inhibition of Set7-dependent methylation at this site is able to prevent its induction without restoring euglycaemia [8,24].We have also recently reported the persistent induction of other pathogenic genes that may be mediated by H3K4m1 writing events, including the induction of IL-8 following exposure to transient hyperglycaemia [25].Exposure to hyperglycaemia also dynamically changes histone acetylation in cells exposed to hyperglycaemia [12, 13] and diabetic patients.More recently, genome-wide increases in monocyte H3 acetylation were associated with conventional treatment compared with intensive treatment group subjects of the Diabetes Control and Complications Trial (DCCT), indicating a possible mechanism of metabolic memory in humans [26].However, overall transcriptional activity is more likely to be dependent on the sum of multiple histone marks, and their interaction with other epigenetic modifications (e.g.DNA methylation) rather than any individual changes [27].For example, glomerulosclerosis in diabetic mice is associated with enrichment of H3 histones dimethylated at K4, acetylated at K9 and K27, and phosphorylated at S10.",
      "\tEpigenetics, Micro RNAs (miRNAs) and Diet: Are They Involved in DM? Previous epigenetic studies have focused on the heritable alteration of DNA and proteins, linking the DNA and histones, which induces modifications in chromatin structure without changing the nucleotide sequence.Modulations in gene expression can be caused by epigenetic mechanisms such as DNA methylation, histone modifications, small and non-coding RNAs [139].Non-coding RNAs (ncRNAs) have been implicated in the epigenetic regulation of gene expression, and recent studies have shown that miRNAs can induce chromatin remodeling.miRNAs are single-stranded RNA molecules that range in size from 18 to 22 nucleotides.The mammalian genome encodes several hundred miRNAs that fine-tune gene expression through the modulation of target mRNAs [140].These findings suggest that DNA methylation, histone modification and miRNAs may function in concert to regulate gene expression [141].",
      "\t\nThe global diabetes epidemic poses a major challenge.Epigenetic events contribute to the etiology of diabetes; however, the lack of epigenomic analysis has limited the elucidation of the mechanistic basis for this link.To determine the epigenetic architecture of human pancreatic islets we mapped the genome-wide locations of four histone marks: three associated with gene activation-H3K4me1, H3K4me2, and H3K4me3-and one associated with gene repression, H3K27me3.Interestingly, the promoters of the highly transcribed insulin and glucagon genes are occupied only sparsely by H3K4me2 and H3K4me3.Globally, we identified important relationships between promoter structure, histone modification, and gene expression.We demonstrated co-occurrences of histone modifications including bivalent marks in mature islets.Furthermore, we found a set of promoters that is differentially modified between islets and other cell types.We also use our histone marks to determine which of the known diabetes-associated single-nucleotide polymorphisms are likely to be part of regulatory elements.Our global map of histone marks will serve as an important resource for understanding the epigenetic basis of type 2 diabetes.",
      "\t\n\nIn addition to genetic factors, epigenetic mechanisms, such as DNA methylation, histone modifications, chromatin remodeling, and RNA editing and biogenesis have recently emerged as a potential link between gene expression and environmental factors [21].DNA methylation refers to the reversible attachment of a methyl group to a cytosine within cytosine-phosphate-guanine (CpG) dinucleotides [22].In differentiated cells, DNA methylation contributes to the maintenance of normal DNA structure, chromosome stability, and gene regulation [23].DNA methylation regulates gene expression without altering the underlying DNA sequence and is of particular interest because of its emerging role in T2D and its complications [24][25][26][27].We recently showed that aberrant DNA methylation is involved in nerve degeneration in T2D and DPN in a small cohort of patients [24].Specifically, our results highlighted the role of DNA methylation in regulating pathways previously shown to be implicated in DPN pathogenesis, including axon guidance, glycerophospholipid metabolism, and MAPK signaling.However, much less is known about the impact of differential DNA methylation on gene expression in DPN and how the interaction between genetic and epigenetic mechanisms may affect biological pathways during DPN pathogenesis.",
      "\t\n\nDNA methylation can be mitotically stable over time, producing long-term changes in gene expression.The present study suggests that changes in DNA methylation of genes involved in pancreatic development and insulin secretion may result in epigenetic dysregulation of these genes, which may mediate the increased risk of diabetes in individuals exposed to a diabetic intrauterine environment.",
      "\t\n\nSeveral studies show that key histone post-translational modifications are involved in the regulation of genes associated with the pathogenesis of diabetes, such as insulin and islet-specific transcription factors. 48,60In addition, several groups are examining the role of histone post-translational modifications in adipocytes related to type 2 diabetes, obesity and the metabolic syndrome. 48,60hese endeavours highlight the increasing evidence that histone post-translational modifications can have key roles in the pathogenesis of diabetes.Logically, they can be expected to also affect chromatin structure of target genes in organs associated with complications, including the kidney.",
      "\t\n\nEpigenetic mechanisms allow alteration of genome function without mutating the underlying sequence.They involve the interacting actions of DNA methylation (the addition of a methyl group to the 5th carbon position of cytosine), histone modifications and noncoding RNAs [18].A number of indirect lines of evidence point to the involvement of epigenetic changes in diabetic nephropathy.Murine models of disease progression displaying temporal variation in gene expression have indicated these supra-sequence devices may be involved in the pathogenesis [19].Gene expression changes reflect dynamic alterations in gene transcription and also messenger RNA stability, which may be influenced by the epigenetic modification of the genome in response to chronic hyperglycaemic stress.Altered DNA methylation has been additionally implicated in vascular disease [20,21].Furthermore, characteristics observed in diabetic nephropathy such as hyperhomocysteinaemia, dyslipidaemia, inflammation and oxidative stress can promote aberrant DNA methylation [22][23][24]."
    ],
    [
      "\t\nFew concepts in recent years have garnered more disease research attention than that of the intestinal (i.e. 'gut') microbiome.This emerging interest has included investigations of the microbiome's role in the pathogenesis of a variety of autoimmune disorders, including type 1 diabetes (T1D).Indeed, a growing number of recent studies of patients with T1D or at varying levels of risk for this disease, as well as in animal models of the disorder, lend increasing support to the notion that alterations in the microbiome precede T1D onset.Herein, we review these investigations, examining the mechanisms by which the microbiome may influence T1D development and explore how multi-disciplinary analysis of the microbiome and the host immune response may provide novel biomarkers and therapeutic options for prevention of T1D.\t\n\nFew concepts in recent years have garnered more disease research attention than that of the intestinal (i.e. 'gut') microbiome.This emerging interest has included investigations of the microbiome's role in the pathogenesis of a variety of autoimmune disorders, including type 1 diabetes (T1D).Indeed, a growing number of recent studies of patients with T1D or at varying levels of risk for this disease, as well as in animal models of the disorder, lend increasing support to the notion that alterations in the microbiome precede T1D onset.Herein, we review these investigations, examining the mechanisms by which the microbiome may influence T1D development and explore how multi-disciplinary analysis of the microbiome and the host immune response may provide novel biomarkers and therapeutic options for prevention of T1D.\tTherapeutic targeting of the gut microbiome to block T1D progression\n\nExperimental microbiome manipulation in young T1D prone rodents provides robust protection from isletautoimmunity and disease, providing proof of principle that microbial therapy could provide effective protection of individuals with high genetic risk [12].The gut microbiome is extensively remodelled during early postnatal development and throughout childhood and puberty [9,41,42].This natural fluctuation in microbial colonization provides a window of opportunity to modify this risk factor in children with risk markers of anti-islet autoimmunity.\t\n\nBased on the available body of literature, it is feasible to suggest that the well-described increased incidence in T1D over the past 50 years [15,16] arises, at least in part, from one of two primary mechanisms related to the intestinal microbiome.In the first notion (Fig. 1), defective development and/or alteration of healthy microbiota in an individual at genetic risk for T1D may result in abnormal immunoregulation that enables autoimmune destruction of insulin-producing  cells.This notion is supported by evidence suggesting that immune education required for self/ non-self immunoregulation is, to a large degree, conferred early in life, through maturation and education of the immune system by microbiota that colonize the gastrointestinal tract, living symbiotically with the host [18,19].The second concept (Fig. 1), acting either independently of or co-incident with the first, is that enhanced leakiness of the gut epithelial barrier (observed in both human patients and animal models of T1D) either results from an altered microbiome or is a key determinant of an altered microbiome, or 'dysbiosis' [17,20].Either type of microbiome-mediated mechanism could underlie the observed combination of increasing disease incidence as well as the younger age of onset [21], resulting from less robust or delayed maturation of immunoregulation in early childhood.Understanding such mechanisms is an important consideration.Indeed, if a central role for the microbiome in T1D risk was confirmed, as will be discussed later, the disease might be preventable by augmenting or accelerating healthy microbiota-induced immunoregulation, as well as by attenuating intestinal leakiness.However, before undertaking such therapeutic efforts, it would appear critical to determine first whether and how an altered microbiome contributes to either defective immunoregulation and/or gut leakiness in T1D.\tUncovering a pathogenic role for the microbiome in T1D -a proposed pathway forward\n\nAs mentioned previously, interactions between susceptibility genes and environmental determinants of T1D remain poorly defined [16].The most pressing outstanding questions regarding the microbiome as an environmental determinant in T1D are (i): does the microbiome hold any additional clues into disease aetiology, including potential viral or bacterial antigens and metabolites; (ii) is there a microbiome-wide dysbiosis linked to pathogenesis (i.e.development of autoimmunity, progression of autoimmunity, onset of clinical disease); and (iii) is defective microbiome-induced immunoregulation contributing to pathogenesis of T1D?\t\n\n Does altered maturation or development of an adult microbiome or a dysbiotic state contribute to the pathogenesis of human type 1 diabetes, what is the mechanism(s), and when does it occur? Does an altered microbiome or dysbiosis act at the level of initiation of autoimmunity and/or progression of type 1 diabetes? What is the basis of healthy microbiome-induced immunoregulation and does the lack of such contribute to the pathogenesis of human type 1 diabetes? Is altered gut epithelial function and integrity important in the pathogenesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to dysbiosis and how do we demonstrate impaired function in humans? How important are the interactions between host genetics, metabolism and the immune system in shaping the microbiome and predilection to disease? Are faecal samples an appropriate representation of the microbiome for type 1 diabetes studies? What are the most promising type 1 diabetes preventive/therapeutic opportunities targeting the microbiome, microbiome-induced immunoregulation, or microbiome-altered gut permeability?",
      "\t\nAssessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide.To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals.We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses.MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance.An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.\t\n\nAssessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide.To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals.We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses.MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance.An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.",
      "\t\n\nIn Brief Liu et al. identify the gut microbiota as an important determinant in the responsiveness of individuals with prediabetes to exercise for the improvement of glucose metabolism and insulin sensitivity.These findings may help in the implementation of a personalized lifestyle intervention for diabetes prevention.\t\n\nA growing body of evidence suggests that dysbiosis of gut microbiota plays an important role in the pathogenesis of insulin resistance and T2D (Bouter et al., 2017) through multiple mechanisms, including increased gut permeability and low-grade endotoxemia, changes in production of short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs), and perturbation of bile acid metabolism (Utzschneider et al., 2016).Compositional and functional changes of gut microbiota have been observed in individuals with T2D and prediabetes (Allin et al., 2018;Qin et al., 2012), whereas fecal microbial transplantation from healthy donors into patients with metabolic syndrome results in increased microbial diversity and improved glycemic control, as well as insulin sensitivity (Kootte et al., 2017).\t\n\nIn conclusion, our study uncovers gut microbiota and its metabolism as key molecular transducers to the heterogeneous adaption to exercise intervention on glucose metabolism and insulin sensitivity.This finding, together with our demonstration of the predictive value of baseline microbial signatures for individualized responsiveness to exercise, may facilitate clinical implementation of personalized lifestyle intervention for diabetes management.\t\n\nConsidering the important role of the gut microbiota in regulating glucose homeostasis and insulin sensitivity, we next explored whether it was involved in the heterogeneous metabolic effects of exercise in our cohort.",
      "\t\n\nHere, we unraveled novel mechanisms linking gut microbiota changes and metabolism in genetic obese mice and found that prebiotics improved leptin sensitivity in diet-induced leptin-resistant mice.Further work is required to understand the functional links between the metabolic/ catabolic activities of gut bacteria and their impact on host metabolism.For instance, it would be of interest to establish a causal relationship, instead of correlations as shown here, by using transfer of bacterial communities.An alternative experiment would be to analyze intestinal (fecal) microbiota in a time-series study in view of identifying the specific impact of prebiotics and the gut microbes on the onset of obesity and type 2 diabetes.\t\n\nCONCLUSIONS-We conclude that specific gut microbiota modulation improves glucose homeostasis, leptin sensitivity, and target enteroendocrine cell activity in obese and diabetic mice.By profiling the gut microbiota, we identified a catalog of putative bacterial targets that may affect host metabolism in obesity and diabetes.",
      "\t\n\nThe intestinal microbiome also seems to be important to the pathophysiology of type 2 diabetes. 46The microbiome has about 100 times more genetic information than has the human genome, together comprising the human metagenome.Many products of the microbiome provide functions beyond that of the host genome, thereby serving an important role in human physiology.These gut communities are thought to play an important part in several conditions and disorders (eg, obesity and type 2 diabetes), although which bacterial species cause changes to human metabolism is not clear. 47Findings from two studies that used faecal samples suggested that functional changes in the gut microbiome might be directly linked to development of type 2 diabetes; 48,49 however, metagenomic markers diff er between populations, suggesting that their ability to predict development of diabetes will probably vary. 49Findings from a recent proof-of-concept study 50 showed improvements in insulin sensitivity in patients with metabolic syndrome 6 weeks after infusion of intestinal microbiota from lean individuals.Lastly, diff erent gut fl ora might aff ect nutrient absorption, because in human beings nutrient load can alter the faecal bacterial community in a short time. 51he nervous system is another important regulator of metabolic processes.Both sympathetic and parasympathetic nervous systems control glucose metabolism, directly through neuronal input, and indirectly through the circulation to aff ect release of insulin and glucagon 52 and production of hepatic glucose. 53In human beings, the vagus is important in regulation of islets, because severing of this nerve results in impaired insulin secretion. 54The hypothalamus is an important integrator, because its ablation in rats results in dysregulation of  cells and development of hyperinsulinaemia. 557][58] Insulin action at this site is also essential in regulation of bodyweight, with decreased activity leading to obesity. 59Infl ammationinduced neuronal injury occurs rapidly in rodents fed a high-fat diet. 60Findings from imaging studies of obese and lean people suggest that structural changes occur in the hypothalamus, consistent with the occurrence of gliosis in obesity. 60Finally, clock genes expressed in the brain are important in establishment of circadian rhythmicity and, together with sleep, have become a focus of investigation because changes in diurnal patterns and quality of sleep can have important eff ects on metabolic processes. 61,62",
      "\t\n\nOver the last five years, several studies have linked diet/nutrients (mainly dietary fiber), gut microbiota and the expression of genes involved in immune responses.It is well known that the diet has a profound effect on the gut microbiota.In mice and humans, microbes respond differently to dietary components, and long-term dietary habits have been linked to the abundance of certain microbial genera [23].The gut lumen contains large amounts of nutrients that strongly influence the composition of the microbiota, which affects gut immunity.These alterations in gut immunity can precipitate T1DM in individuals prone to T1DM.It has also been observed that diabetes-prone BioBreeding (BBdp) rats housed in specific germ-free (GF) conditions and weaned onto cereal diets displayed an upregulation of the interferon gamma (Ifng) and interleukin 15 (Il15) genes and a downregulation of the forkhead box P3 (Foxp3) gene [24].Both Ifng and IL-15 are proinflammatory cytokines that promote T1DM in non-obese diabetic (NOD) mice [25], whereas Foxp3 is a master transcription factor that directs the differentiation and function of regulatory T cells and plays a central role in the inhibition of autoimmunity and suppression of physiological immune responses [26].When BBdp rats were weaned onto cereal diets and housed in specific pathogen-free conditions (allowing gut microbiota growth), the rats also showed an upregulation of the lymphocyte-specific protein tyrosine kinase (Lck) gene [23].Lck encodes tyrosine kinase/p56, a lymphocyte-specific protein involved in the initiation of T cell activation [27].Finally, in this last condition, BBdp rats showed decreased expression of the cathelicidin antimicrobial peptide (Camp) gene.CAMP is a multifunctional antimicrobial effector and immunomodulatory host defense factor [28], which may alter the gut microbiota.",
      "\t\n\nSpecific microbiome profiles render individuals prone to develop obesity and altered glucose metabolism 313 .The ability to identify protective microbiome profiles might provide a key to the development of obesity and diabetes interventions.It remains to be determined whether specific dietary components are involved in microbiome changes and induce unfavourable transitions.Probiotics or pharmacological manipulation of microbiome elements that favour more 'healthy' flora may prove to be useful in stemming the 'twin epidemics' of obesity and T2DM 313 .Surgical rearrangement of the gastrointestinal tract has shown remarkable efficacy in treating obese patients with T2DM 307,314 .Development of minimally invasive reversible procedures, such as the duodenal sleeve and temporary mucosal barriers, might replace surgery in the near future.",
      "\t\n\nIn conclusion, our data suggest that the levels of glucose tolerance or severity of diabetes should be considered while linking microbiota with obesity and other metabolic diseases in humans.It is especially important for developing the strategies to modify the gut microbiota in order to control metabolic diseases, since obesity and diabetes might be associated with different bacterial populations.\t\n\nBackground: Recent evidence suggests that there is a link between metabolic diseases and bacterial populations in the gut.The aim of this study was to assess the differences between the composition of the intestinal microbiota in humans with type 2 diabetes and non-diabetic persons as control.",
      "\t\n\nIn recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported 1,2 .In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals.Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D).Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis 3,4 .Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin.We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species.Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa 3,4 .These in turn cause functional microbiome shifts, in part alleviated by metformininduced changes.Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication."
    ],
    [
      "\t\n\nIn this review, we limit our summary to data obtained from studies that compared clinical risk scores with scores derived from extended models containing multiple genetic markers for T2D or CVD; we also report the AUCs for the relevant risk models.To assess the issue of prediction, prospective studies are warranted.However, given the scarcity of appropriate studies, our overview includes studies with both prevalent and incident cases, as indicated in Tables 3 and 4.",
      "\t\n\nIn this review, we limit our summary to data obtained from studies that compared clinical risk scores with scores derived from extended models containing multiple genetic markers for T2D or CVD; we also report the AUCs for the relevant risk models.To assess the issue of prediction, prospective studies are warranted.However, given the scarcity of appropriate studies, our overview includes studies with both prevalent and incident cases, as indicated in Tables 3 and 4.",
      "\tSummary and outlook\n\nA lot of work has been performed to assess the incremental value of novel markers, beyond established risk factors, for the prediction of diabetes.Nevertheless, several questions remain to be answered.First, the addition of biomarkers to conventional diabetes risk scores has so far not or, at best, only slightly improved the predictive ability of the models.This raises the question, under which condition novel markers may have a larger incremental value.Often biomarkers are strongly correlated with conventional risk factors so that they do not provide additional predictive information [98,100].While in the near future many novel biomarkers are expected to be described as a result of technological progress, these will only improve diabetes prediction if they are at best weakly correlated with established risk factors.Moreover, it is conceivable that the slope of a biomarker trajectory (the change of the biomarker over time) captures incremental predictive information above the last measurement of the marker alone.However, the potential of trajectories has not yet been assessed for diabetes prediction.\t\n\nThird, beyond optimising the predictive ability of diabetes risk scores, there is a wide range of issues which have not been considered in this review.From a public health perspective, it has to be asked whether diabetes risk scores are accepted by physicians, and which barriers might prevent physicians from using them; how scores are best implemented in clinical practice; to what extent intuitive risk assessments made by physicians are concordant with score-based assessments; and how good is the effectiveness and efficiency of diabetes prediction models.All these questions have hardly been addressed so far.Another issue to consider regarding noneconomic costs relates to false positive test results (which could increase anxiety) and false negative risk estimates (which could lead to false reassurance).Finally, the successful implementation of any prognostic diabetes model will depend on a cost-effective intervention strategy for those persons for whom a high risk of developing type 2 diabetes is diagnosed.This list demonstrates that the assessment of the performance of novel biomarkers in risk models needs to be investigated in a substantially larger context than it is currently before recommendations for their widespread use can be given with certainty.",
      "\tVelu in [12] employed the most emerged three techniques for classification of the\ndiabetic patients, i.e. , EM algorithms, H Means + clustering, and Genetic Algorithm\n(GA) [6]. From their result analysis, H Means + clustering techniques give a better\nresult as compared to other two techniques in case of diabetes disease. Ganji in\n[13] adopted fuzzy ant colony optimization techniques to find the set of rules for the\nadiabatic patient and their diagnosis. Now it is also used for the prima Indian diabetes\ndatasets. Jayalakshmi T. in [14] diagnoses the adiabatic patient through their new\napproachANN techniques.\t: Prediction of diabetes using classification algorithms. Proc. Comput. Sci. 132, 15781585 (2018)\n10. Aljumah, A.A., Ahamad, M.G. , Siddiqui, M.K. : Application of data mining: diabetes health\ncare in young and old patients. J. King Saud Univ. Comput. Inf. Sci. 25(2), 127136 (2013)\n11. Iyer, A., Jeyalatha, S., Sumbaly, R.: Diagnosis of diabetes using classification mining\ntechniques. arXiv preprint arXiv:1502.03774\n12. Velu, C.M. , Kashwan, K.R. : Visual data mining techniques for classification of diabetic patients. In: 2013 3rd IEEE International Advance Computing Conference (IACC), pp. 10701075. IEEE (2013)\n13. Ganji, M.F. , Abadeh, M.S.\tThe analytical process can be done by different machine learning\nalgorithms. This paper presents two sets of machine learning approach for prediction\nof diabetes. One of them is a classification-based algorithm, and the other one is a\nhybrid algorithm. In classification, we have taken the random forest algorithm. For\nhybrid approach, we have chosen XGBoost algorithm. These two algorithms were\nimplemented and compared in order to explore the prediction accuracy in diabetes\nfor two different machine learning approaches and got the mean score 74.10% which\nis better than the Random Forest algorithm.\tIn: International Conference on Remote\nEngineering and Virtual Instrumentation, pp. 306314 (2019)\n17. Aishwarya, R., Gayathri, P., Jaisankar, N.: A method for classification using machine learning\ntechnique for diabetes. Int. J. Eng. Technol. 5, 29032908 (2013)\n18. Rashid, T.A. , Abdulla, S.M. , Abdulla, R.M. : Decision support system for diabetes mellitus\nthrough machine learning techniques. Int. J. Adv. Comput. Sci. Appl. 7, 170178 (2016)\n19. Wang N, Kang G (2012) Monitoring system for type 2 diabetes mellitus. In: IEEE Conference\non E-health Networking, pp. 6267\n20.",
      "\tComputational Insight into Diabetes Research\n\nWhen it comes to machine learning and data mining, significant conclusions are drawn through the present detailed account.It is worth mentioning that the vast majority of the reported articles enhanced classification accuracy, above 80%, in the prediction of DM.With regard to the prediction task itself, almost all of the common known classification algorithms have been employed.However, the most commonly used ones are SVM, ANN, and DT.It should be mentioned that SVM rises as the most successful algorithm in both biological and clinical datasets in DM.A great deal of articles (~85%) used the supervised learning approaches, i.e. in classification and regression tasks.In the remaining 15%, association rules were employed mainly to study associations between biomarkers.More specifically, concerning the part dealing with the evaluation task, in all reported research reports, the identified subsets of biomarkers (features) were evaluated through appropriate procedures, such as splitting the dataset into train and test set or via cross-validation.By analogy, the same approaches have been followed in DM prediction.\t\n\nIn the case of nephropathy, Huang et al. employed a Decision Tree-based prediction tool that combines both genetic and clinical features in order to identify diabetic nephropathy in patients with T2D [81].Leung et al. compared several machine learning methods that include partial least square regression, classification and regression tree, the C5.0 Decision Tree, Random Forest, naive Bayes, neural networks and support vector machines [82].The dataset used consists of both genetic (Single Nucleotide Polymorphisms -SNPs) and clinical data.Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism arose as the most efficient predictors.",
      "\tOverview of the risk assessment algorithms\n\nWe tested a machine-learning approach called Support Vector Machine (SVM, see Methods), as well as logistic regression (LR, see Methods) in order to assess individual disease risk for type 1 diabetes (T1D) using three GWAS datasets (Table 1).SVM is one of the most popular classifiers in the field of machine learning and achieves state-of-the-art accuracy in many computational biology applications [28].In essence, SVM is a supervised machinelearning algorithm that produces a linear boundary to achieve maximum separation between two classes of subjects (cases versus controls), by mathematical transformation (kernel function) of the input features (SNP genotypes) for each subject.Unlike most regression-based methods, SVM allows more input features (such as SNPs or genes) than samples, so it is particularly useful in classifying high-dimensional data, such as microarray gene expression data [29].We also applied LR as a control algorithm, since it is widely used in genetic studies to model the joint effects of multiple variants.Unlike previous disease assessment studies that typically use genotype data from a handful of validated susceptibility loci, we examined a large ensemble of SNP markers with suggestive evidence for association with T1D, using a few Pvalue cutoff thresholds ranging from 1610 23 to 1610 28 , as well as highly stringent quality control measures (see Methods).When more relaxed P-value criteria are being used, the contributing SNPs scatter across the genome; when more stringent criteria are used (P,1610 28 ), only a few independent loci contribute (assuming that all MHC markers represent a single locus).Furthermore, we included the 45 known T1D susceptibility markers [4] into the prediction models to ensure that their predictive values were accounted for.Although these SNP lists may contain some false positive loci that are not genuinely associated with T1D, recent advancements in machine-learning, such as regularization, have made classifiers more tolerant to irrelevant input features [30].Since we cannot completely eliminate falsely associated loci from the list of predictors, our goal is to include them in the prediction models (using various thresholds) and then assess their influence on performance.\tDiscussion\n\nIn this study, we tested the plausibility of building a classifier and using a large number of SNPs for disease risk assessment on three large T1D datasets.In general, the SVM algorithm achieved satisfactory performance when hundreds of SNPs were included in prediction models, with AUC scores of ,0.84 for predicting disease risk for T1D in several GWAS datasets.In contrast, the SVM or the LR algorithm achieved only an AUC score of 0.66-0.68when 45 known T1D susceptibility loci were used.This difference clearly indicates that the predictive value lies in utilizing a large number of SNPs in a sophisticated machine-learning algorithm.We note that another recent study also reported that using thousands of SNPs improve the performance of disease risk assessment compared to using fewer SNPs for diseases studied by WTCCC [39], although the study used a cross-validation design.On the other hand, we observed a decrease in the predictive accuracy when too many SNPs were used, suggesting an upper bound of the number of SNPs for T1D risk assessment before noises from falsely associated markers lead to degraded performance.However, we caution that this upper bound depends on the sample size and the power of the study to rank truly associated SNPs higher than background noises.\t\n\nFigure2.Performance of risk assessment models trained on the CHOP/Montreal-T1D dataset.For both the WTCCC-T1D and the GoKind-T1D datasets, the SVM (support vector machine) algorithm consistently outperforms LR (logistic regression), and the best performance is achieved when SNPs were selected using P-value cutoff of 1610 26 or 161025 .doi:10.1371/journal.pgen.1000678.g002",
      "\tMethodology\n\nThis study is focused on predicting future illnesses such as type-2 diabetes from genomic and tabular data.Genomic data are analyzed for possible gene expression highly likely to be affected by type-2 diabetes.Tabular data from the PIMA dataset with various features are also explored through the proposed RNN model by identifying the feature vector's pivotal features.The proposed model relies on the Deep Neural Networks (DNN) framework for analyzing the genomic data, making the precise assessment of possible future illnesses with better Accuracy than the conventional pattern-matching techniques.DNN is a probabilistic measure that would summarize the possible illness outcome that would better assist in decision-making by the physicians.The working procedure and implementation details are discussed in the current section.The models are trained from the available gene base from scratch initially, and at the later stages, the model learns from the experimental outcomes.\t\n\nVarious studies have been presented to predict future illness through existing patient data using machine learning algorithms.Predicting future illness has become a demanding topic in healthcare [29].Several studies have used machine intelligence techniques to analyze the Pima Indian Diabetes Dataset.C. Yue [30] has investigated various hybrid approaches, including Neural Networks, integrated Quantum Particle Swarm Optimization (QPSO), and Weighted Least Square (WLS) Support Vector Machine (SVM) for diabetes prediction, with the WLS-SVM hybrid model showing a classification accuracy of 82.18%.However, the hybridization model needs considerable effort in the evaluation process.In addition, the SVM model is not suitable for working with larger data [31].Moreover, the SVM model underperforms if the number of attributes for every data point exceeds the training samples.The combinational models for diabetes prediction using Cross-validation and Self-Organizing Maps (SOM) have achieved an accuracy of 78.4% [32,33].SOM can rely on the associated weights of neurons for precise classification.Inappropriate assignment of initial weights may impact the model's performance.A C4.5 technique [34] has been used to analyze the PIMA dataset, attaining an Accuracy of 71.1%.The model works through the entropy value associated with the feature vector.The conventional classification models exhibit poor performance when working with distinct feature vectors [35].\tExperimental Outcome of Genomic Data\n\nThe performance of the proposed RNN model for predicting type 2 diabetes was analyzed using performance evaluation metrics such as sensitivity, specificity, F1 score, Mathews correlation Coefficient, and accuracy measures [76].The above-discussed metrics are assessed through true positive, true negative, false positive, and false negative values approximating experimental outcomes.The dataset is split into a training set and a validation set at a ratio of 70:30.In the following graph, as shown in Figure 7, it is clear that data values are skewed toward data instances, indicating that no diabetes exists.The percentage of available data records of non-diabetic patients (or those who do not have diabetes) is almost double that of diabetic patients.\t\n\nAll the mentioned models rely on tabular datasets such as PIMA and ECG signals [47] in classifying the records with possible diabetic illnesses.The current study considers that genomic data yields a better patient-centric outcome than tabular data.\tResults and Discussion\n\nThe proposed model has been evaluated on genomic data and the tabular data by using the same feature engineering mechanism and the layered approach for predicting the type-2 diabetes.The proposed RNN-based type-2 diabetes is evaluated against genomic and tabular data from the PIMA Indian dataset independently and the evaluations are presented independently in the current section.The model was evaluated against two datasets concerning various evaluation metrics such as sensitivity, specificity, Accuracy, and F1 score.The classification efficiency of the proposed model was assessed using true positive (TuP, the number of times that the model accurately predicted the gene with a high possibility of diabetes correctly), true negative (TuN, identifying the gene with less possibility of diabetes precisely), false positive (FsP, misinterpreting the gene with the high possibility of diabetes as low possibility of diabetes), and false negative (FsN, misinterpreting the low diabetes gene as a high possibility of illness).The sensitivity metric determines the ratio of how many were accurately recognized as positive samples out of how many were truly positive samples in the complete dataset.The specificity measure determines the ratio of how many were recognized as negative samples out of how many among the samples are truly negative from the complete dataset.The Accuracy measures the correctly predicted True positives and Negative samples against the overall sample in the complete dataset.The harmonic mean of sensitivity and specificity measures are determined as the F1 score.MCC is the best single-value classification score for summarizing the confusion matrix.The formulas for the aforementioned metrics are presented through Equations ( 27)-( 32) [75].\tRecurrent Neural Network Model for Type 2 Diabetes Forecasting Based on Genomic Data\n\nPredictions of future illness can be performed through Convolutional Neural Networks (CNN), as stated by Leevy J.L. et al. [51] and Yadav S.S. and Jadhav S. M. [52] using Recurrent Neural Network (RNN) module-based architecture described by SivaSai J.G. et al. [53].CNN model consists of many intermediate nodes connected.Each node is significant in delivering the output following the anticipated outcome.RNN is robust in handling variable-length input sequences with the help of internal auxiliary memory modules [54].The detailed architecture along with the implementation procedure for the proposed approach, is presented in this section.\t\n\nA fuzzy entropy approach for feature selection for a similarity classifier has been evaluated against various medical datasets, such as Pima-Indian diabetes, exhibiting an accuracy of 75.29% [36].A fuzzy model primarily depends on the membership evaluation that requires considerable effort.Non-linearity in evaluating the model will limit the model's performance [37].Genetic Algorithm (GA) with Radial Basis Function Neural Network (RBF NN) has been used in the evaluation process of diabetes data, exhibiting an accuracy of 77.39% over the testing dataset [38].Moreover, for artificial evolutionary algorithms such as GA, the most prohibitive and restricting element is frequently repeated fitness function assessment for complex gene patterns.Hybridization of models with GA would need more computational efforts than neural networks alone.Various cutting-edge technologies for the classification and prediction of type-2 diabetes are presented in Table 1."
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