Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2

BackgroundDiabetes mellitus of type 2 (T2D), also known as noninsulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes, is a common disease. It is estimated that more than 300 million people worldwide suffer from T2D. In this study, we investigated the T2D, pre-diabetic and healthy human (no diabetes) bloodstream samples using genomic, genealogical, and phonemic information. We identified differentially expressed genes and pathways. The study has provided deeper insights into the development of T2D, and provided useful information for further effective prevention and treatment of the disease.ResultsA total of 142 bloodstream samples were collected, including 47 healthy humans, 22 pre-diabetic and 73 T2D patients. Whole genome scale gene expression profiles were obtained using the Agilent Oligo chips that contain over 20,000 human genes. We identified 79 significantly differentially expressed genes that have fold change ≥ 2. We mapped those genes and pinpointed locations of those genes on human chromosomes. Amongst them, 3 genes were not mapped well on the human genome, but the rest of 76 differentially expressed genes were well mapped on the human genome. We found that most abundant differentially expressed genes are on chromosome one, which contains 9 of those genes, followed by chromosome two that contains 7 of the 76 differentially expressed genes. We performed gene ontology (GO) functional analysis of those 79 differentially expressed genes and found that genes involve in the regulation of cell proliferation were among most common pathways related to T2D. The expression of the 79 genes was combined with clinical information that includes age, sex, and race to construct an optimal discriminant model. The overall performance of the model reached 95.1% accuracy, with 91.5% accuracy on identifying healthy humans, 100% accuracy on pre-diabetic patients and 95.9% accuract on T2D patients. The higher performance on identifying pre-diabetic patients was resulted from more significant changes of gene expressions among this particular group of humans, which implicated that patients were having profound genetic changes towards disease development.ConclusionDifferentially expressed genes were distributed across chromosomes, and are more abundant on chromosomes 1 and 2 than the rest of the human genome. We found that regulation of cell proliferation actually plays an important role in the T2D disease development. The predictive model developed in this study has utilized the 79 significant genes in combination with age, sex, and racial information to distinguish pre-diabetic, T2D, and healthy humans. The study not only has provided deeper understanding of the disease molecular mechanisms but also useful information for pathway analysis and effective drug target identification.

[1]  T. Hudson,et al.  A genome-wide association study identifies novel risk loci for type 2 diabetes , 2007, Nature.

[2]  E. Zeggini,et al.  A new era for Type 2 diabetes genetics , 2007, Diabetic medicine : a journal of the British Diabetic Association.

[3]  Ray Burke,et al.  Opportunistic screening for diabetes in routine clinical practice. , 2004, Diabetes care.

[4]  Min Xu,et al.  Significance of serum microRNAs in pre-diabetes and newly diagnosed type 2 diabetes: a clinical study , 2011, Acta Diabetologica.

[5]  Mark I McCarthy,et al.  Genome-wide association studies in type 2 diabetes , 2009, Current diabetes reports.

[6]  Thomas M. Aune,et al.  Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes. , 2011, Genes and immunity.

[7]  Yoav Ben-Shlomo,et al.  A putative functional polymorphism in the IGF-I gene: association studies with type 2 diabetes, adult height, glucose tolerance, and fetal growth in U.K. populations. , 2002, Diabetes.

[8]  Senlin Luo,et al.  The characteristics of impaired fasting glucose associated with obesity and dyslipidaemia in a Chinese population , 2010, BMC public health.

[9]  N. Agrawal,et al.  Association of Variant rs7903146 (C/T) Single Nucleotide Polymorphism of TCF7L2 Gene With Impairment in Wound Healing Among North Indian Type 2 Diabetes Population , 2013, The international journal of lower extremity wounds.

[10]  Eric J. Brunner,et al.  Elevated Levels of the Anti-Inflammatory Interleukin-1 Receptor Antagonist Precede the Onset of Type 2 Diabetes , 2009, Diabetes Care.

[11]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[12]  C. Boissonnet,et al.  High prevalence of diabetes and impaired fasting glucose in urban Latin America: the CARMELA Study , 2009, Diabetic medicine : a journal of the British Diabetic Association.

[13]  H Katagiri,et al.  No correlation of plasma cell 1 overexpression with insulin resistance in diabetic rats and 3T3-L1 adipocytes. , 1999, Diabetes.

[14]  M. Isa,et al.  Prevalence of diabetes in the Malaysian National Health Morbidity Survey III 2006. , 2010, The Medical journal of Malaysia.

[15]  M. Marazita,et al.  Genome-wide Association Studies , 2012, Journal of dental research.

[16]  C. Palmer,et al.  Novel Insights Into the Etiology of Diabetes From Genome-Wide Association Studies , 2009, Diabetes.

[17]  Jenna Mandel-Ricci,et al.  Prevalence and Control of Diabetes and Impaired Fasting Glucose in New York City , 2009, Diabetes Care.

[18]  A. Aplogan,et al.  From the Centers for Disease Control and Prevention , 2015 .

[19]  V. Mootha,et al.  Metabolite profiles and the risk of developing diabetes , 2011, Nature Network Boston.

[20]  V. Mootha,et al.  Metabolite profiles and the risk of developing diabetes , 2011, Nature Medicine.

[21]  References , 1971 .

[22]  Youping Deng,et al.  Integrated lipidomics and transcriptomic analysis of peripheral blood reveals significantly enriched pathways in type 2 diabetes mellitus , 2013, BMC Medical Genomics.

[23]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[24]  Maria Inês Schmidt,et al.  Fasting Plasma Glucose Is a Useful Test for the Detection of Gestational Diabetes , 1998, Diabetes Care.

[25]  Christian Gieger,et al.  Novel biomarkers for pre-diabetes identified by metabolomics , 2012, Molecular systems biology.

[26]  T. Frayling.  Genome–wide association studies provide new insights into type 2 diabetes aetiology , 2007, Nature Reviews Genetics.

[27]  R Holle,et al.  Inflammation and Type 2 Diabetes: Results from KORA Augsburg , 2005, Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)).