HLA-associated susceptibility to type 2 (non-insulin-dependent) diabetes mellitus: the Wadena City Health Study

Virus-induced diabetes mellitus: revisiting infection etiology in light of SARS-CoV-2

Diabetes mellitus (DM) is comprised of two predominant subtypes: type 1 diabetes mellitus (T1DM), accounting for approximately 5 % of cases worldwide and resulting from autoimmune destruction of insulin-producing β-cells, and type 2 (T2DM), accounting for approximately 95 % of cases globally and characterized by the inability of pancreatic β-cells to meet the demand for insulin due to a relative β-cell deficit in the setting of peripheral insulin resistance. Both types of DM involve derangement of glucose metabolism and are metabolic diseases generally considered to be initiated by a combination of genetic and environmental factors. Viruses have been reported to play a role as infectious etiological factors in the initiation of both types of DM in predisposed individuals. Among the reported viral infections causing DM in humans, the most studied include coxsackie B virus, cytomegalovirus and hepatitis C virus. The recent COVID-19 pandemic has highlighted the diabetogenic potential of SARS-CoV-2, rekindling interest in the field of virus-induced diabetes (VID). This review discusses the reported mechanisms of viral-induced DM, addressing emerging concepts in VID, as well as highlighting areas where knowledge is lacking, and further investigation is warranted.

Class I MHC Polymorphisms Associated with Type 2 Diabetes in the Mexican Population

Type 2 diabetes (T2D) has been linked to the expression of Human Leukocyte Antigens, principally to the Major Histocompatibility Complex Class II, with only scarce reports of Major Histocompatibility Complex Class I in specific populations. The objective of the present work was to explore the presence of polymorphisms in the MHC Class I related to T2D in the Mexican population using the Genome-Wide Association Studies Slim Initiative in Genomic Medicine of the Americas (GWAS SIGMA) database. This database contains information on 3848 Mexican individuals with T2D and 4366 control individuals from the same population without a clinical or hereditary history of the disease. The searching criteria considered a p-value of <0.005 and an odds ratio (OR) of >1.0. Ten novel, statistically significant nucleotide variants were identified: four polymorphisms associated with HLA-A (A*03:01:01:01) and six with HLA-C (C*01:02:01:01). These alleles have a high prevalence in Latin American populations and could potentially be associated with autoimmunity mechanisms related to the development of T2D complications.

Evaluation of the Host Genetic Effects of Tuberculosis-Associated Variants Among Patients With Type 1 and Type 2 Diabetes Mellitus

Background

Understanding the link between tuberculosis (TB) and diabetes is increasingly important as public health responds to the growing global burden of noncommunicable diseases. Genetic association studies have identified numerous host genetic variants linked to TB; however, potential host genetic mechanisms linking TB and diabetes remain unexplored.

Methods

We used genetic and phenotypic data from the UK Biobank to evaluate the association of 6 previously reported TB-related host genetic variants (genome-wide significant associations from published studies) with diabetes. The study included 409692 adults of European ancestry including 2177 with type 1 diabetes mellitus (T1DM) and 13976 with type 2 diabetes mellitus (T2DM), defined by ICD-10 diagnosis codes.

Results

Of the 6 TB-associated single nucleotide polymorphisms (SNPs), 2 were associated with T1DM and 3 with T2DM, after adjusting for age, sex, body mass index, smoking, alcohol use, and population structure. After correction for multiple testing, SNPs rs2894257 and rs3135359 (HLA-DRA-DQA1) were associated with T1DM (rs2894257: odds ratio [OR], 1.32; 95% confidence interval [CI], 1.21–1.45; rs3135359: OR, 1.72; 95% CI, 1.57–1.88) and T2DM (rs2894257: OR, 1.11; 95% CI, 1.08–1.15; rs3135359: OR, 1.06; 95% CI, 1.025–1.096). The associations with T2DM weakened for rs2894257 and rs3135359 after further exclusion of probable T1DM cases defined by International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes. SNP rs4733781 on chromosome 8 (ASAP1 gene) was associated with T2DM after exclusion of T1DM cases.

Conclusions

Our findings suggest that common host genetic effects may play a role in the molecular mechanism linking TB and diabetes. Future large genetic studies of TB and diabetes should focus on developing countries with high burdens of infectious and chronic diseases.

HLA Class II Allele Analyses Implicate Common Genetic Components in Type 1 and Non-Insulin-Treated Type 2 Diabetes

CONTEXT

Common genetic susceptibility may underlie the frequently observed co-occurrence of type 1 and type 2 diabetes in families. Given the role of HLA class II genes in the pathophysiology of type 1 diabetes, the aim of the present study was to test the association of high density imputed human leukocyte antigen (HLA) genotypes with type 2 diabetes.

OBJECTIVES AND DESIGN

Three cohorts (Ntotal = 10 413) from Leipzig, Germany were included in this study: LIFE-Adult (N = 4649), LIFE-Heart (N = 4815) and the Sorbs (N = 949) cohort. Detailed metabolic phenotyping and genome-wide single nucleotide polymorphism (SNP) data were available for all subjects. Using 1000 Genome imputation data, HLA genotypes were imputed on 4-digit level and association tests for type 2 diabetes, and related metabolic traits were conducted.

RESULTS

In a meta-analysis including all 3 cohorts, the absence of HLA-DRB5 was associated with increased risk of type 2 diabetes (P = 0.001). In contrast, HLA-DQB*06:02 and HLA-DQA*01:02 had a protective effect on type 2 diabetes (P = 0.005 and 0.003, respectively). Both alleles are part of the well-established type 1 diabetes protective haplotype DRB1*15:01~DQA1*01:02~DQB1*06:02, which was also associated with reduced risk of type 2 diabetes (OR 0.84; P = 0.005). On the contrary, the DRB1*07:01~DQA1*02:01~DQB1*03:03 was identified as a risk haplotype in non-insulin-treated diabetes (OR 1.37; P = 0.002).

CONCLUSIONS

Genetic variation in the HLA class II locus exerts risk and protective effects on non-insulin-treated type 2 diabetes. Our data suggest that the genetic architecture of type 1 diabetes and type 2 diabetes might share common components on the HLA class II locus.

Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus

BACKGROUND

This study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM).

METHODS

The GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA-transcription factor (TF)-DEG regulatory network was analyzed.

RESULTS

In total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted.

CONCLUSIONS

Four candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease.

Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94) Collapse

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MESH Headings

• Black or African American/genetics
• Diabetes Mellitus, Type 2/genetics
• Diabetes Mellitus, Type 2/pathology
• Genome-Wide Association Study
• HLA-B27 Antigen/genetics
• HMGA2 Protein/genetics
• Humans
• KCNQ1 Potassium Channel/genetics
• Mutant Chimeric Proteins/genetics
• Polymorphism, Single Nucleotide
• Transcription Factor 7-Like 2 Protein/genetics

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Affiliation(s)

Maggie C. Y. Ng Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Daniel Shriner Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Brian H. Chen Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Center for Metabolic Disease Prevention, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
Jiang Li Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wei-Min Chen Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Xiuqing Guo Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Jiankang Liu Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Suzette J. Bielinski Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
Lisa R. Yanek The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Michael A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Mary E. Comeau Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Laura J. Rasmussen-Torvik Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Richard A. Jensen Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America
Daniel S. Evans San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Yan V. Sun Department of Epidemiology and Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America
Ping An Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Sanjay R. Patel Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
Yingchang Lu The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Jirong Long Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Loren L. Armstrong Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Lynne Wagenknecht Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Lingyao Yang Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Beverly M. Snively Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Nicholette D. Palmer Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Poorva Mudgal Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Carl D. Langefeld Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Keith L. Keene Department of Biology, Center for Health Disparities, East Carolina University, Greenville, North Carolina, United States of America
Barry I. Freedman Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Josyf C. Mychaleckyj Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Uma Nayak Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Leslie J. Raffel Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Mark O. Goodarzi Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Y-D Ida Chen Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Herman A. Taylor Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America Jackson State University, Tougaloo College, Jackson, Mississippi, United States of America
Adolfo Correa Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Mario Sims Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
David Couper Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
James S. Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
Eric Boerwinkle Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Adebowale Adeyemo Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Ayo Doumatey Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Guanjie Chen Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Rasika A. Mathias The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Division of Allergy and Clinical Immunology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Dhananjay Vaidya The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
Andrew B. Singleton Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Alan B. Zonderman Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
Robert P. Igo Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
John R. Sedor Department of Medicine, Case Western Reserve University, MetroHealth System campus, Cleveland, Ohio, United States of America Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, United States of America
the FIND Consortium
Edmond K. Kabagambe Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
David S. Siscovick Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
Barbara McKnight Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Kenneth Rice Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wen-Chi Hsueh Department of Medicine, University of California, San Francisco, California, United States of America
Wei Zhao Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Lawrence F. Bielak Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Aldi Kraja Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Michael A. Province Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Erwin P. Bottinger The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Omri Gottesman The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Qiuyin Cai Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Wei Zheng Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
William J. Blot Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee; International Epidemiology Institute, Rockville, Maryland, United States of America
William L. Lowe Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Jennifer A. Pacheco Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Dana C. Crawford Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
the eMERGE Consortium
the DIAGRAM Consortium
Elin Grundberg Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
the MuTHER Consortium
Stephen S. Rich Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
M. Geoffrey Hayes Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Xiao-Ou Shu Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Ruth J. F. Loos The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Ingrid B. Borecki Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Patricia A. Peyser Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Steven R. Cummings San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Bruce M. Psaty Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America Department of Health Services, University of Washington, Seattle, Washington, United States of America
Myriam Fornage Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Sudha K. Iyengar Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
Michele K. Evans Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore Maryland, United States of America
Diane M. Becker The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
W. H. Linda Kao Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
James G. Wilson Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Michèle M. Sale Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
Simin Liu Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Department of Epidemiology, University of California Los Angeles, Los Angeles, California, United States of America Departments of Epidemiology and Medicine, Brown University, Providence, Rhode Island, United States of America
Charles N. Rotimi Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Donald W. Bowden Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
for the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium

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No association of the SUMO4 polymorphism M55V variant in type 2 diabetes in Iranian subjects

INTRODUCTION

Diabetes mellitus incidence has an increasing rate and it's genetic aspect is an important approach as a risk factor and predictive value in this disorder. In some population, SUMO4, a regulator of NF-κB, gene polymorphism is associated with diabetes. A single-nucleotide polymorphism was detected in SUMO4; substituting a highly conserved methionine with a valine residue (M55V). We studied the association between M55V polymorphism in the SUMO4 gene insusceptibility of type 2 diabetes in patients with type 2 diabetes.

MATERIALS AND METHODS

Participants were 50 patients with type 2 diabetes and 50 control Iranian subjects. Genotyping was done using polymorphism chain reaction (PCR) technique and subsequent cleavage by restriction endonuclease (RFLP) for the M55V SUMO4 gene variant.

RESULTS

The frequency of SUMO4 AA, AG and GG were 13%, 25% and 12% in control group and 20%, 22%, 18% in the type 2 diabetes patients respectively. The SUMO4 M55V variant was not associated with the susceptibility of type 2 diabetes.

CONCLUSION

The study indicates that the SUMO4 gene M55V variant was not associated with the susceptibility of the type 2 diabetes polymorphism.

Immunogenetic susceptibility to diabetes mellitus in patients with liver disease

BACKGROUND/AIM

Genetic, environmental, metabolic and infectious influences, such as hepatitis C virus (HCV) infection, are thought to impact on the development of diabetes in patients with liver disease. As specific human leucocyte antigen (HLA) alleles provide the major genetic risk factors for type 1 diabetes, our aim was to investigate whether HLA class I and II alleles constitute additional risk factors for diabetes in patients with liver disease.

METHODS

We evaluated two independent databases of 193 and 728 adult patients with chronic liver disease for the diagnosis of diabetes and the presence of specific HLA subtypes.

RESULTS

In each database, 24 and 19% of patients met criteria for diabetes. In the first database, specific class I and II alleles were observed more frequently in diabetics compared with non-diabetics: Cw7 (50 vs. 32%, P=0.04), DR51 (17 vs. 3%P=0.003) and DQ6 (37 vs. 18%, P=0.02). In the second database, DQ6 was observed in 16% of diabetics vs. 8% of non-diabetics (P=0.04). The DR2-DR51-DQ6 haplotype was higher in patients with diabetes in both databases (22 vs.7%, P=0.02 and 12 vs. 5%, P=0.02). In a subgroup analysis of patients with HCV infection, increased frequencies of Cw7, DR2/DR51, DQ6 and DR2-DR51-DQ6 were also observed to be higher in subjects with diabetes compared with those without diabetes.

CONCLUSIONS

Patients with chronic liver disease, especially those with HCV infection, have an immunogenetic risk for diabetes characterized by the presence of Cw7, DR51, DQ6 and DR2-DR51-DQ6.

Association of small ubiquitin-like modifier 4 (SUMO4) variant, located in IDDM5 locus, with type 2 diabetes in the Japanese population

CONTEXT

Despite distinct differences in the pathogenesis, epidemiological data have indicated familial clustering of type 1 and type 2 diabetes, suggesting a common genetic basis between these two types of diabetes. Few shared susceptibility genes, however, have been reported to date.

OBJECTIVE

Small ubiquitin-like modifier 4 (SUMO4) has been identified as a candidate gene for the IDDM5 locus and suggested to have possible involvement in immune responses, such as autoimmunity and inflammation. Recent reports demonstrated that a polymorphism with an amino acid substitution (Met55Val) in SUMO4 was associated with type 1 diabetes in Asian populations, although no association was reproduced in subjects of Caucasian descent. The present study aimed to clarify the contribution of SUMO4 to type 2 diabetes susceptibility in the Japanese population.

SUBJECTS

The 753 subjects included 355 cases and 398 control subjects.

METHODS

The SUMO4 Met55Val (rs237025) and 001Msp (rs577001) polymorphisms were genotyped.

RESULTS

Strong linkage disequilibrium (D': 1.0 in each pair of single-nucleotide polymorphisms) across the MAP3K7IP2/SUMO4 region was shown in the Japanese population. The frequency of genotypes with the G allele of the SUMO4 Met55Val polymorphism was significantly higher in patients with type 2 diabetes [odds ratio, 1.46; 95% confidence interval (CI), 1.08-1.93; P = 0.01, chi(2) test]. The association was concentrated in patients without insulin therapy (odds ratio, 1.56; 95% CI, 1.13-2.15; P = 0.0072), but not in those with insulin (odds ratio, 1.24; 95% CI, 0.81-1.89; not significant).

CONCLUSIONS

These data, together with previous reports, suggest the contribution of the SUMO4 Met55Val polymorphism to both type 1 and type 2 diabetes susceptibility in the Japanese population.

Interaction of KIR Genes and G1M Immunoglobulin Allotypes Confer Susceptibility to Type 2 Diabetes in Puerto Rican Americans

The susceptibility to type 2 diabetes (T2D) involves genetic factors. We studied the distribution of KIR and MHC class I ligands phenotype and genotype frequencies, as well as immunoglobulin KM and GM allotype frequencies in a group of patients (N = 95) with T2D and ethnically matched healthy controls (N = 74) with Puerto Rican ethnic background. We found a slight increase of the 2DL3/2DL3 homozygous genotype in T2D. Moreover, the association between 2DL3/2DL3 genotype was significant in the presence of 2DS4 (pC = 0.01). Also, we observed an epistatic effect of the interaction of 2DL3/2DL3, 2DS4 with allele z of G1M in T2D (pC = 0.004, OR = 3.60, 95% CI, 1.62-8.10). This genetic interaction between KIR and G1M allotypes, associated with T2D, was also significant by multiple logistic regression analysis (p < 0.0001, OR = 4.90, 95% CI, 2.12-11.3). We did not detect population stratification using unlinked short tandem repeat (STR) markers, demonstrating that the patients and controls were ethnically matched. Hence, we have demonstrated in this study an epistatic interaction between KIR genes and the G1M allotype that influences the susceptibility to T2D in Puerto Rican Americans. Our findings are important for understanding the autoimmune or innate immune inflammatory-mediated mechanisms involved in the pathogenesis of T2D.

Type 1 and type 2 diabetes: what do they have in common?

Type 1 and type 2 diabetes frequently co-occur in the same families, suggesting common genetic susceptibility. Such mixed family history is associated with an intermediate phenotype of diabetes: insulin resistance and cardiovascular complications in type 1 diabetic patients and lower BMI and less cardiovascular complications as well as lower C-peptide concentrations in type 2 diabetic patients. GAD antibody positivity is more common in type 2 diabetic patients from mixed families than from common type 2 diabetes families. The mixed family history is associated with more type 1-like genetic (HLA and insulin gene) and phenotypic characteristics in type 2 diabetic patients, especially in the GAD antibody-positive subgroup. Leaving out the extreme ends of diabetes phenotypes, young children progressing rapidly to total insulin deficiency and strongly insulin-resistant subjects mostly with non-Europid ethnic origin, a large proportion of diabetic patients may have both type 1 and type 2 processes contributing to their diabetic phenotype.

An immune origin of type 2 diabetes?

Subclinical, low-grade systemic inflammation has been observed in patients with type 2 diabetes and in those at increased risk of the disease. This may be more than an epiphenomenon. Alleles of genes encoding immune/inflammatory mediators are associated with the disease, and the two major environmental factors the contribute to the risk of type 2 diabetes-diet and physical activity-have a direct impact on levels of systemic immune mediators. In animal models, targeting of immune genes enhanced or suppressed the development of obesity or diabetes. Obesity is associated with the infiltration and proinflammatory activity of macrophages in adipose tissue, and immune mediators may be important regulators of insulin resistance, mitochondrial function, ectopic lipid storage and beta cell dysfunction or death. Intervention studies targeting these pathways would help to determine the contribution of an activated innate immune system to the development of type 2 diabetes.

Common genetic basis between type 1 and type 2 diabetes mellitus indicated by interview-based assessment of family history

To investigate the intrafamilial clustering of type 1 and type 2 diabetes, an interview-based assessment of family history of diabetes was conducted. Outpatients with either type 1 (n = 23) or type 2 diabetes (n = 124), and non-diabetic subjects (n = 118) received an interview regarding the diabetic status of each of their family members. In patients with type 1 diabetes, 22% (5 out of 23) had a parental history of diabetes, and diabetes in these 5 parents was assessed as type 2 diabetes mellitus. The prevalence of parental diabetes in the type 1 diabetic probands (22%) was significantly higher (P < 0.05) than that in non-diabetic probands (7%, 8 out of 118). In probands with type 2 diabetes, the prevalence of parental diabetes was 39% (48 out of 124), which was significantly higher (P < 0.0005) than that in the non-diabetic probands (7%). In the type 2 diabetic probands, no significant difference was noted in the prevalence between paternal (19%, 23 out of 124) and maternal diabetes (23%, 28 out of 124), suggesting no preferential inheritance of maternal diabetes in this population. The present interview-based assessment of family history of diabetes suggested a common genetic basis between type 1 and type 2 diabetes.

Type 1 diabetes in the offspring does not increase the risk of parental type 2 diabetes in South Indians

OBJECTIVES

(a) To study whether there was an increased prevalence of glucose intolerance in the parents of probands with Type 1 diabetes and (b) to look for any possible link between the glucose intolerance in the parents with HLA-DQB1 alleles transmitted in excess to the Type 1 diabetes offspring. Study Design and Methods From 215 families of South Indian Type 1 diabetes probands, 336 parents (170 fathers, age 30-70 years; 166 mothers, age 23-72 years) were studied by oral glucose tolerance test (GTT). Glucose intolerance in the parents was compared with the population data available. HLA-DQB1 alleles in 170 of the families were studied by the Olerup method (based on sequence specific primers) and the transmission disequilibrium test (TDT) was used to determine the Type 1 diabetes-associated DQB1 alleles.

RESULTS

Among the parents 11.2% had Type 2 diabetes which was similar to the population data of 11.6%. However there was a male predominence among the diabetic parents (chi(2)=7.0, p=0.008), while in the population there was a female predominence. Prevalence of IGT was significantly more among the parents (13.6%) compared with the population data (9.1%) (chi(2)=6.43, p=0.011). Both HLA-DQB1*0201 (p<0.0001) and DQB1*0302 (p=0.0001) were positively associated with Type 1 diabetes in the probands although 21% of the probands possessed neither DQB1*0201 or DQB1*0302. The distribution of glucose tolerance categories in the parents of the probands differed according to the presence of DQB1*0302 (p= 0.035) whilst no such differences existed for DQB1*0201.

CONCLUSIONS

In summary, the presence of Type 1 diabetes in the South Indian offspring does not predict a higher occurrence of Type 2 diabetes in the parents. However, there is an increased occurrence of impaired glucose tolerance (IGT) among the parents. Family based studies demonstrate increased transmission of HLA-DQB1*0201 and HLA-DQB1*0302 with Type 1 diabetes similar to North American and European Caucasian subjects. Furthermore, HLA-DQB1*0302 may be a minor determinant of glucose tolerance in parents of offspring with Type 1 diabetes.

Non-insulin-dependent diabetes mellitus--a collision between thrifty genes and an affluent society

Non-insulin-dependent diabetes mellitus (NIDDM) is one of the most common non-communicable diseases in the world. It has become obvious that NIDDM is the result of a collision between thrifty genes and an affluent society. Genes predisposing to NIDDM might have been survival genes for our ancestors, helping them to store energy during long periods of starvation. When these genes are exposed to a sedentary lifestyle and high caloric intake typical to the Western world, they predispose to obesity and insulin resistance. NIDDM results when beta cells cannot compensate for insulin resistance by increasing insulin secretion. Therefore, at least two inherited defects can be expected in NIDDM, one causing obesity and insulin resistance and the other inability to increase insulin secretion. In reality there may be more inherited defects. It has become quite clear that diabetes cannot simply be divided into NIDDM and insulin-dependent diabetes mellitus (IDDM). The disease is more heterogeneous; unmasking this heterogeneity and identifying new subgroups of diabetes presents a challenge to modern molecular biology.

GAD 65 antibody but not ICA positivity in adult-onset diabetic patients is associated with early progression to clinical insulin dependency

Correct classification of diabetic patients in adulthood at the time of diagnosis is often difficult. Some may be initially diagnosed as having non-insulin-dependent diabetes mellitus and be treated with diet and/or oral hypoglycaemic agents (OHA) but later require insulin treatment. Islet cell antibodies and antibodies to GAD 65 have been associated with the development of insulin deficiency in this group of patients. In the present study, 150 patients with the initial diagnosis of type 2 diabetes mellitus in adulthood (30-60 years) were seen regularly over a period of 5 years in our diabetes outpatient clinic. Though treatment was started with diet or diet plus OHA, insulin therapy had to be introduced in a subset of patients. In all cases, serum obtained at the time of the initial diagnosis was analysed for islet cell antibodies and GAD 65 antibodies, as well as for thyroid and adrenal autoantibodies as possible markers for polyendocrine involvement. Islet cell antibody status, body mass index and the presence of thyroid and adrenal autoantibodies showed no significant correlation to subsequent insulin requirement (< 2 years after diagnosis). In contrast, GAD 65 antibodies were significantly associated with the occurrence of clinical insulin dependency less than 2 years after the initial diagnosis (P < 0.01), thus identifying a substantial proportion of patients requiring insulin therapy within the first 2 years after the diagnosis of type 2 diabetes. Determination of GAD 65 antibodies in patients with late-onset diabetes may contribute to their correct classification and adequate treatment.

Insulin receptor substrate-1 gene mutations in NIDDM; implications for the study of polygenic disease

Variations in the coding regions of the insulin receptor substrate-1 (IRS-1) gene have recently been suggested to contribute to the susceptibility of non-insulin-dependent diabetes mellitus (NIDDM). The purpose of this study was to examine the role of the IRS-1 missense mutations at codons 972 (glycine to arginine) and 513 (alanine to proline) in two diverse populations from South India and Finland at high risk for NIDDM. DNA was amplified and digested with restriction enzymes BstN1 to detect the codon 972 mutation and Dra III to detect the codon 513 mutation. The codon 513 mutation was not found in the study subjects. The codon 972 mutation was present in 10.3% of 126 middle-aged NIDDM subjects and 5.3% of 95 matched control subjects in the South Indians (p = 0.17). In elderly Finnish subjects the frequency of the mutation was 7.5% in 40 NIDDM subjects and 7% in 42 matched control subjects. The frequency of codon 972 mutation in the South Indian NIDDM subjects was very similar to the two previously published studies in Danish and French subjects although each study individually fails to reach conventional levels of significance. The data from all four ethnic groups were analysed together after ascertaining that significant heterogeneity did not exist between the studies. Overall, the frequency of the codon 972 mutation is found in 10.7% NIDDM subjects and 5.8% control subjects (p = 0.02). These studies suggest that the codon 972 mutation of the IRS-1 gene might act as a susceptibility gene predisposing to NIDDM in certain ethnic groups.