A Case Report of Anti-GAD65 Antibody-Positive Autoimmune Encephalitis Associated with Diabetes.

Objective To investigate the predictive factors of residual β-cell function in young patients with newly-diagnosed type 1 diabetes mellitus. Methods After an average follow-up of (35.1±13.8)months, a total of 110 young patients with type 1 diabetes mellitus were analyzed. At baseline and follow-up, oral glucose tolerance test(OGTT)and C-peptide release test were carried out, the levels of HbA1C, glutamic acid decarboxylase-antibody(GAD), and the genetic polymorphisms of human leukocyte antigen(HLA)-Ⅱ were detected. The patients were divided into two groups: high-residual β-cell function group(high-RBF group)and low-residual β-cell function group(low-RBF group), which were defined as stimulated C-peptide(Cmax)≥0.6 ng/ml and <0.6 ng/ml at endpoint, respectively. Logistic regression analyses were performed to explore the factors that influence long-term residual β-cell function. Results Compared with low-RBF group, Cmax levels were higher [(1.87±1.35 vs 0.23±0.19)ng/ml, P<0.01], HbA1C levels were lower [(7.00±1.69 vs 8.39±1.77)%, P < 0.01], insulin dosages were lower[(0.62±0.17 vs 0.79±0.17)IU·kg-1·d-1,P <0.01] in high-RBF group at endpoint. Logistic regression analysis suggested that factors including age of onset(years)[OR=0.82, 95%CI 0.73-0.92, P=0.001], higher HLA risk status(OR=4.73, 95%CI 1.28-17.52, P=0.020), female sex(OR=3.39, 95%CI 1.03-11.22, P=0.045), diabetic ketoacidosis history at onset(OR=8.71, 95%CI 2.31-32.83, P=0.001), and higher mean HbA1C levels(OR=2.46, 95%CI 1.47-4.11, P=0.001)were related to low residual β-cell function. Compared with mean HbA1C level during diabetes course, predicted probability value for the four baseline factors(diagnosis age, HLA risk status, gender, and diabetic ketoacidosis history)as the onset state of type 1 diabetes patients was higher[(OR=145.75, 95%CI 17.30-1 228.31, P<0.01)vs(OR=1.82, 95%CI 1.24-2.69, P=0.003)]. Conclusion Younger age of onset, female, higher HLA risk status, diabetic ketoacidosis history at onset and poor glycemic control during diabetes course were the predictive factors of low residual β-cell function in the development of the disease. Compared with mean HbA1C level during diabetes course, an onset state of type 1 diabetes mellitus patients is more valuable to predict long-term residual β-cell function. (Chin J Endocrinol Metab, 2016, 32: 728-733) Key words: Diabetes mellitus, type 1; Islet β-cell function; Human leukocyte antigen class Ⅱ; Prognosis

Objective To determine vitamin D levels in patients with type 1 diabetes mellitus (T1DM),and to explore the relationship of vitamin D and pancreatic β-cell function in these patients.Methods Sixty-five patients with T1DM,78 controls with normal glucose tolerance (NGT),as well as 87 type 2 diabetes mellitus (T2DM) patients,whose HbA1C,blood glucose levels,and duration of diabetes were matched with those in the T1 DM group,were enrolled from January 2011 to April 2012.The differences in serum 25-hydroxyvitamin D3 [25 (OH) D3] levels among the three groups were compared.Pearson correlation analysis was used for investigating the relationship between fasting C-peptide levels and other variables in T1DM group,all P＜0.05 variables then were included in a stepwise multiple linear regression analysis.Results Serum 25 (OH) D3 levels were significantly lower in T1 DM group than those in NGT and T2DM groups [7.7 (4.8-14.9)vs 13.9 (10.0-17.9),12.5 (9.3-17.4)ng/mg,both P＜0.01].Stepwise multiple linear regression analysis showed that fasting serum C-peptide concentration was independently associated with serum 25 (OH) D3 level in T1 DM group.Conclusions T1DM patients had lower vitamin D levels than NGT and T2DM subjects,vitamin D status showed independently a positive association with β-cell function in T1DM patients.Whether supplement of vitamin D to TIDM patients is a topic worth studying. Key words: Vitamin D; Diabetes mellitus, type 1 ; β-cell function

The function of β-cells is closely correlated with the onset of type 2 diabetes mellitus.Currently the bariatric surgery has been considered to be the most effective therapy for ameliorating the complications and improving the prognosis of type 2 diabetes mellitus,while the mechanism remains unknown.The improvement of β-cells function following bariatric surgery might play important roles in the remission of type 2 diabetes mellitus.In this article,the relationship between the changes of related factors after bariatric surgery and the function of pancreatic β-cells is reviewed. Key words: Diabetes mellitus, type 2; Bariatric surgery; Pancreatic beta cells

Objective To expore the functional changes of islet α- and β-cells in early-onset type 2 diabetes mellitus. Method Forty patients with early-onset type 2 diabetes mellitus (EOD group), 40 patients with late-onset type 2 diabetes mellitus (LOD group) and 30 normal controls (NC group) were recruited in this study and received oral glucose tolerance test, insulin and glucagon release tests. Blood glucose, insulin and glucagon levels were compared before and after glucose loading. Analysis of variance was used for comparison between groups, and t test or Mann-Whitney U test were used for pairwise comparison. Results There were no significant differences in blood glucose levels between EOD and LOD group at each time point (t=-1.101-0.007, both P>0.05); Fasting insulin and fasting glucagon in EOD group were significantly higher than those in LOD and control groups (U=140.000-218.000, both P<0.01); HOMA-IR in EOD group were higher than those in LOD and NC group (t=4.980, 2.094, both P<0.05), ISIMatsuda in EOD group were lower than those in LOD and NC group (t=-4.315, -2.146, both P<0.05), HOMA-β and ΔI30/ΔG30 in EOD group were higher than those in LOD group but lower than those in NC group (t=2.140-9.166, both P<0.05), AUCIns in EOD group were higher than those in LOD group (t=-1.527, 2.319, P<0.05), AUCGcg and Gcg/Glu in fasting and 1, 2 h after glucose loading in EOD group were higher than those in LOD group (2 h Gcg/Glu in EOD group was 1.4±0.7, and was 1.0±0.8 in LOD, t=2.113-3.354, both P<0.05). Conclusions β-cell function in EOD patients is better than that in LOD patients. However, compared with LOD patients, the insulin resistance and the islet α cell dysfunction are higher in EOD patients, including high levels of fasting glucagon, lower inhibition effect of blood glucose on glucagon, which may be one of the reasons for the early-onset type 2 diabetes mellitus. Key words: Diabetes mellitus, type 2; Early-onset; Islet α-cell; Islet β-cell

Objective To evaluate the relationship between β-cell function and sudomotor function in patients with type 2 diabetes mellitus (T2DM). Methods A total of 385 patients with T2DM who were hospitalized in the Department of Endocrinology, General Hospital of Eastern Theater Command from March 2016 to May 2018 were enrolled. According to the hands and feet electrochemical skin conductance (HESC, FESC) assessed with SUDOSCAN, patients were divided into normal sudomotor function group (n=262) and sudomotor dysfunction group (n=123). Basal insulin secretion was assessed with homeostasis model assessment of β-cell (HOMA-β). Early-phase insulin secretion function was assessed with the ratio of 30 min increment in C-peptide levels to 30 min increment in glucose levels (ΔC-P30/ΔG30) and the ratio of 30 min increment in insulin levels to 30 min increment in glucose levels (ΔI30/ΔG30). Total β-cell function was assessed with ratio of area under the curve (AUC) of C-peptide to the AUC of glucose in 120 min (C-PAUC/GAUC) and ratio of the AUC of insulin to the AUC of glucose in 120 min (IAUC/GAUC). Results (1) Compared with those in the normal sudomotor function group, ΔC-P30/ΔG30 and ΔI30/ΔG30 in sudomotor dysfunction group were lower [0.19(0.08, 0.30) vs 0.30(0.19, 0.53) and 1.47(0.67, 3.28) vs 2.26(1.28, 4.65), respectively, Z=-5.495,-3.897, all P<0.05], and also C-PAUC/GAUC and IAUC/GAUC were much lower [0.07(0.04, 0.16) vs 0.12(0.06, 0.25) and 2.79(0.76, 6.35) vs 3.30(1.35, 8.32) respectively, Z=-3.894, -2.092, all P<0.05]. (2)Both ΔC-P30/ΔG30 and ΔI30/ΔG30 were positively correlated with HESC (r=0.306, 0.272, respectively, all P<0.05) and FESC (r=0.304, 0.233, respectively, all P<0.05). (3) Multiple linear stepwise regression analysis showed that ΔC-P30/ΔG30, diabetes duration, age, glycated hemoglobin and plasma uric acid were independent factors that impacted on the sudomotor function. Conclusions Early-phase insulin secretion dysfunction independently related with sudomotor dysfunction in T2DM patients. Evaluation of sudomotor function maybe necessary for screening diabetic peripheral neuropathy in patients with early-phase insulin secretion dysfunction. Key words: Diabetes mellitus, type 2; β-cell function; Diabetic peripheral neuropathy; Sudomotor function

Objective To evaluate the efficacy and safety of two dose levels of gliclazide MR (30 mg/d and 60 mg/d)in the treatment of Chinese patients with newly diagnosed type 2 diabetes mellitus (T2DM), and its effects on insulin resistance and β-cell function. Methods A total of 120 subjects with newly diagnosed T2DM were randomized to receive 30 mg/d (Group 30 mg)or 60 mg/d (Group 60 mg)of gliclazide MR in the proportion of 1 to 1 according to random number table for 16 weeks. Glycated hemoglobin A1c (HbA1c)was measured as major efficacy index, and secondary indices such as fasting plasma glucose (FPG), two-hour postprandial plasma glucose (2 h PG), 7-point glucose profile, area under curve of insulin (AUCINS), insulin sensitivity index (SEN) and the indices of the β-cell secretory function [including early insulin secretion index (ΔI30/ΔG30) , basic insulin secretion index (HOMA-β)and modified β- cell function (MBCI)]. Safety was assessed by adverse events (AEs), and so on. The difference of the measurement data was compared with the t test. The Wilcoxon test was used to assess the statistical difference among the groups. Results At 16 weeks, the change from baseline in HbA1c was -1.2%±1.1% in the Group 30 mg and -2.2%±1.4% in the Group 60 mg, intergroup comparison with baseline HbA1c as a covariate showed that the descent range in the Group 60 mg was more significant (F=4.2, P=0.04). There was no intergroup difference in the change from baseline of FPG, 2 hPG and 7-point glucose profile. Also, no intergroup difference was noted in the change from baseline of AUCINS and ΔI30/ΔG30. After treatment, the index of HOMA-β had a significant increasing amplitude in the Group 60 mg compared to that in the Group 30 mg (t=1951.0, P=0.04). The incidence of hypoglycemia related to study drug was 10.5% (6/57)in the Group 30 mg and 16.1%(9/56) in the Group 60 mg. To the end of follow-up, no significant difference in the body mass index was found between the two groups[(24.8±2.6) kg/m2 in Group 30 mg and (24.2±2.5) kg/m2 in Group 60 mg, t=1.09, P>0.05]. Conclusion The dose regimen of gliclazide MR 60 mg/d is superior to 30 mg/d in the improvement of HbA1c and β-cell function for Chinese patients with newly diagnosed T2DM; however, dosage of 60 mg/d increases the incidence of slight hypoglycemia events. Thus, for patients with newly diagnosed T2DM, selection of initial therapy dose should be patient- tailored, based on clinical features of β cell function and hypoglycemia risks. Key words: Diabetes mellitus, type 2; Gliclazide modified release; Treatment outcome; Safety

New Medications for the Treatment of Diabetes.

Type 2 diabetes mellitus is a chronic progressing disease.In some recent studies,hyper glycemic patients get fine control of blood glucose without any hypoglycemic agents after a period of treatment.These results suggest the possibility of remission of islet β-cell function,at least in some diabetic patients.Restoration of β-cell function brings a new hope in the treatment for type 2 diabetes mellitus. Key words: Diabetes mellitus, type 2 ; Treatment ; Islet β-cell function

Effects of Sitagliptin on Insulin and Glucagon Levels in Type 2 Diabetes Mellitus.

The incidence of type 2 diabetes (T2D) is on the rise in youth in the US1Dabelea D. Mayer-Davis E.J. Saydah S. Imperatore G. Linder B. Divers J. et al.Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009.JAMA. 2014; 311: 1778-1786Crossref PubMed Scopus (933) Google Scholar,2Mayer-Davis E.J. Dabelea D. Lawrence J.M. Incidence trends of type 1 and type 2 diabetes among youths, 2002-2012.N Engl J Med. 2017; 377: 301Crossref PubMed Scopus (7) Google Scholar and worldwide.3Pinhas-Hamiel O. Zeitler P. The global spread of type 2 diabetes mellitus in children and adolescents.J Pediatr. 2005; 146: 693-700Abstract Full Text Full Text PDF PubMed Scopus (484) Google Scholar T2D is characterized by hyperglycemia from a combination of insulin resistance and relative deficiency of insulin secretion. The contribution of insulin resistance to diabetes pathogenesis explains the clinical association of diabetes with obesity and, subsequently, the coincidence of increasing T2D prevalence with increasing prevalence and severity of obesity in children.4Skinner A.C. Ravanbakht S.N. Skelton J.A. Perrin E.M. Armstrong S.C. Prevalence of obesity and severe obesity in US children, 1999-2016.Pediatrics. 2018; 141: e20173459Crossref PubMed Scopus (783) Google Scholar Differences have been described in the natural history of youth-onset T2D and adult-onset T2D. Compared with adults, T2D in youth appears to progress more rapidly, with higher rates of metformin treatment failure and more rapid rates of beta cell decline.5Zeitler P. Hirst K. Pyle L. Linder B. Copeland K. et al.TODAY Study GroupA clinical trial to maintain glycemic control in youth with type 2 diabetes.N Engl J Med. 2012; 366: 2247-2256Crossref PubMed Scopus (597) Google Scholar, 6Bacha F. Pyle L. Nadeau K. Cuttler L. Goland R. Haymond M. et al.Determinants of glycemic control in youth with type 2 diabetes at randomization in the TODAY study.Pediatr Diabetes. 2012; 13: 376-383Crossref PubMed Scopus (33) Google Scholar, 7Narasimhan S. Weinstock R.S. Youth-onset type 2 diabetes mellitus: lessons learned from the TODAY study.Mayo Clin Proc. 2014; 89: 806-816Abstract Full Text Full Text PDF PubMed Scopus (63) Google ScholarThe presence of diabetes encumbers those affected with a long-term burden of chronic disease and an increased risk of cardiovascular and microvascular complications. This risk increases with the duration of the disease, putting children with T2D at extremely high risk for complications. Follow-up data from both the SEARCH for Diabetes in Youth study and the Treatment Options for Diabetes in Youth (TODAY) trial have found a substantial presence of diabetes complications as early as adolescence and young adulthood.8TODAY Study GroupRapid rise in hypertension and nephropathy in youth with type 2 diabetes: the TODAY clinical trial.Diabetes Care. 2013; 36: 1735-1741Crossref PubMed Scopus (188) Google Scholar, 9TODAY Study GroupLipid and inflammatory cardiovascular risk worsens over 3 years in youth with type 2 diabetes: the TODAY clinical trial.Diabetes Care. 2013; 36: 1758-1764Crossref PubMed Scopus (122) Google Scholar, 10Maahs D.M. Snively B.M. Bell R.A. Dolan L. Hirsch I. Imperatore G. et al.Higher prevalence of elevated albumin excretion in youth with type 2 than type 1 diabetes: the SEARCH for Diabetes in Youth study.Diabetes Care. 2007; 30: 2593-2598Crossref PubMed Scopus (126) Google Scholar, 11Mayer-Davis E.J. Davis C. Saadine J. D'Agostino Jr., R.B. Dabelea D. Dolan L. et al.Diabetic retinopathy in the SEARCH for Diabetes in Youth cohort: a pilot study.Diabet Med. 2012; 29: 1148-1152Crossref PubMed Scopus (55) Google Scholar Moreover, the prevalence of complications and overall mortality are higher in youth with T2D compared with adults with T2D or even youth with type 1 diabetes.12Constantino M.I. Molyneaux L. Limacher-Gisler F. Al-Saeed A. Luo C. Wu T. et al.Long-term complications and mortality in young-onset diabetes: type 2 diabetes is more hazardous and lethal than type 1 diabetes.Diabetes Care. 2013; 36: 3863-3869Crossref PubMed Scopus (268) Google Scholar,13Dabelea D. Stafford J.M. Mayer-Davis E.J. D'Agostino Jr., R. Dolan L. Imperatore G. et al.Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood.JAMA. 2017; 317: 825-835Crossref PubMed Scopus (2) Google Scholar This burden of disease underscores the need to improve our understanding of diabetes risk, prevention, and optimal treatment in youth.T2D is a complex, multifactorial disease influenced by both environmental factors and genetic variation as well as their interactions.14Barroso I. McCarthy M.I. The genetic basis of metabolic disease.Cell. 2019; 177: 146-161Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar,15Kolb H. Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes.BMC Med. 2017; 15: 131Crossref PubMed Scopus (256) Google Scholar The heritability of T2D is demonstrated by both the high concordance rates in identical twins16Barnett A.H. Eff C. Leslie R.D. Pyke D.A. Diabetes in identical twins. A study of 200 pairs.Diabetologia. 1981; 20: 87-93Crossref PubMed Scopus (835) Google Scholar,17Willemsen G. Ward K.J. Bell C.G. Christensen K. Bowden J. Dalgård C. et al.The concordance and heritability of type 2 diabetes in 34,166 twin pairs from international twin registers: the Discordant Twin (DISCOTWIN) Consortium.Twin Res Hum Genet. 2015; 18: 762-771Crossref PubMed Scopus (82) Google Scholar and the typical presence of a family history of T2D in those with the disease.18Copeland K.C. Zeitler P. Geffner M. Guandalini C. Higgins J. Hirst K. et al.Characteristics of adolescents and youth with recent-onset type 2 diabetes: the TODAY cohort at baseline.J Clin Endocrinol Metab. 2011; 96: 159-167Crossref PubMed Scopus (283) Google Scholar,19Klein B.E. Klein R. Moss S.E. Cruickshanks K.J. Parental history of diabetes in a population-based study.Diabetes Care. 1996; 19: 827-830Crossref PubMed Scopus (100) Google Scholar Investigations of the genetics of diabetes risk have examined both overall T2D risk as well as individual glycemic traits that may predispose to diabetes, such as fasting glucose levels, insulin secretion, insulin resistance, and β-cell function. Understanding the genetic underpinnings of diabetes risk offers an opportunity to improve both our knowledge of the mechanisms contributing to diabetes pathogenesis and our understanding of how best to individualize diabetes treatment and prevent complications. Here we review the current state of T2D genetics, specifically as it pertains to children and adolescents.Monogenic DiabetesDiabetes as a result of a single gene abnormality, or monogenic diabetes, shares clinical overlap with T2D, particularly T2D in youth. There are 3 major subgroups of monogenic diabetes: neonatal diabetes, syndromic diabetes, and maturity-onset diabetes of the young (MODY). Neonatal diabetes presents in infancy, although only a subset of infants develops diabetes in the neonatal period (the first 30 days of life); the majority of patients become symptomatic within the first 6 months of life.20Rubio-Cabezas O. Ellard S. Diabetes mellitus in neonates and infants: genetic heterogeneity, clinical approach to diagnosis, and therapeutic options.Horm Res Paediatr. 2013; 80: 137-146Crossref PubMed Scopus (66) Google Scholar Syndromic diabetes presents with additional extrapancreatic features, typically also in infancy.20Rubio-Cabezas O. Ellard S. Diabetes mellitus in neonates and infants: genetic heterogeneity, clinical approach to diagnosis, and therapeutic options.Horm Res Paediatr. 2013; 80: 137-146Crossref PubMed Scopus (66) Google Scholar MODY is characterized by non–insulin-dependent diabetes diagnosed at a young age (<25 years) demonstrating an autosomal dominant inheritance pattern.21Tattersall R.B. Fajans S.S. A difference between the inheritance of classical juvenile-onset and maturity-onset type diabetes of young people.Diabetes. 1975; 24: 44-53Crossref PubMed Scopus (346) Google Scholar Subtypes of MODY are based on specific genetic defects, with involvement of different genes associated with differences in clinical and physiologic phenotypes.22Hattersley A.T. Maturity-onset diabetes of the young: clinical heterogeneity explained by genetic heterogeneity.Diabet Med. 1998; 15: 15-24Crossref PubMed Scopus (264) Google ScholarMonogenic diabetes can be caused by pathogenic mutations in genes that disrupt glucose sensing, insulin transcription, the potassium–adenosine triphosphate channel that transduces the signal for insulin release, the insulin gene, or pancreatic development. Understanding the genes associated with monogenic forms of diabetes has provided insight into the disease mechanisms of diabetes. Eleven different genes have been identified as causal for MODY in the Online Mendelian Inheritance in Man catalog: HNF4A (MODY 1), GCK (MODY 2), HNF1A (MODY 3), PDX1 (MODY 4), HNF1B (MODY 5), NEUROD1 (MODY 6), CEL (MODY 8), INS (MODY 10), ABCC8 (MODY 12), KCNJ11 (MODY 13), and APPL1 (MODY 14). Note that the genes previously reported as causal for MODY 7, MODY 9, and MODY 11 are absent from this list, owing to the recent proposal to eliminate them from the list of causal MODY genes based on updated genetic evidence.23Laver T.W. Wakeling M.N. Knox O. Colclough K. Wright C.F. Ellard S. et al.Evaluation of evidence for pathogenicity demonstrates that BLK, KLF11, and PAX4 should not be included in diagnostic testing for MODY.Diabetes. 2022; 71: 1128-1136Crossref PubMed Scopus (4) Google Scholar Mutations in the HNF4A and HNF1A genes lead to abnormal insulin secretion, and MODY caused by variants in these genes can be effectively managed with oral sulfonylurea therapy.24Pearson E.R. Pruhova S. Tack C.J. Johansen A. Castleden H.A. Lumb P.J. et al.Molecular genetics and phenotypic characteristics of MODY caused by hepatocyte nuclear factor 4alpha mutations in a large European collection.Diabetologia. 2005; 48: 878-885Crossref PubMed Scopus (178) Google Scholar,25Pearson E.R. Starkey B.J. Powell R.J. Gribble F.M. Clark P.M. Hattersley A.T. Genetic cause of hyperglycaemia and response to treatment in diabetes.Lancet. 2003; 362: 1275-1281Abstract Full Text Full Text PDF PubMed Scopus (461) Google Scholar MODY caused by pathogenic variants in GCK, the gene encoding glucokinase, which phosphorylates glucose to glucose-6-phosphate in pancreatic cells and acts as a glucose sensor, is characterized by a mild, stable hyperglycemia with a low risk of complications that commonly does not require any treatment.26Steele A.M. Shields B.M. Wensley K.J. Colclough K. Ellard S. Hattersley A.T. Prevalence of vascular complications among patients with glucokinase mutations and prolonged, mild hyperglycemia.JAMA. 2014; 311: 279-286Crossref PubMed Scopus (200) Google ScholarGiven the clinical overlap between T2D and rarer forms of diabetes, a long-held hypothesis is that the genetic underpinnings of both common and rare forms of diabetes might not be entirely distinct. Multiple studies have shown that MODY affects a small but not insignificant subset of youth with diabetes, including those clinically diagnosed with T2D. The SEARCH for Diabetes in Youth Study published the first systematic study of MODY prevalence in US youth, which reported a 1.2% overall prevalence of MODY.27Pihoker C. Gilliam L.K. Ellard S. Dabelea D. Davis C. Dolan L.M. et al.Prevalence, characteristics and clinical diagnosis of maturity onset diabetes of the young due to mutations in HNF1A, HNF4A, and glucokinase: results from the SEARCH for Diabetes in Youth.J Clin Endocrinol Metab. 2013; 98: 4055-4062Crossref PubMed Scopus (219) Google Scholar A genetic sequencing study of participants in the TODAY study found that 4.5% carried a pathogenic variant in a MODY gene.28Kleinberger J.W. Copeland K.C. Gandica R.G. Haymond M.W. Levitsky L.L. Linder B. et al.Monogenic diabetes in overweight and obese youth diagnosed with type 2 diabetes: the TODAY clinical trial.Genet Med. 2018; 20: 583-590Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar A larger genetic study conducted by the Progress in Diabetes Genetics in Youth (ProDiGY) consortium that included the TODAY cohort, a second cohort recruited by the TODAY researchers for genetic studies, and a subset of the SEARCH for Diabetes in Youth study participants identified a 2.8% incidence of MODY.28Kleinberger J.W. Copeland K.C. Gandica R.G. Haymond M.W. Levitsky L.L. Linder B. et al.Monogenic diabetes in overweight and obese youth diagnosed with type 2 diabetes: the TODAY clinical trial.Genet Med. 2018; 20: 583-590Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar,29Todd J.N. Kleinberger J.W. Zhang H. Srinivasan S. Tollefsen S.E. Levitsky L.L. et al.Monogenic diabetes in youth with presumed type 2 diabetes: results from the Progress in Diabetes Genetics in Youth (ProDiGY) collaboration.Diabetes Care. 2021; 44: 2312-2319Crossref Scopus (10) Google Scholar These studies focused on rare, highly penetrant variants in known MODY genes. In adult studies of T2D genetics, there is increasing overlap of common variant associations and genes associated with monogenic diabetes.30Flannick J. Johansson S. Njølstad P.R. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes.Nat Rev Endocrinol. 2016; 12: 394-406Crossref PubMed Scopus (78) Google Scholar Although such associations have not yet been shown in youth with T2D, it is possible that further examination of rarer variants will find associations along a spectrum of disease risk in genes or pathways relevant to diabetes.Candidate Gene StudiesMany efforts to understand the genetic underpinnings of T2D in children have focused on genetic variants with known associations with glycemic traits or T2D risk in adults, examining whether similar associations exist in youth. Individual variants have been shown to have similar associations in children for both fasting glucose31Barker A. Sharp S.J. Timpson N.J. Bouatia-Naji N. Warrington N.M. Kanoni S. et al.Association of genetic Loci with glucose levels in childhood and adolescence: a meta-analysis of over 6,000 children.Diabetes. 2011; 60: 1805-1812Crossref PubMed Scopus (87) Google Scholar,32Kelliny C. Ekelund U. Andersen L.B. Brage S. Loos R.J. Wareham N.J. et al.Common genetic determinants of glucose homeostasis in healthy children: the European Youth Heart Study.Diabetes. 2009; 58: 2939-2945Crossref PubMed Scopus (50) Google Scholar and the homeostasis model assessment of β cell function.31Barker A. Sharp S.J. Timpson N.J. Bouatia-Naji N. Warrington N.M. Kanoni S. et al.Association of genetic Loci with glucose levels in childhood and adolescence: a meta-analysis of over 6,000 children.Diabetes. 2011; 60: 1805-1812Crossref PubMed Scopus (87) Google Scholar Several T2D risk genes have been associated with youth-onset T2D, including TCF7L2,33Dabelea D. Dolan L.M. D'Agostino Jr., R. Hernandez A.M. McAteer J.B. Hamman R.F. et al.Association testing of TCF7L2 polymorphisms with type 2 diabetes in multi-ethnic youth.Diabetologia. 2011; 54: 535-539Crossref PubMed Scopus (42) Google Scholar,34Miranda-Lora A.L. Cruz M. Molina-Díaz M. Gutiérrez J. Flores-Huerta S. Klünder-Klünder M. Associations of common variants in the SLC16A11, TCF7L2, and ABCA1 genes with pediatric-onset type 2 diabetes and related glycemic traits in families: a case-control and case-parent trio study.Pediatr Diabetes. 2017; 18: 824-831Crossref PubMed Scopus (16) Google Scholar SLC16A11, and ABCA1.34Miranda-Lora A.L. Cruz M. Molina-Díaz M. Gutiérrez J. Flores-Huerta S. Klünder-Klünder M. Associations of common variants in the SLC16A11, TCF7L2, and ABCA1 genes with pediatric-onset type 2 diabetes and related glycemic traits in families: a case-control and case-parent trio study.Pediatr Diabetes. 2017; 18: 824-831Crossref PubMed Scopus (16) Google ScholarGenetic risk scores (GRSs), which allow for the assessment of the aggregate genetic risk of a given trait, have demonstrated association of GRSs constructed from variants associated in adults with glycemic traits and/or T2D risk with fasting glucose and measures of β-cell function,35Carayol J. Hosking J. Pinkney J. Marquis J. Charpagne A. Metairon S. et al.Genetic susceptibility determines β-cell function and fasting glycemia trajectories throughout childhood: a 12-year cohort study (EarlyBird 76).Diabetes Care. 2020; 43: 653-660Crossref PubMed Scopus (9) Google Scholar, 36Giannini C. Dalla Man C. Groop L. Cobelli C. Zhao H. Shaw M.M. et al.Co-occurrence of risk alleles in or near genes modulating insulin secretion predisposes obese youth to prediabetes.Diabetes Care. 2014; 37: 475-482Crossref PubMed Scopus (29) Google Scholar, 37Morandi A. Bonnefond A. Lobbens S. Yengo L. Miraglia Del Giudice E. Grandone A. et al.Associations between type 2 diabetes-related genetic scores and metabolic traits, in obese and normal-weight youths.J Clin Endocrinol Metab. 2016; 101: 4244-4250Crossref PubMed Scopus (7) Google Scholar as well as measures of insulin resistance35Carayol J. Hosking J. Pinkney J. Marquis J. Charpagne A. Metairon S. et al.Genetic susceptibility determines β-cell function and fasting glycemia trajectories throughout childhood: a 12-year cohort study (EarlyBird 76).Diabetes Care. 2020; 43: 653-660Crossref PubMed Scopus (9) Google Scholar,36Giannini C. Dalla Man C. Groop L. Cobelli C. Zhao H. Shaw M.M. et al.Co-occurrence of risk alleles in or near genes modulating insulin secretion predisposes obese youth to prediabetes.Diabetes Care. 2014; 37: 475-482Crossref PubMed Scopus (29) Google Scholar,38Graae A.S. Hollensted M. Kloppenborg J.T. Mahendran Y. Schnurr T.M. Appel E.V. et al.An adult-based insulin resistance genetic risk score associates with insulin resistance, metabolic traits and altered fat distribution in Danish children and adolescents who are overweight or obese.Diabetologia. 2018; 61: 1769-1779Crossref PubMed Scopus (9) Google Scholar in youth. Two studies have examined the ability of GRSs to identify children at risk of progressing to T2D; even though the scores were shown to be associated with T2D risk,36Giannini C. Dalla Man C. Groop L. Cobelli C. Zhao H. Shaw M.M. et al.Co-occurrence of risk alleles in or near genes modulating insulin secretion predisposes obese youth to prediabetes.Diabetes Care. 2014; 37: 475-482Crossref PubMed Scopus (29) Google Scholar,39Miranda-Lora A.L. Vilchis-Gil J. Juárez-Comboni D.B. Cruz M. Klünder-Klünder M. A genetic risk score improves the prediction of type 2 diabetes mellitus in Mexican youths but has lower predictive utility compared with non-genetic factors.Front Endocrinol (Lausanne). 2021; 12: 647864Crossref PubMed Scopus (4) Google Scholar in one of the studies clinical factors such as body mass index (BMI) and family history of T2D had higher predictive utility.34Miranda-Lora A.L. Cruz M. Molina-Díaz M. Gutiérrez J. Flores-Huerta S. Klünder-Klünder M. Associations of common variants in the SLC16A11, TCF7L2, and ABCA1 genes with pediatric-onset type 2 diabetes and related glycemic traits in families: a case-control and case-parent trio study.Pediatr Diabetes. 2017; 18: 824-831Crossref PubMed Scopus (16) Google ScholarGenome-Wide Association StudiesSince the first genome-wide association study (GWAS) for T2D in adults was published in 2007,40Sladek R. Rocheleau G. Rung J. Dina C. Shen L. Serre D. et al.A genome-wide association study identifies novel risk loci for type 2 diabetes.Nature. 2007; 445: 881-885Crossref PubMed Scopus (2352) Google Scholar there has been an explosion of genetic discoveries related to adult T2D with extremely well-powered studies and advanced analytic techniques. At the time of this publication, >400 variants have been associated with T2D in adults.41Mahajan A. Taliun D. Thurner M. Robertson N.R. Torres J.M. Rayner N.W. et al.Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.Nat Genet. 2018; 50: 1505-1513Crossref PubMed Scopus (671) Google Scholar In comparison, large scale studies of pediatric T2D have not been conducted, largely due to limited sample sizes. To address this gap, we and colleagues formed the ProDiGY Consortium, which is a collaboration of 3 research groups: the TODAY study,42Zeitler P. Epstein L. Grey M. Hirst K. Kaufman F. et Study for type 2 diabetes in adolescents and a study of the of metformin or in combination with or in adolescents with type 2 Diabetes. 2007; PubMed Scopus Google Scholar SEARCH for Diabetes in Study for Diabetes in a study of the incidence and of diabetes mellitus in Clin Full Text Full Text PDF PubMed Scopus Google Scholar and the 2 Diabetes Genetic by in C. J. T.M. A. K.J. et al.The genetic of type 2 diabetes.Nature. 2016; PubMed Scopus Google Scholar The ProDiGY study is a to understanding of the genetics of T2D in youth by the sample and phenotypic data of 2 pediatric T2D studies with the genetic and of a adult diabetes genetics we conducted the first for T2D in youth to identify genetic variants specifically to youth-onset S. L. J. Divers J. S. S. et al.The first genome-wide association study for type 2 diabetes in the Progress in Diabetes Genetics in Youth (ProDiGY) 2021; PubMed Scopus (10) Google Scholar our genetic in youth with T2D who were for pancreatic and adult years and identified genome-wide including the novel in with an of for T2D, that of the risk a of T2D compared with the A The 6 loci were previously identified in adults and included TCF7L2, and S. L. J. Divers J. S. S. et al.The first genome-wide association study for type 2 diabetes in the Progress in Diabetes Genetics in Youth (ProDiGY) 2021; PubMed Scopus (10) Google the of our ProDiGY our hypothesis was that genetic were in youth with T2D compared with adults, given the T2D with to early age of To this we constructed GRSs in ProDiGY from known T2D variants identified in A. Zhang K.J. T. et meta-analysis insight into the genetic of type 2 diabetes Genet. 2014; PubMed Scopus Google Scholar of the association of the risk score between youth and adult and a higher for T2D in the youth compared with the adult in with our S. L. J. Divers J. S. S. et al.The first genome-wide association study for type 2 diabetes in the Progress in Diabetes Genetics in Youth (ProDiGY) 2021; PubMed Scopus (10) Google Scholar the efforts of the ProDiGY consortium have provided insight into the genetic of T2D in youth and have shown that the genetic of T2D in youth largely with that in adults but with a aggregate genetic risk studies of genes are to understand how the identified genetic variants disease the between and genetic factors are also to T2D risk in of Genetics in has been in our understanding of the genetics of T2D in youth to in sequencing and such as ProDiGY to sample sizes. Here we the of genetics as it pertains to pediatric T2D is a disease of from both environmental and genetic as well as of insulin resistance and β-cell among The of in MODY 3 is a of how genetics can be to disease and Although not as genetics also can be to for the more common of T2D. A approach has been to the heterogeneity of T2D with of loci mechanisms of disease based on β cell insulin resistance, and fat J. M. S. J.B. J. et 2 diabetes genetic loci by associations to disease mechanisms and a Med. 2018; 15: PubMed Scopus Google D. R.B. A. R. M. et association differences of Genet. 2021; PubMed Scopus Google Scholar These efforts to to with the of clinical and the risk of complications in the The of using genetic data to disease is that it over an and can be even a particularly for children who have T2D from the Diabetes Association youth with obesity for T2D based on the presence of additional risk including a family history of T2D, history of diabetes, and associated with insulin resistance, such as Diabetes and of in Care. 2021; 44: PubMed Scopus Google Scholar Although these allow to identify youth diabetes it is not which of these youth will on to T2D. In there is or between clinical as body mass fasting insulin or and the to the early days of T2D studies that a risk score for T2D in adults does not clinical of but to clinical risk genetic may predictive particularly for of adults in risk such as glucose might not have T. B. T. et al.Common genetic variants the from clinically of fasting glucose 2012; PubMed Scopus Google Scholar a risk score of 1 variants and that the of the distribution can identify adults with a to in T2D the of the as the the reported predictive was and the predictive was T. A. et of a risk score for type 2 diabetes in 2022; Scopus Google Scholar Although the current clinical utility of in the more scores from will to improve the predictive of these into can related to T2D with the of pathways related to the genetic the in TCF7L2, which has one of the known for common variants in both youth and adults with D. Dolan L.M. D'Agostino Jr., R. Hernandez A.M. McAteer J.B. Hamman R.F. et al.Association testing of TCF7L2 polymorphisms with type 2 diabetes in multi-ethnic youth.Diabetologia. 2011; 54: 535-539Crossref PubMed Scopus (42) Google R. Rocheleau G. Rung J. Dina C. Shen L. Serre D. et al.A genome-wide association study identifies novel risk loci for type 2 diabetes.Nature. 2007; 445: 881-885Crossref PubMed Scopus (2352) Google S. L. J.

Objective To investigate the risk factors for the occurrence of hyperuricemia in patients with type 2 diabetes mellitus. Methods The data of 126 patients with type 2 diabetes mellitus admitted to the First Affiliated Hospital of Henan University from July 2016 to July 2018 were retrospectively analyzed. Patients with type 2 diabetes mellitus complicated with hyperuricemia were included in the high uric acid group (n=38). The other patients were included in the normal uric acid group (n=88). The risk factors for the occurrence of hyperuricemia in patients with type 2 diabetes mellitus were analyzed. Results By the analysis of the clinical data from 126 patients with type 2 diabetes mellitus, it was found that age, gender, course of disease and combined hypertension were not associated with the occurrence of hyperuricemia in patients with type 2 diabetes mellitus (P>0.05). Body mass index (BMI), triglyceride (TG), fasting plasan glucose (FPG), glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) were associated with the occurrence of hyperuricemia in patients with type 2 diabetes mellitus (P<0.05). Multivariate logistic regression analysis found that BMI, TG, FPG and BUN were independent risk factors for the occurrence of hyperuricemia in patients with type 2 diabetes mellitus (OR=1.429, 95%CI 1.038-1.967; OR=1.296, 95%CI 1.014-1.655; OR=1.468, 95%CI 1.075-2.005; OR=1.446, 95%CI 1.125-1.859; P<0.05). And eGFR was the protective factor against hyperuricemia in patients with type 2 diabetes mellitus (OR=0.476, 95%CI 0.284-0.798, P<0.05). Conclusions BMI, TG, FPG and BUN are independent risk factors for hyperuricemia in patients with type 2 diabetes mellitus. eGFR is a protective factor against hyperuricemia in patients with type 2 diabetes mellitus. For the above factors, preventive measures should be made to further reduce the incidence of hyperuricemia in patients with type 2 diabetes mellitus. Key words: Type 2 diabetes mellitus; Hyperuricemia; Risk factor

Type 2 diabetes mellitus (T2DM) is characterized by pancreatic islet failure due to loss of β-cell secretory function and mass. Studies have identified a link between a variance in the gene encoding melatonin (MT) receptor 2, T2DM, and impaired insulin secretion. This genetic linkage raises the question whether MT signaling plays a role in regulation of β-cell function and survival in T2DM. To address this postulate, we used INS 832/13 cells to test whether activation of MT signaling attenuates proteotoxicity-induced β-cell apoptosis and through which molecular mechanism. We also used nondiabetic and T2DM human islets to test the potential of MT signaling to attenuate deleterious effects of glucotoxicity and T2DM on β-cell function. MT signaling in β-cells (with duration designed to mimic typical nightly exposure) significantly enhanced activation of the cAMP-dependent signal transduction pathway and attenuated proteotoxicity-induced β-cell apoptosis evidenced by reduced caspase-3 cleavage (∼40%), decreased activation of stress-activated protein kinase/Jun-amino-terminal kinase (∼50%) and diminished oxidative stress response. Activation of MT signaling in human islets was shown to restore glucose-stimulated insulin secretion in islets exposed to chronic hyperglycemia as well as in T2DM islets. Our data suggest that β-cell MT signaling is important for the regulation of β-cell survival and function and implies a preventative and therapeutic potential for preservation of β-cell mass and function in T2DM.

Objective To assess the relations between glycosylated hemoglobin A1c(HbA1c) and β-cell function as measured by indices derived from oral glucose tolerance test (OGTT) in Chinese subjects. Methods A total of 913 subjects, who underwent 75 g OGTT for evaluation of glucose intolerance from June 2010 to February 2013, were included. According to OGTT, the enrolled subjects were divided into the following three groups: normal glucose tolerance (NGT, n=205), impaired glucose regulation (IGR, n=328), and type 2 diabetes mellitus (T2DM, n=380). Meanwhile, the subjects were also divided into three groups according to HbA1c levels: HbA1c 6.4% (n=245). Insulin sensitivity was measured by Matsuda insulin sensitivity index (ISIM) and 1/homeostasis model assessment of insulin resistance (1/HOMA-IR). β-cell function adjusted by insulin sensitivity was assessed from disposition index (DI) at early-phase DI30 and total DI120. ANOVA, Chi-Square test and general linear model were used for data analysis. Results Compared with the HbA1c 6.4% group (F=12.765-317.316, all P<0.05). In NGT group, DI30 and DI120 decreased in subjects with elevated HbA1c; in HbA1c<5.7% group, DI30 and DI120 declined as glucose increased. Conclusions Both impairment of β-cell function and insulin sensitivity contributes to the deterioration of HbA1c in Chinese population. A combination of HbA1c and OGTT is effective in screening for subjects with β-cell dysfunction. Key words: Diabetes mellitus, type 2; Glycated hemoglobins; β-cell function

Background: Diabetic ketoacidosis and hyperosmolar hyperglycemic non-ketotic coma (HHNK) are two serious acute complications of diabetes mellitus. DKA consists of the biochemical triad of hyperglycemia, ketonemia and acidemia. In DKA and HHNK dehydration and sodium depletion is seen. Lack of insulin causes hyperglycemia and also inhibits entry of potassium into the cells leading to hyperkalemia. Moreover Hyperglucagonemia also contributes to hyperglycemia and can worsen the diabetic state. Study Design: This study was retrospective, analytical case control study. Non - probability convenient sampling technique was used. Materials and Methods: We reviewed the hospital admissions and patients coming to OPD with type1 & 2 diabetes mellitus as well as diabetic complications like diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic non-ketotic coma (HHNK). We compared the groups for plasma glucose, plasma osmolality, plasma glucagon, serum electrolytes and arterial blood gases (ABGs) with control group. Results: Twelve persons were considered as control being non-diabetic with normal oral glucose tolerance. Mean plasma glucose level & mean plasma osmolality level in the patients of uncontrolled type 2 diabetes mellitus, uncontrolled type I diabetes mellitus, DKA and HHNK was significantly higher (p < 0.001) as compared with control subjects. Mean plasma glucagon level in the patients of uncontrolled type I diabetes mellitus and DKA was found significantly higher (p < 0.001) as compared with control subjects. Serum potassium level was significantly higher in patients of uncontrolled type 2 diabetes mellitus, uncontrolled type 1 diabetes mellitus, HHNK (p <0.001) and DKA (p <0.01) as compared with control subjects. Arterial pH was significantly lower in patients of DKA (p < 0.001), uncontrolled type 1 diabetes mellitus (p < 0.05) and HHNK (p < 0.01). Arterial PCO 2 was significantly lower in patients of DKA (p < 0.05). Plasma bicarbonate levels were found significantly lower in patients of DKA (p < 0.001) and HHNK (p < 0.01). Discussion: The present study showed that in type 1 DM hyperglucagonemia was more marked leading to excessive ketone bodies production and resulting in DKA. The ketoacids formed during DKA are strong acids that fully dissociate at physiological pH. So ketonuria lead to excretion of positively charged cations (Na + , K + , NH 4 + ). Moreover, the hydrogen ions were titrated by plasma bicarbonate ions, resulting in metabolic acidosis and retention of anions lead to increase in the plasma anion gap in DKA. The degree of hyperosmolality and hyperglycemia was more marked in patients with HHNK as compared with DKA. The osmotic effects of glycosuria resulted in impairment reabsorption of NaC1 and H 2 O and ultimately hyponatremia. Whereas Serum potassium level was found to be significantly higher in uncontrolled type I diabetes mellitus, uncontrolled type 2 diabetes mellitus, DKA and HHNK. These observations were according to the results of previous studies. Conclusions: Hyperglucagonemia causes marked hyperglycemia under conditions of relative insulin deficiency and can worsen the diabetic state like development of DKA when insulin deficiency becomes absolute as in type 1 diabetes mellitus. Key words: Diabetes mellitus (DM), diabetic ketoacidosis (DKA), hyperosmolar hyperglycemic non-ketotic coma (HHNK), arterial blood gases (ABGs), plasma osmolality.

Objective To investigate the relationship between fasting plasma glucose (FPG) and islet α-cell and β-cell function in patients with type 2 diabetes mellitus (T2DM).Methods Four hundred and thirty-seven patients with T2DM were divided into 3 groups according to the level of FPG:F1 group:FPG ≤ 6 mmol/L (73 cases),F2 group:6 mmol/L ＜ FPG ≤ 7 mmol/L (103 cases),and F3 group:FPG ＞ 7mmol/L (261 cases),and 30 cases of healthy people were selected as control group.Oral glucose tolerance test,insulin releasing test and glucagon releasing test were performed to observe the differences of glucagon,glucagon/ insulin,the ratio of 30 min insulin and blood glucose value after glucose load (△ I30/△ G30),and the area under curve of insulin (AUC1) among the 4 groups and the correlation analysis was performed between glucagon and other indicators.Results Glycosylated hemoglobin (HbA1c),plasma glucose 120 at min after glucose load in F1,F2 and F3 group were significantly higher than those in control group,and there were statistical differences (P ＜0.05).In F1,F2,F3 group,with the increase of the HbA1c,the course of disease and plasma glucose at 120 min after glucose load showed increasing trend.The triglyceride in F2 group and F3 group was significantly higher than that in F1 group and control group,and low density lipoprotein cholesterol in F3 group was significantly higher than that in F1 group,F2 group and control group,and there were statistical differences (P ＜ 0.05).The glucagon at 60,120 min after glucose load in F1 group,30,60,120 min after glucose load in F2 group,and 30,60,120,180 min after glucose load in F3 group was significantly higher than that in control group,and there were statistical differences (P ＜ 0.05).The glucagon at 60,120,180 min after glucose load in F2 group,at fasting and 30,60,120,180 rain after glucose load in F3 group was significantly higher than that in F1 group,and there were statistical differences (P ＜ 0.05).The glucagon at fasting and 30,60,120,180 min after glucose load in F3 group was significantly higher than that in F2 group,and there were statistical differences (P ＜ 0.05).The area under curve of glucagon in control group was 9.5 ±0.3,in F1 group was 9.7 ± 0.2,in F2 group was 9.9 ± 0.2,in F3 group was 10.2 ± 0.3,and there were statistical differences among the 4 groups (P ＜ 0.05).The glucagon/insulin at fasting and 30,60 min after glucose load in F1 groups,fasting and 30,60,120 min after glucose load in F2 group,fasting and 30,60,120 min after glucose load in F3 group was significantly higher than that in control group,and there were statistical differences (P＜ 0.05).The glucagon/insulin at fasting and 60,120 min after glucose load in F2 group,fasting and 30,60,120,180 min after glucose load in F3 group was significantly higher than that in F1 group,and there were statistical differences (P ＜ 0.05).The glucagon/insulin 30,60,120,180 min after glucose load in F3 group was significantly higher than that in F2 group,and there were statistical differences (P＜ 0.05).The homeostasis model of assessment for insulin resistance index (HOMA-IR) in F2 group and F3 group was significantly higher than that in control group and F1 group,in F3 group was significantly higher than that in F2 group,and there were statistical differences (P＜ 0.05).The insulin sensitivity index (ISI) in F2 group and F3 group was significantly lower than that in control group and F1 group,in F3 group was significantly lower than that in F2 group,and there were statistical differences (P ＜ 0.05).The homeostasis model of assessment for islet β-cell function index (HOMA-β) and △I30/△G30 in F1,F2,F3 group were significantly lower than those in control group,and there were statistical differences (P ＜ 0.05).The AUC1 in F2 group was significantly lower than that in control group,and AUC1 in F3 group was significantly lower than that in control group,F1 group and F2 group,there were statistical differences (P ＜0.05).The results of Pearson correlation analysis showed there was negative correlation between glucagon and △I30/△G30,HOMA-β,body mass index,ISI,AUC1 (r =-0.229,-0.153,-0.151,-0.146,-0.136,P＜0.01 or ＜0.05),and there was positive correlation between glucagon and FPG,area under curve of glucose (AUCG),HbA1c,course of disease and HOMA-IR (r =0.545,0.476,0.273,0.193,0.189,P ＜ 0.01).The results of multiplestepwise regression analysis showed there was positive correlation between glucagon and FPG,AUCG,HbA1c,course of disease (P ＜0.01 or ＜0.05),and there was negative correlation between glucagon and △I30/△ G30 (P ＜ 0.05).Conclusions Islet β-cell function is decreased with the increasing of FPG,while islet α-cell function is increased,especially in those with higher levels of FPG.Regulation of glucagon should be concerned to make the blood glucose target easier to reach,at the same time of protecting β-cell function. Key words: Glucagon; Glucagon-secreting cell; Insulin-secreting cell; Blood glucose; Diabetes mellitus, type 2