Joachim Spranger
Mar 1, 2003; 52:812-817
Pathophysiology

Franz M Matschinsky
Feb 1, 1996; 45:223-241
Banting Lecture 1995

G Perseghin
Aug 1, 1999; 48:1600-1606
Articles

R A DeFronzo
Dec 1, 1981; 30:1000-1007
Original Contribution

Charles M. Alexander
May 1, 2003; 52:1210-1214
Complications

Miriam Cnop
Dec 1, 2005; 54:S97-S107
Section III: Inflammation and beta-Cell Death

Giulio Marchesini
Aug 1, 2001; 50:1844-1850
Pathophysiology

Gökhan S Hotamisligil
Nov 1, 1994; 43:1271-1278
Perspectives in Diabetes

D Dabelea
Dec 1, 2000; 49:2208-2211
Articles

Tatsuya Hayashi
Aug 1, 1998; 47:1369-1373
Rapid Publications

C. Ronald Kahn
Aug 1, 1994; 43:1066-1085
Banting Lecture

T Inoguchi
Nov 1, 2000; 49:1939-1945
Articles

Eve Van Cauter
Mar 1, 1992; 41:368-377
Original Article

Ralph A. DeFronzo
Apr 1, 2009; 58:773-795
Banting Lecture

D A Pan
Jun 1, 1997; 46:983-988
Original Article

D Koya
Jun 1, 1998; 47:859-866
Articles

Roger H Unger
Aug 1, 1995; 44:863-870
Perspectives in Diabetes

Richard S Surwit
Sep 1, 1988; 37:1163-1167
Original Article

Samar I. Itani
Jul 1, 2002; 51:2005-2011
Rapid Publications

G Xu
Dec 1, 1999; 48:2270-2276
Articles

J. Denis McGarry
Jan 1, 2002; 51:7-18
Banting Lecture 2001

Paul E Lacy
Jan 1, 1967; 16:35-39
Original Contribution

Andreas Festa
Apr 1, 2002; 51:1131-1137
Complications

The Diabetes Control and Complications Trial Research Group
Feb 1, 1997; 46:271-286
Original Article

The Diabetes Control and Complications Trial Research Group
Aug 1, 1995; 44:968-983
Original Article

David E. Cummings
Aug 1, 2001; 50:1714-1719
Rapid Publications

JW Baynes
Jan 1, 1999; 48:1-9
Articles

Norikazu Maeda
Sep 1, 2001; 50:2094-2099
Pathophysiology

Thomas A. Buchanan
Sep 1, 2002; 51:2796-2803
Pathophysiology

ME Griffin
Jun 1, 1999; 48:1270-1274
Articles

Guenther Boden
Jan 1, 1997; 46:3-10
Perspectives in Diabetes

U.K. Prospective Diabetes Study Group
Nov 1, 1995; 44:1249-1258
Perspectives in Diabetes

John W Baynes
Apr 1, 1991; 40:405-412
Perspectives in Diabetes

Ralph A DeFronzo
Jun 1, 1988; 37:667-687
Lilly Lecture 1987

Steven E Kahn
Nov 1, 1993; 42:1663-1672
Original Article

JC Henquin
Nov 1, 2000; 49:1751-1760
Articles

David E. Kelley
Oct 1, 2002; 51:2944-2950
Metabolism and Signal Transduction

BM Spiegelman
Apr 1, 1998; 47:507-514
Articles

HE Hohmeier
Mar 1, 2000; 49:424-430
Articles

Patrice D. Cani
Jun 1, 2008; 57:1470-1481
Metabolism

Michael Brownlee
Jun 1, 2005; 54:1615-1625
Banting Lecture 2004

Patrice D. Cani
Jul 1, 2007; 56:1761-1772
Obesity Studies

National Diabetes Data Group
Dec 1, 1979; 28:1039-1057
Articles

Alexandra E. Butler
Jan 1, 2003; 52:102-110
Islet Studies

Gerald M Reaven
Dec 1, 1988; 37:1595-1607
Banting Lecture 1988

Night shift work, behavioral rhythms, and the common MTNR1B risk single nucleotide polymorphism (SNP), rs10830963, associate with type 2 diabetes; however, whether they exert joint effects to exacerbate type 2 diabetes risk is unknown. Among employed participants of European ancestry in the UK Biobank (N = 189,488), we aimed to test the cross-sectional independent associations and joint interaction effects of these risk factors on odds of type 2 diabetes (n = 5,042 cases) and HbA_1c levels (n = 175,156). Current shift work, definite morning or evening preference, and MTNR1B rs10830963 risk allele associated with type 2 diabetes and HbA_1c levels. The effect of rs10830963 was not modified by shift work schedules. While marginal evidence of interaction between self-reported morningness-eveningness preference and rs10830963 on risk of type 2 diabetes was seen, this interaction did not persist when analysis was expanded to include all participants regardless of employment status and when accelerometer-derived sleep midpoint was used as an objective measure of morningness-eveningness preference. Our findings suggest that MTNR1B risk allele carriers who carry out shift work or have more extreme morningness-eveningness preference may not have enhanced risk of type 2 diabetes.

Lipodystrophies are a group of disorders characterized by absence or loss of adipose tissue and abnormal fat distribution, commonly accompanied by metabolic dysregulation. Although considered rare disorders, their prevalence in the general population is not well understood. We aimed to evaluate the clinical and genetic prevalence of lipodystrophy disorders in a large clinical care cohort. We interrogated the electronic health record (EHR) information of >1.3 million adults from the Geisinger Health System for lipodystrophy diagnostic codes. We estimate a clinical prevalence of disease of 1 in 20,000 individuals. We performed genetic analyses in individuals with available genomic data to identify variants associated with inherited lipodystrophies and examined their EHR for comorbidities associated with lipodystrophy. We identified 16 individuals carrying the p.R482Q pathogenic variant in LMNA associated with Dunnigan familial partial lipodystrophy. Four had a clinical diagnosis of lipodystrophy, whereas the remaining had no documented clinical diagnosis despite having accompanying metabolic abnormalities. We observed a lipodystrophy-associated variant carrier frequency of 1 in 3,082 individuals in our cohort with substantial burden of metabolic dysregulation. We estimate a genetic prevalence of disease of ~1 in 7,000 in the general population. Partial lipodystrophy is an underdiagnosed condition. and its prevalence, as defined molecularly, is higher than previously reported. Genetically guided stratification of patients with common metabolic disorders, like diabetes and dyslipidemia, is an important step toward precision medicine.

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

The placenta participates in maternal insulin sensitivity changes during pregnancy; however, mechanisms remain unclear. We investigated associations between maternal insulin sensitivity and placental DNA methylation markers across the genome. We analyzed data from 430 mother-offspring dyads in the Gen3G cohort. All women underwent 75-g oral glucose tolerance tests at ~26 weeks of gestation; we used glucose and insulin measures to estimate insulin sensitivity (Matsuda index). At delivery, we collected samples from placenta (fetal side) and measured DNA methylation using Illumina EPIC arrays. Using linear regression models to quantify associations at 720,077 cytosine-guanine dinucleotides (CpGs), with adjustment for maternal age, gravidity, smoking, BMI, child sex, and gestational age at delivery, we identified 188 CpG sites where placental DNA methylation was associated with Matsuda index (P < 6.94 x 10^–8). Among genes annotated to these 188 CpGs, we found enrichment in targets for miRNAs, in histone modifications, and in parent-of-origin DNA methylation including the H19/MIR675 locus (paternally imprinted). We identified 12 known placenta imprinted genes, including KCNQ1. Mendelian randomization analyses revealed five loci where placenta DNA methylation may causally influence maternal insulin sensitivity, including the maternally imprinted gene DLGAP2. Our results suggest that placental DNA methylation is fundamentally linked to the regulation of maternal insulin sensitivity in pregnancy.

Permanent neonatal diabetes mellitus (PNDM) is caused by reduced β-cell number or impaired β-cell function. Understanding of the genetic basis of this disorder highlights fundamental β-cell mechanisms. We performed trio genome sequencing for 44 patients with PNDM and their unaffected parents to identify causative de novo variants. Replication studies were performed in 188 patients diagnosed with diabetes before 2 years of age without a genetic diagnosis. EIF2B1 (encoding the eIF2B complex α subunit) was the only gene with novel de novo variants (all missense) in at least three patients. Replication studies identified two further patients with de novo EIF2B1 variants. In addition to having diabetes, four of five patients had hepatitis-like episodes in childhood. The EIF2B1 de novo mutations were found to map to the same protein surface. We propose that these variants render the eIF2B complex insensitive to eIF2 phosphorylation, which occurs under stress conditions and triggers expression of stress response genes. Failure of eIF2B to sense eIF2 phosphorylation likely leads to unregulated unfolded protein response and cell death. Our results establish de novo EIF2B1 mutations as a novel cause of permanent diabetes and liver dysfunction. These findings confirm the importance of cell stress regulation for β-cells and highlight EIF2B1’s fundamental role within this pathway.