| 1. |
- Halban, Philippe A, et al.
(author)
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β-Cell Failure in Type 2 Diabetes: Postulated Mechanisms and Prospects for Prevention and Treatment.
- 2014
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In: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 37:6, s. 1751-1758
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Journal article (peer-reviewed)abstract
- This article examines the foundation of β-cell failure in type 2 diabetes (T2D) and suggests areas for future research on the underlying mechanisms that may lead to improved prevention and treatment.RESEARCH DESIGN AND METHODS: A group of experts participated in a conference on 14-16 October 2013 cosponsored by the Endocrine Society and the American Diabetes Association. A writing group prepared this summary and recommendations.RESULTS: The writing group based this article on conference presentations, discussion, and debate. Topics covered include genetic predisposition, foundations of β-cell failure, natural history of β-cell failure, and impact of therapeutic interventions.CONCLUSIONS: β-Cell failure is central to the development and progression of T2D. It antedates and predicts diabetes onset and progression, is in part genetically determined, and often can be identified with accuracy even though current tests are cumbersome and not well standardized. Multiple pathways underlie decreased β-cell function and mass, some of which may be shared and may also be a consequence of processes that initially caused dysfunction. Goals for future research include to 1) impact the natural history of β-cell failure; 2) identify and characterize genetic loci for T2D; 3) target β-cell signaling, metabolic, and genetic pathways to improve function/mass; 4) develop alternative sources of β-cells for cell-based therapy; 5) focus on metabolic environment to provide indirect benefit to β-cells; 6) improve understanding of the physiology of responses to bypass surgery; and 7) identify circulating factors and neuronal circuits underlying the axis of communication between the brain and β-cells.
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| 2. |
- Halban, Philippe A, et al.
(author)
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β-cell Failure in Type 2 Diabetes: Postulated Mechanisms and Prospects for Prevention and Treatment.
- 2014
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In: Journal of Clinical Endocrinology and Metabolism. - : The Endocrine Society. - 1945-7197 .- 0021-972X. ; 99:6, s. 1983-1992
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Journal article (peer-reviewed)abstract
- Objective: This report examines the foundation of β-cell failure in type 2 diabetes and suggests areas for future research on the underlying mechanisms that may lead to improved prevention and treatment. Participants: A group of experts participated in a conference on October 14-16, 2013 cosponsored by The Endocrine Society and the American Diabetes Association. A writing group prepared this summary and recommendations. Evidence: The writing group based this report on conference presentations, discussion, and debate. Topics covered include genetic predisposition, the foundations of β-cell failure, natural history of β-cell failure, and impact of therapeutic interventions. Conclusions: β-cell failure is central to the development and progression of type 2 diabetes. It antedates and predicts diabetes onset and progression, is in part genetically determined, and often can be identified with accuracy even though current tests are cumbersome and not well standardized. Multiple pathways underlie decreased β-cell function and mass, some of which may be shared and may also be a consequence of processes that initially caused dysfunction. Goals for future research include: 1) Impact the natural history of β-cell failure; 2) Identify and characterize genetic loci for type 2 diabetes; 3) Target β-cell signaling, metabolic, and genetic pathways to improve function/mass; 4) Develop alternative sources of β-cells for cell-based therapy; 5) Focus on metabolic environment to provide indirect benefit to β-cells; 6) Improve understanding of the physiology of responses to bypass surgery; 7) Identify circulating factors and neuronal circuits underlying the axis of communication between the brain and β-cells.
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| 3. |
- Maxwell, Taylor J., et al.
(author)
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Quantitative trait loci, G×E and G×G for glycemic traits : response to metformin and placebo in the Diabetes Prevention Program (DPP)
- 2022
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In: Journal of Human Genetics. - : Springer Science and Business Media LLC. - 1434-5161 .- 1435-232X. ; 67:8, s. 465-473
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Journal article (peer-reviewed)abstract
- The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify G×E and G×G, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging G×G and G×E). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 × 10−7) rQTL and vQTL generated a priori hypotheses of individual G×E tests for a SNP × metformin treatment interaction and secondarily for G×G screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment × SNP interactions (4 for IGI, one insulin, and one glucose) and 12G×G interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 × 10−9). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait’s relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, a gene affecting β-cell apoptosis and insulin secretion. This rQTL may link MST1 with insulin sensitivity where ISI and insulin responses differentially vary by genotype. This study demonstrates evidence for context-dependent effects (G×G & G×E) and the complexity of these T2D-related traits.
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| 4. |
- Hivert, Marie-France, et al.
(author)
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Lifestyle and metformin ameliorate insulin sensitivity independently of the genetic burden of established insulin resistance variants in Diabetes Prevention Program participants.
- 2016
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In: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 65:2, s. 520-526
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Journal article (peer-reviewed)abstract
- Genome-wide association studies of glycemic traits have identified genetics variants that are associated with insulin resistance (IR) in the general population. It is unknown if people with genetic enrichment for these IR-variants respond differently to interventions that aim to improve insulin sensitivity. We built a genetic risk score based on 17 established IR-variants and their effect sizes (weighted IR-GRS) in 2,713 participants of the Diabetes Prevention Program (DPP) with genetic consent. We tested associations between the weighted IR-GRS and insulin sensitivity index (ISI) at baseline in all participants, and with change in ISI over 1-year of follow-up in DPP intervention (metformin and lifestyle) and control (placebo) arms. All models were adjusted for age, sex, ethnicity, and waist circumference at baseline (plus baseline ISI for 1-year ISI change models). A higher IR-GRS was associated with lower baseline ISI (β= -0.754 [SE=0.229] log-ISI per unit; P=0.001 in fully adjusted models). There was no differential effect of treatment for the association between IR-GRS on change in ISI; higher IR-GRS was associated with attenuation in ISI improvement over 1 year (β= -0.520 [SE=0.233]; P=0.03 in fully adjusted models; all treatment arms). Lifestyle intervention and metformin improved ISI, regardless of the genetic burden of IR-variants.
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| 5. |
- Varga, Tibor V., et al.
(author)
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Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes : A machine learning approach in the Diabetes Prevention Program
- 2021
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In: BMJ Open Diabetes Research and Care. - : BMJ. - 2052-4897. ; 9:1
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Journal article (peer-reviewed)abstract
- Introduction Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes. Research design and methods Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility. Results Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study. Conclusions NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study. Trial registration number Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.
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