| 1. |
- Lyssenko, Valeriya, et al.
(author)
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Genetics of diabetes-associated microvascular complications
- 2023
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In: Diabetologia. - 0012-186X. ; 66:9, s. 1601-1613
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Research review (peer-reviewed)abstract
- Diabetes is associated with excess morbidity and mortality due to both micro- and macrovascular complications, as well as a range of non-classical comorbidities. Diabetes-associated microvascular complications are those considered most closely related to hyperglycaemia in a causal manner. However, some individuals with hyperglycaemia (even those with severe hyperglycaemia) do not develop microvascular diseases, which, together with evidence of co-occurrence of microvascular diseases in families, suggests a role for genetics. While genome-wide association studies (GWASs) produced firm evidence of multiple genetic variants underlying differential susceptibility to type 1 and type 2 diabetes, genetic determinants of microvascular complications are mostly suggestive. Identified susceptibility variants of diabetic kidney disease (DKD) in type 2 diabetes mirror variants underlying chronic kidney disease (CKD) in individuals without diabetes. As for retinopathy and neuropathy, reported risk variants currently lack large-scale replication. The reported associations between type 2 diabetes risk variants and microvascular complications may be explained by hyperglycaemia. More extensive phenotyping, along with adjustments for unmeasured confounding, including both early (fetal) and late-life (hyperglycaemia, hypertension, etc.) environmental factors, are urgently needed to understand the genetics of microvascular complications. Finally, genetic variants associated with reduced glycolysis, mitochondrial dysfunction and DNA damage and sustained cell regeneration may protect against microvascular complications, illustrating the utility of studies in individuals who have escaped these complications.
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| 2. |
- Lyssenko, Valeriya, et al.
(author)
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Genetics of type 2 diabetes : It matters from which parent we inherit the risk
- 2015
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In: Review of Diabetic Studies. - 1613-6071. ; 12:3-4, s. 233-242
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Research review (peer-reviewed)abstract
- Type 2 diabetes (T2D) results from a co-occurrence of genes and environmental factors. There are more than 120 genetic loci suggested to be associated with T2D, or with glucose and insulin levels in European and multi-ethnic populations. Risk of T2D is higher in the offspring if the mother rather than the father has T2D. Genetically, this can be associated with a unique parent-of-origin (PoO) transmission of risk alleles, and it relates to genetic programming during the intrauterine period, resulting in the inability to increase insulin secretion in response to increased demands imposed by insulin resistance later in life. Such PoO transmission is seen for variants in the KLF14, KCNQ1, GRB10, TCF7L2, THADA, and PEG3 genes. Here we describe T2D susceptibility genes associated with defects in insulin secretion, and thereby risk of overt T2D. This review emphasizes the need to consider distorted parental transmission of risk alleles by exploring the genetic risk of T2D.
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| 3. |
- Salanti, Georgia, et al.
(author)
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Underlying Genetic Models of Inheritance in Established Type 2 Diabetes Associations
- 2009
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In: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 170:5, s. 537-545
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Research review (peer-reviewed)abstract
- For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.
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