scholarly journals Clinical and Genetic Risk Factors for Type 2 Diabetes at Early or Late Post Partum After Gestational Diabetes Mellitus

2013 ◽  
Vol 98 (4) ◽  
pp. E744-E752 ◽  
Author(s):  
Soo Heon Kwak ◽  
Sung Hee Choi ◽  
Hye Seung Jung ◽  
Young Min Cho ◽  
Soo Lim ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Genetic Risk ◽  
Post Partum ◽  
Genetic Risk Factors

scholarly journals Circulating progranulin levels in women with gestational diabetes mellitus and healthy controls during and after pregnancy

2012 ◽  
Vol 167 (4) ◽  
pp. 561-567 ◽  
Author(s):  
Jelena Todoric ◽  
Ammon Handisurya ◽  
Thomas Perkmann ◽  
Bernhard Knapp ◽  
Oswald Wagner ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Gestational Diabetes ◽  
Glucose Tolerance ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Post Partum ◽  
Oral Glucose ◽  
Positive Correlation

ObjectiveProgranulin (PGRN) was recently introduced as a novel marker of chronic inflammatory response in obesity and type 2 diabetes capable of directly affecting the insulin signaling pathway. This study aimed to investigate the role of PGRN in gestational diabetes mellitus (GDM), which is regarded as a model for early type 2 diabetes.MethodsPGRN serum levels were measured in 90 pregnant women (45 GDM and 45 normal glucose tolerance (NGT)). In addition, PGRN was measured during a 2-h, 75 g oral glucose tolerance test in 20 pregnant women (ten GDM and ten NGT) and in 16 of them post partum (ten GDM and six NGT).ResultsPGRN concentrations were significantly higher in pregnant women compared with post partum levels (536.79±31.81 vs 241.53±8.86, P<0.001). Multivariate regression analyses showed a strong positive correlation of PGRN with estrogen and progesterone. The insulinogenic index, a marker of early insulin secretion, displayed a positive correlation with PGRN, both during and after pregnancy (R=0.47, P=0.034; R=0.63, P=0.012). HbA1c and the oral glucose insulin sensitivity index showed significant post partum associations with PGRN (R=0.43, P=0.049; R=−0.65, P=0.009).ConclusionsPGRN concentrations are markedly lower after pregnancy regardless of the gestational glucose tolerance state. PGRN levels per se do not discriminate between mild GDM and NGT in pregnant women. Therefore, the development of GDM appears to be due to impaired β-cell function that is not related to PGRN effect.

scholarly journals 184Are genetic risk factors clinically useful in prognostic models for type 2 diabetes mellitus?

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Vera Helen Buss ◽  
Margo Barr ◽  
Marlien Varnfield ◽  
Mark Harris
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Genetic Risk ◽  
Prediction Models ◽  
Genetic Risk Factors ◽  
Genetic Models ◽  
Prognostic Models

Abstract Background Many prognostic models exist for the prediction of type 2 diabetes mellitus. Some of these include genetic risk factors. The objective of this review was to investigate whether including genetic variables in prediction models for type 2 diabetes mellitus is clinically useful. Methods Studies were included if they reported on prognostic prediction models for type 2 diabetes mellitus. Seven medical and bioengineering databases were searched using terms regarding risk prediction and diabetes combined via Boolean operators. In studies including genetic variables, the c-statistics of genetic and non-genetic models were compared. Results Seventy-six studies published between 2002 and 2019 were included in the review. Twenty of these (published 2008 to 2019) included genetic variables, namely deleterious alleles and specific small nuclear polymorphisms. Study samples represented the general population. When comparing genetic to non-genetic models, one study reported a statistically significantly greater c-statistic for the genetic and four for the non-genetic models. Adding genetic risk factors to a clinical model did not substantially increase the predictive accuracy of any study. Conclusions The use of genetic data did not show any meaningful improvements in the predictive performance for the general population compared to clinical models. Studies using genetic data were often based on small sample sizes and not externally validated, raising concerns regarding potential overfitting and lack of generalisability. Key messages Currently, the clinical usefulness of genetic risk scores for type 2 diabetes mellitus seems quite limited.

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