Diagnosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is associated with a high risk of obstetric and neonatal complications. Adequate diagnosis and appropriate treatment are key to prevention of these complications. Most international guidelines have adopted the IADPSG 2010/WHO 2013 diagnostic criteria, for the diagnosis of GDM by recommending the following glycemic thresholds for a 75 g OGTT: fasting plasma glucose value =5.1 mmol/l (92 mg/dl); 1-h value =10.0 mmol/l (180 mg/dl); and 2-h value =8.5 mmol/l (153 mg/dl). These specific cut-off values were chosen because they predict a 75% higher chance of adverse pregnancy outcomes compared to normal glucose values. Some countries have adopted either higher levels for fasting glucose (5.6 or 7.0 mmol/l) or lower levels for 2-h post-OGTT glucose (7.8 mmol/l). There still is some debate whether it is desirable to lower the diagnostic 2-h glucose thresholds to =7.5 mmol/l for Caucasian women and =7.2 mmol/l for women from South Asian background. Several studies have shown that about 20-30% (depending on the applied diagnostic criteria) of the women screened for GDM had/have abnormal OGTT results, necessitating referral, active counseling, and treatment. By adopting the new IADPSG/WHO diagnostic criteria, the prevalence of GDM has increased, which has a major impact on the costs and the capacity of healthcare systems. Screening for GDM may follow either a one-step or a two-step approach. In the one-step approach, GDM is diagnosed based on the results of a single 75 g OGTT. The two-step screening strategy makes use of a non-fasting 50 g glucose challenge test (GCT), whereby an abnormal test result (i.e., a 1-h plasma glucose value =7.8 mmol/l) is followed by a 100 g OGTT. There also is no international consensus on whether universal or risk factor-based screening is preferred. Universal screening implies that all pregnant women will undergo screening between 24 and 28 weeks of pregnancy, while in selective screening, only women who have specific risk factors for developing GDM or who exhibit a possible consequence of hyperglycemia, i.e., macrosomia or polyhydramnios, will undergo an OGTT. Most studies comparing these strategies have mainly reported data on GDM classification, not on GDM treatment or, even better, pregnancy outcomes. Some countries, therefore, follow a hybrid approach of partly risk factor-based and partly universal screening. Recently published systematic economic evaluations support universal screening and the one-step approach as a more likely cost-effective strategy.</p

From shared care to disease management: key-influencing factors

BACKGROUND: In order to improve the quality of care of chronically ill patients the traditional boundaries between primary and secondary care are questioned. To demolish these boundaries so-called ‘shared care’ projects have been initiated in which different ways of substitution of care are applied. When these projects end, disease management may offer a solution to expand the achieved co-operation between primary and secondary care. OBJECTIVE: Answering the question: What key factors influence the development and implementation of shared care projects from a management perspective and how are they linked? THEORY: The theoretical framework is based on the concept of the learning organisation. DESIGN: Reference point is a multiple case study that finally becomes a single case study. Data are collected by means of triangulation. The studied cases concern two interrelated Dutch shared care projects for type 2 diabetic patients, that in the end proceed as one disease management project. RESULTS: In these cases the predominant key-influencing factors appear to be the project management, commitment and local context, respectively. The factor project management directly links the latter two, albeit managing both appear prerequisites to its success. In practice this implies managing the factors' interdependency by the application of change strategies and tactics in a committed and skilful way. CONCLUSION: Project management, as the most important and active key factor, is advised to cope with the interrelationships of the influencing factors in a gradually more fundamental way by using strategies and tactics that enable learning processes. Then small-scale shared care projects may change into a disease management network at a large scale, which may yield the future blueprint to proceed

A non-invasive risk score including skin autofluorescence predicts diabetes risk in the general population

Abstract Increased skin autofluorescence (SAF) predicts the development of diabetes-related complications and cardiovascular disease. We assessed the performance of a simple model which includes SAF to identify individuals at high risk for undiagnosed and incident type 2 diabetes, in 58,377 participants in the Lifelines Cohort Study without known diabetes. Newly-diagnosed diabetes was defined as fasting blood glucose ≥ 7.0 mmol/l and/or HbA1c ≥ 6.5% (≥ 48 mmol/mol) or self-reported diabetes at follow-up. We constructed predictive models based on age, body mass index (BMI), SAF, and parental history of diabetes, and compared to results with the concise FINDRISC model. At 2nd visit to Lifelines, 1113 (1.9%) participants were identified with undiagnosed diabetes and 1033 (1.8%) participants developed diabetes during follow-up. A model comprising age, BMI and SAF yielded an AUC of 0.783 and was non-inferior to the concise FINDRISC model, which had an AUC of 0.797 to predict new diabetes. At a score of 5.8, sensitivity was 78% and specificity of 66%. Model 2 which also incorporated parental diabetes history, had an AUC of 0.792, and a sensitivity of 74% and specificity of 70% at a score of 6.5. Net reclassification index (NRI) did not improve significantly (NRI 1.43% (− 0.50–3.37 p = 0.15). The combination of an easy to perform SAF measurement with age and BMI is a good alternative screening tool suitable for medical and non-medical settings. Parental history of diabetes did not significantly improve model performance in this homogeneous cohort

Communication of an Abnormal Metabolic New-Born Screening Result in The Netherlands:The Parental Perspective

In the Netherlands, abnormal New-Born Screening (NBS) results are communicated to parents by the general practitioner (GP). Good communication and consequential trust in professionals is of the utmost importance in the treatment of phenylketonuria (PKU). The aim of this study was to assess parental satisfaction regarding the communication of an abnormal NBS result for PKU in the Netherlands. An email containing the link to a web-based questionnaire was sent by the Dutch PKU Association to their members. Responses to open questions were categorized, data of both open and closed questions were analysed with descriptive statistics and the Chi-Square test using SPSS. Out of 113 parents of a child with PKU (born between 1979 and 2020), 68 stated they were overall unsatisfied with the first communication of the NBS result. Seventy-five parents indicated that wrong or no information about PKU was given. A significant decrease was found in the number of parents being contact by their own GP over the course of 40 years (p < 0.05). More than half of all parents were overall unsatisfied with the first communication of the abnormal NBS result for PKU. Further research on how to optimize communication of an abnormal NBS results is necessary

Genome-wide association study identifies novel loci associated with skin autofluorescence in individuals without diabetes

BACKGROUND: Skin autofluorescence (SAF) is a non-invasive measure reflecting accumulation of advanced glycation endproducts (AGEs) in the skin. Higher SAF levels are associated with an increased risk of developing type 2 diabetes and cardiovascular disease. An earlier genome-wide association study (GWAS) revealed a strong association between NAT2 variants and SAF. The aim of this study was to calculate SAF heritability and to identify additional genetic variants associated with SAF through genome-wide association studies (GWAS). RESULTS: In 27,534 participants without diabetes the heritability estimate of lnSAF was 33% ± 2.0% (SE) in a model adjusted for covariates. In meta-GWAS for lnSAF five SNPs, on chromosomes 8, 11, 15 and 16 were associated with lnSAF (P  T), which results in a Met30Val missense variant in MMP27 exon 1 (NM_022122.3); 2. rs2470893 (Chr15:75,019,449,C > T), in intergenic region between CYP1A1 and CYP1A2; with attenuation of the SNP-effect when coffee consumption was included as a covariate; 3. rs12931267 (Chr16:89,818,732,C > G) in intron 30 of FANCA and near MC1R; and following conditional analysis 4. rs3764257 (Chr16:89,800,887,C > G) an intronic variant in ZNF276, 17.8 kb upstream from rs12931267; finally, 30 kb downstream from NAT2 5. rs576201050 (Chr8:18,288,053,G > A). CONCLUSIONS: This large meta-GWAS revealed five SNPs at four loci associated with SAF in the non-diabetes population. Further unravelling of the genetic architecture of SAF will help in improving its utility as a tool for screening and early detection of diseases and disease complications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-09062-x

Development and validation of decision rules models to stratify coronary artery disease, diabetes, and hypertension risk in preventive care:Cohort study of returning UK biobank participants

Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated

Developing Effective Questionnaire-Based Prediction Models for Type 2 Diabetes for Several Ethnicities

Background: Type 2 diabetes disproportionately affects individuals of non-white ethnicity through a complex interaction of multiple factors. Early disease prediction and detection is therefore essential and requires tools that can be deployed at large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes for multiple ethnicities.Methods: Logistic regression models, using questionnaire-only features, were trained on the White population of the UK Biobank, and validated in five other ethnicities and externally in Lifelines. In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Predictive accuracy was assessed and a detailed sensitivity analysis was conducted to assess potential clinical utility. Furthermore, we compared the questionnaire algorithms to clinical non-laboratory type 2 diabetes risk tools.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC=0·901) and eight-year incidence (AUC=0·873) in the White UK Biobank population. Both models replicate well in Lifelines, with AUCs of 0·917 and 0·817 for prevalence and incidence. Both models performed consistently well across ethnicities, with AUCs of 0·855 to 0·894 for prevalence and from 0·819 to 0·883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified &gt;3,000 type 2 diabetes cases. Model performance improved with the addition of blood biomarkers, but not with the addition of physical measurements.Interpretation: Easy-to-implement, questionnaire-based models can predict prevalent and incident type 2 diabetes with high accuracy across all ethnicities, providing a highly-scalable solution for population-wide risk stratification

Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes

Plasma levels of liver enzymes provide insights into hepatic function and related diseases. Here, the authors perform a genome-wide association study on three liver enzymes, identifying genetic variants associated with their plasma concentration as well as links to metabolic and cardiovascular diseases. Serum concentration of hepatic enzymes are linked to liver dysfunction, metabolic and cardiovascular diseases. We perform genetic analysis on serum levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) using data on 437,438 UK Biobank participants. Replication in 315,572 individuals from European descent from the Million Veteran Program, Rotterdam Study and Lifeline study confirms 517 liver enzyme SNPs. Genetic risk score analysis using the identified SNPs is strongly associated with serum activity of liver enzymes in two independent European descent studies (The Airwave Health Monitoring study and the Northern Finland Birth Cohort 1966). Gene-set enrichment analysis using the identified SNPs highlights involvement in liver development and function, lipid metabolism, insulin resistance, and vascular formation. Mendelian randomization analysis shows association of liver enzyme variants with coronary heart disease and ischemic stroke. Genetic risk score for elevated serum activity of liver enzymes is associated with higher fat percentage of body, trunk, and liver and body mass index. Our study highlights the role of molecular pathways regulated by the liver in metabolic disorders and cardiovascular disease

Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to similar to 370,000 women, we identify 389 independent signals (P <5 x 10(-8)) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain similar to 7.4% of the population variance in age at menarche, corresponding to similar to 25% of the estimated heritability. We implicate similar to 250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility

A non-invasive risk score including skin autofluorescence predicts diabetes risk in the general population

Increased skin autofluorescence (SAF) predicts the development of diabetes-related complications and cardiovascular disease. We assessed the performance of a simple model which includes SAF to identify individuals at high risk for undiagnosed and incident type 2 diabetes, in 58,377 participants in the Lifelines Cohort Study without known diabetes. Newly-diagnosed diabetes was defined as fasting blood glucose ≥ 7.0 mmol/l and/or HbA1c ≥ 6.5% (≥ 48 mmol/mol) or self-reported diabetes at follow-up. We constructed predictive models based on age, body mass index (BMI), SAF, and parental history of diabetes, and compared to results with the concise FINDRISC model. At 2nd visit to Lifelines, 1113 (1.9%) participants were identified with undiagnosed diabetes and 1033 (1.8%) participants developed diabetes during follow-up. A model comprising age, BMI and SAF yielded an AUC of 0.783 and was non-inferior to the concise FINDRISC model, which had an AUC of 0.797 to predict new diabetes. At a score of 5.8, sensitivity was 78% and specificity of 66%. Model 2 which also incorporated parental diabetes history, had an AUC of 0.792, and a sensitivity of 74% and specificity of 70% at a score of 6.5. Net reclassification index (NRI) did not improve significantly (NRI 1.43% (− 0.50–3.37 p = 0.15). The combination of an easy to perform SAF measurement with age and BMI is a good alternative screening tool suitable for medical and non-medical settings. Parental history of diabetes did not significantly improve model performance in this homogeneous cohort