Association of parental history of diabetes with cardiovascular disease risk factors in children with type 2 diabetes

Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985-2015 Swedish birth cohort

The goal is to examine the risk of conception mode-type-1 diabetes using different survival analysis modelling approaches and examine if there are differentials in the risk of type-1 diabetes between children from fresh and frozen-thawed embryo transfers. We aimed to compare the performances and fitness of different survival analysis regression models with the Cox proportional hazard (CPH) model used in an earlier study. The effect of conception modes and other prognostic factors on type-1 diabetes among children conceived either spontaneously or by assisted reproductive technology (ART) and its sub-groups was modelled in the earlier study. We used the information on all singleton children from the Swedish Medical Birth Register hosted by the Swedish National Board of Health and Welfare, 1985 to 2015. The main explanatory variable was the mode of conception. We applied the CPH, parametric and flexible parametric survival regression (FPSR) models to the data at 5% significance level. Loglikelihood, Akaike and Bayesian information criteria were used to assess model fit. Among the 3,138,540 singletons, 47,938 (1.5%) were conceived through ART (11,211 frozen-thawed transfer and 36,727 fresh embryo transfer). In total, 18,118 (0.58%) of the children had type-1 diabetes, higher among (0.58%) those conceived spontaneously than the ART-conceived (0.42%). The median (Interquartile range (IQR)) age at onset of type-1 diabetes among spontaneously conceived children was 10 (14-6) years, 8(5-12) for ART, 6 (4-10) years for frozen-thawed embryo transfer and 9 (5-12) years for fresh embryo transfer. The estimates from the CPH, FPSR and parametric PH models are similar. There was no significant difference in the risk of type-1 diabetes among ART- and spontaneously conceived children; FPSR: (adjusted Hazard Ratio (aHR) = 1.070; 95% Confidence Interval (CI):0.929-1.232, p = 0.346) vs CPH: (aHR = 1.068; 95%CI: 0.927-1.230, p = 0.361). A sub-analysis showed that the adjusted hazard of type-1 diabetes was 37% (aHR = 1.368; 95%CI: 1.013-1.847, p = 0.041) higher among children from frozen-thawed embryo transfer than among children from spontaneous conception. The hazard of type-1 diabetes was higher among children whose mothers do not smoke (aHR = 1.296; 95%CI:1.240-1.354, p<0.001) and of diabetic mothers (aHR = 6.419; 95%CI:5.852-7.041, p<0.001) and fathers (aHR = 8.808; 95%CI:8.221-9.437, p<0.001). The estimates from the CPH, parametric models and the FPSR model were close. This is an indication that the models performed similarly and any of them can be used to model the data. We couldn't establish that ART increases the risk of type-1 diabetes except when it is subdivided into its two subtypes. There is evidence of a greater risk of type-1 diabetes when conception is through frozen-thawed transfer.

Comparison of the effects of sibling and parental history of type 2 diabetes on metabolic syndrome

The aim of this study was to investigate the associations between sibling history, parental history and simultaneous sibling and parental history of diabetes, and the presence of the metabolic syndrome (MetS) and its components. Our study comprised 5000 participants from Taiwan Biobank until April, 2014. The participants were stratified into four groups according to sibling and/or parental family history (FH) of DM. MetS was defined as having 3 of the following 5 abnormalities based on the standard of the NCEP ATP III and modified criteria for Asians. The prevalence of MetS and its traits was estimated and compared among the four familial risk strata. Multivariate logistic regression analysis showed participants with sibling FH of DM [vs. no FH of DM; odds ratio (OR) 1.815; 95% confidence interval (CI) 1.293 to 2.548; p = 0.001], participants with parental FH of DM (vs. no FH of DM; OR 1.771; 95% CI 1.468 to 2.135; p < 0.001), and participants with simultaneous sibling and parental FH of DM (vs. no FH of DM; OR 2.961; 95% CI 2.108 to 4.161; p < 0.001) were significantly associated with MetS. A synergistic effect of sibling FH of DM and parental FH of DM on the association of MetS was also observed. In a nationally representative sample of Taiwan adults, a simultaneous sibling and parental history of diabetes shows a significant, independent association with MetS and its components, except for abdominal obesity. The association highlights the importance of obtaining stratified FH information in clinical practice and may help to identify individuals who should be targeted for screening and early prevention of MetS.

Impact of Family History of Diabetes on Diabetes Control and Complications

OBJECTIVE

Our aim was to assess the impact of parental and sibling history of type 2 diabetes (T2D) on patient characteristics, glycemic control, and T2D complications.

METHODS

This cross-sectional study included adults with T2D. Type 1 diabetes and gestational diabetes patients were excluded. The laboratory data were retrieved from the patients' electronic files, and baseline measurements were obtained by the researchers.

RESULTS

The study included a total of 511 T2D patients, with a mean age of 60.1 ± 10.9 years and mean hemoglobin A1c of 8.94 ± 2.1% (74.2 ± 22.9 mmol/mol). Of these patients, 54% were male and 49.7% had a parental history of T2D. The patients with parental history of T2D were diagnosed at a younger age and had a higher body mass index (BMI) ( P = .035) and higher waist circumference (WC) ( P = .013) than those T2D patients with no parental history. Approximately 60% of the participants had siblings with a history of T2D, and in comparison with those with no sibling history, they had higher prevalence of cerebrovascular accidents ( P = .02).

CONCLUSION

Having a parental history of T2D is significantly associated with diagnosis at a younger age and a higher BMI and WC. Having a sibling history of T2D is significantly associated with worse cerebrovascular outcome.

ABBREVIATIONS

ACR = albumin to creatinine ratio; BMI = body mass index; DBP = diastolic blood pressure; DM = diabetes mellitus; FBG = fasting blood glucose; GFR = glomerular filtration rate; HbA1c = hemoglobin A1c; LDL = low-density lipoprotein; SBP = systolic blood pressure; T2D = type 2 diabetes; TG = triglyceride; WC = waist circumference.

Type 2 Diabetes in Youth: New Lessons from the SEARCH Study

PURPOSE OF REVIEW

The purpose of this review is to provide an update on the recent body of evidence emerging for type 2 diabetes as identified through the SEARCH for Diabetes in Youth study.

RECENT FINDINGS

This body of evidence illustrates that type 2 diabetes continues to increase in incidence, although this increase may be partially attributable to increased surveillance. Disease management is influenced by the transition from adolescent to adult care and psychosocial factors may also contribute. This evidence also describes a high prevalence of disease-associated complications and comorbidities. Risk factors for cardiovascular disease are also highly prevalent. The SEARCH for Diabetes in Youth study continues to inform our understanding of the descriptive epidemiology and natural history of type 2 diabetes in youth. As the cohort matures, new opportunities emerge for building on our understanding of how youth-onset type 2 diabetes impacts future health.

Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication

The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques toward the development of personalized models able to predict the risk of fatal or nonfatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a subsampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.