Association of parental history of type 2 diabetes with age, lifestyle, anthropometric factors, and clinical severity at type 2 diabetes diagnosis: results from the DD2 study

Associations of Clinical Risk Factors and Novel Biomarkers With Age at Onset of Type 2 Diabetes

CONTEXT

Younger onset of type 2 diabetes (T2D) was associated with higher risks of vascular complications and mortality.

OBJECTIVE

To prospectively assess risk profiles for incident T2D stratified by age at onset.

METHODS

A total of 471 269 participants free of T2D at baseline were included from the UK Biobank. Approximately 70 clinical, lipid, lipoprotein, inflammatory, and metabolic markers, and genetic risk scores (GRSs) were analyzed. Stratified Cox proportional-hazards regression models were used to estimate hazard ratios (HRs) for T2D with age of diagnosis divided into 4 groups (≤50.0, 50.1-60.0, 60.1-70.0, and >70.0 years).

RESULTS

During 11 years of follow-up, 15 805 incident T2D were identified. Among clinical risk factors, obesity had the highest HR at any age, ranging from 13.16 (95% CI, 9.67-17.91) for 50.0 years and younger to 4.13 (3.78-4.51) for older than 70.0 years. Other risks associated with T2D onset at age 50.0 years and younger included dyslipidemia (3.50, 2.91-4.20), hypertension (3.21, 2.71-3.80), cardiovascular disease (2.87, 2.13-3.87), parental history of diabetes (2.42, 2.04-2.86), education lower than college (1.89, 1.57-2.27), physical inactivity (1.73, 1.43-2.10), smoking (1.38, 1.13-1.68), several lipoprotein particles, inflammatory markers, liver enzymes, fatty acids, amino acids, as well as GRS. Associations of most risk factors and biomarkers were markedly attenuated with increasing age at onset (P interaction <.05), and some were not significant for onset at age older than 70.0 years, such as smoking, systolic blood pressure, and apolipoprotein B.

CONCLUSION

Most risk factors or biomarkers had stronger relative risks for T2D at younger ages, which emphasizes the necessity of promoting primary prevention among younger individuals. Moreover, obesity should be prioritized.

The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes

AIMS/HYPOTHESIS

The reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults.

METHODS

We analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants.

RESULTS

The T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m²) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantibody-positive adults (all p<0.0001). In both adults and children, type 1 diabetes genetic risk was unaffected by the number of autoantibodies (p>0.3). These findings, along with the identification of seven misclassified adults with monogenic diabetes among autoantibody-negative adults and the results of a sensitivity analysis with and without measurement of ZnT8A, suggest that the intermediate type 1 diabetes genetic risk in autoantibody-negative adults is more likely to be explained by the inclusion of misclassified non-autoimmune diabetes (estimated to represent 67% of all antibody-negative adults, 95% CI 61%, 73%) than by the presence of unmeasured autoantibodies or by a discrete form of diabetes. When these estimated individuals with non-autoimmune diabetes were adjusted for, the prevalence of autoantibody positivity in adult-onset type 1 diabetes was similar to that in children (93% vs 91%, p=0.4).

CONCLUSIONS/INTERPRETATION

The inclusion of non-autoimmune diabetes is the most likely explanation for the observed lower rate of autoantibody positivity in clinician-diagnosed adult-onset type 1 diabetes. Our data support the utility of islet autoantibody measurement in clinician-suspected adult-onset type 1 diabetes in routine clinical practice.

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.

Using electronic health records to quantify and stratify the severity of type 2 diabetes in primary care in England: rationale and cohort study design

INTRODUCTION

The increasing prevalence of type 2 diabetes mellitus (T2DM) presents a significant burden on affected individuals and healthcare systems internationally. There is, however, no agreed validated measure to infer diabetes severity from electronic health records (EHRs). We aim to quantify T2DM severity and validate it using clinical adverse outcomes.

METHODS AND ANALYSIS

Primary care data from the Clinical Practice Research Datalink, linked hospitalisation and mortality records between April 2007 and March 2017 for patients with T2DM in England will be used to develop a clinical algorithm to grade T2DM severity. The EHR-based algorithm will incorporate main risk factors (severity domains) for adverse outcomes to stratify T2DM cohorts by baseline and longitudinal severity scores. Provisionally, T2DM severity domains, identified through a systematic review and expert opinion, are: diabetes duration, glycated haemoglobin, microvascular complications, comorbidities and coprescribed treatments. Severity scores will be developed by two approaches: (1) calculating a count score of severity domains; (2) through hierarchical stratification of complications. Regression models estimates will be used to calculate domains weights. Survival analyses for the association between weighted severity scores and future outcomes-cardiovascular events, hospitalisation (diabetes-related, cardiovascular) and mortality (diabetes-related, cardiovascular, all-cause mortality)-will be performed as statistical validation. The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions. We anticipate that the developed algorithm will be a practical tool for practitioners, aid clinical management decision-making, inform stratified medicine, support future clinical trials and contribute to more effective service planning and policy-making.

ETHICS AND DISSEMINATION

The study protocol was approved by the Independent Scientific Advisory Committee. Some data were presented at the National Institute for Health Research School for Primary Care Research Showcase, September 2017, Oxford, UK and the Diabetes UK Professional Conference March 2018, London, UK. The study findings will be disseminated in relevant academic conferences and peer-reviewed journals.

Danish Centre for Strategic Research in Type 2 Diabetes (DD2) project cohort of newly diagnosed patients with type 2 diabetes: a cohort profile

PURPOSE

The aim of this article is to provide a detailed description of the ongoing nationwide Danish Centre for Strategic Research in Type 2 Diabetes (DD2) project cohort and biobank. The DD2 cohort continuously enrols newly diagnosed patients with type 2 diabetes (T2D) throughout Denmark. The overall goal of the DD2 project is to establish a large and data-rich T2D cohort that can serve as a platform for exhaustive T2D research including (1) improved genotypic and phenotypic characterisation of T2D, (2) intervention studies of more individualised T2D treatment, (3) pharmacoepidemiological studies and (4) long-term follow-up studies on predictors of T2D complications and prognosis.

PARTICIPANTS

Between 2010 and 2016, 7011 individuals with T2D have been enrolled and assessed at baseline. Information collected include interview data (eg, body weight at age 20 years, physical activity and alcohol consumption), clinical examination data (eg, hip-waist ratio and resting heart rate) and biological samples (whole blood, DNA, plasma and urine) stored at -80°C and currently analysed for a range of biomarkers and genotypes.

FINDINGS TO DATE

Registry linkage has provided extensive supplemental continuous data on glycosylated haemoglobin A, lipids, albuminuria, blood pressure, smoking habits, body mass index, primary care contacts, hospital diagnoses and procedures, medication use, cancer and mortality. Cross-sectional associations between biomarkers, family history, anthropometric and lifestyle measures and presence of complications at baseline have been reported.

FUTURE PLANS

During 2016, a detailed follow-up questionnaire has been answered by 85% of initial participants, providing follow-up information on baseline variables and on presence of diabetic neuropathy. The DD2 cohort has now been followed for a total of 18 862 person-years, and nested intervention trials and follow-up studies are ongoing. In the future, the cohort will serve as a strong national and international resource for recruiting patients to nested case studies, clinical trials, postmarketing surveillance, large-scale genome studies and follow-up studies of T2D complications.

Early-onset type 2 diabetes: Age gradient in clinical and behavioural risk factors in 5115 persons with newly diagnosed type 2 diabetes-Results from the DD2 study

AIM

To examine the association between early onset of type 2 diabetes mellitus (DM) and clinical and behavioural risk factors for later complications of diabetes.

METHODS

We conducted a cross-sectional study of 5115 persons with incident type 2 DM enrolled during 2010-2015 in the Danish Centre for Strategic Research in Type 2 Diabetes-cohort. We compared risk factors at time of diagnosis among those diagnosed at ≤45 years (early onset) with diagnosis age 46 to 55, 56 to 65 (average onset = reference), 66 to 75, and >75 years (late onset). Prevalence ratios (PRs) were computed by using Poisson regression.

RESULTS

Poor glucose control, ie, HbA1c ≥ 75 mmol/mol (≥9.0%) in the early-, average-, and late-onset groups was observed in 12%, 7%, and 1%, respectively (PR 1.70 [95% confidence intervals (CI) 1.27, 2.28] and PR 0.17 [95% CI 0.06, 0.45]). A similar age gradient was observed for severe obesity (body mass index > 40 kg/m² : 19% vs. 8% vs. 2%; PR 2.41 [95% CI 1.83, 3.18] and 0.21 (95% CI 0.08, 0.57]), dyslipidemia (90% vs. 79% vs. 68%; PR 1.14 [95% CI 1.10, 1.19] and 0.86 [95% CI 0.79, 0.93]), and low-grade inflammation (C-reactive protein > 3.0 mg/L: 53% vs. 38% vs. 26%; PR 1.41 [95% CI 1.12, 1.78] and 0.68 [95% CI 0.42, 1.11]). Daily smoking was more frequent and meeting physical activity recommendations less likely in persons with early-onset type 2 DM.

CONCLUSIONS

We found a clear age gradient, with increasing prevalence of clinical and behavioural risk factors the younger the onset age of type 2 DM. Younger persons with early-onset type 2 DM need clinical awareness and support.

Transmission of Type 2 diabetes to sons and daughters: the D.E.S.I.R. cohort

AIMS

To document the family transmission of Type 2 diabetes to men and women.

METHOD

The French D.E.S.I.R. cohort followed men and women over 9 years, with 3-yearly testing for incident Type 2 diabetes. First- and/or second-degree family histories of diabetes were available for 2187 men and 2282 women. Age-adjusted hazard ratios were estimated for various family members and groupings of family members, as well as for a genetic diabetes risk score, based on 65 diabetes-associated loci.

RESULTS

Over 9 years, 136 men and 63 women had incident Type 2 diabetes. The hazard ratios for diabetes associated with having a first-degree family member with diabetes (parents, siblings, children) differed between men [1.21 (95% CI 0.80, 1.85)] and women [3.02 (95% CI 1.83, 4.99); P_interaction =0.006]. The genetic risk score was predictive of diabetes in both men and women, with similar hazard ratios 1.10 (95% CI 1.06, 1.15) and 1.08 (95% CI 1.02, 1.14) respectively, for each additional at-risk allele. In women, the risk associated with having a family member with diabetes persisted after adjusting for the genetic score.

CONCLUSION

Women with a family history of diabetes (paternal or maternal) were at risk of developing Type 2 diabetes and this risk was independent of a genetic score; in contrast, for men, there was no association. Diabetes screening and prevention may need to more specifically target women with diabetes in their family, but further studies are required as the number of people with diabetes in this study was small.

Familial History of Diabetes is Associated with Poor Glycaemic Control in Type 2 Diabetics: A Cross-sectional Study

To investigate the association of familial history (FH) of diabetes with the glycaemic control status of patients with type 2 diabetes (T2D), a cross-sectional study using stratified cluster sampling was conducted with 20,340 diabetic patients in Jiangsu, China. In total, 21.3% of the subjects reported a FH of diabetes. Patients with a FH of diabetes showed a higher risk of poor glycaemic control (59.7%) than those without a diabetic FH (49.8%), with an odds ratio (OR) of 1.366 (P < 0.001). Glycaemic control status did not significantly differ between the T2D patients with parental FH and those with sibling FH. Compared with patients with paternal FH, patients with maternal FH had a higher risk of poor glycaemic control (OR = 1.611, P = 0.013). Stratified analyses showed that a FH of diabetes was significantly associated with poor glycaemic control among T2D patients with a low education level (P < 0.05). In the <60 years old, overweight, and low level of physical activity groups, patients with a maternal history of diabetes showed a higher risk of poor glycaemic control than those without a FH (P < 0.05). In conclusion, FH of diabetes, especially a maternal history, had an independently adverse effect on the glycaemic control of T2D patients.