Only a few studies have investigated the metabolic consequences of social jetlag. Therefore, we examined the association of social jetlag with the metabolic syndrome and type 2 diabetes mellitus in a population-based cohort. We used cross-sectional data from the New Hoorn Study cohort (n = 1585, 47% men, age 60.8 ± 6 years). Social jetlag was calculated as the difference in midpoint sleep (in hours) between weekdays and weekend days. Poisson and linear regression models were used to study the associations, and age was regarded as a possible effect modifier. We adjusted for sex, employment status, education, smoking, physical activity, sleep duration, and body mass index. In the total population, we only observed an association between social jetlag and the metabolic syndrome, with prevalence ratios adjusted for sex, employment status, and educational levels of 1.64 (95% CI 1.1-2.4), for participants with >2 h social jetlag, compared with participants with <1 h social jetlag. However, we observed an interaction effect of median age (<61 years). In older participants (≥61 years), no significant associations were observed between social jetlag status, the metabolic syndrome, and diabetes or prediabetes. In the younger group (<61 years), the adjusted prevalence ratios were 1.29 (95% CI 0.9-1.9) and 2.13 (95% CI 1.3-3.4) for the metabolic syndrome and 1.39 (95% CI 1.1-1.9) and 1.75 (95% CI 1.2-2.5) for diabetes/prediabetes, for participants with 1-2 h and >2 h social jetlag, compared with participants with <1 h social jetlag. In conclusion, in our population-based cohort, social jetlag was associated with a 2-fold increased risk of the metabolic syndrome and diabetes/prediabetes, especially in younger (<61 years) participants.
Background: Dairy consumption has been postulated to reduce the risk of obesity and metabolic disturbances. Objective: The aim of this study was to evaluate the associations of dairy consumption with body weight and other components of the metabolic syndrome. Design: We used cross-sectional data for 2064 men and women aged 50 -75 y who participated in the Hoorn Study. The metabolic syndrome was defined according to the National Cholesterol Education Program Expert Panel. Dairy consumption was assessed by using a semiquantitative food-frequency questionnaire. Results: The median consumption of total dairy products was 4.1 servings/d. After adjustment for potential confounders (ie, dietary factors, physical activity, smoking, income, educational level, and antihypertensive medication), total dairy consumption was significantly associated with lower diastolic blood pressure (␤ Ȁ SE: Ҁ0.31 Ȁ 0.12 mm Hg/serving) and higher fasting glucose concentrations (0.04 Ȁ 0.02 mmol/L per serving), but not with body weight or other metabolic variables (ie, lipids, postload glucose, or insulin). When different dairy products were distinguished, borderline significant (P 0.10) inverse associations were observed for dairy desserts, milk, and yogurt with systolic (Ҁ1.26 Ȁ 0.58, Ҁ0.57 Ȁ 0.34, and Ҁ1.28 Ȁ 0.74 mm Hg/serving, respectively) and diastolic (Ҁ0.58 Ȁ 0.31, Ҁ0.57 Ȁ 0.18, and Ҁ0.35 Ȁ 0.40 mm Hg/serving, respectively) blood pressure, whereas cheese consumption was positively associated with body mass index (0.15 Ȁ 0.08/serving). Conclusion:In an elderly Dutch population, higher dairy consumption was not associated with lower weight or more favorable levels of components of the metabolic syndrome, except for a modest association with lower blood pressure.Am J Clin Nutr 2007;85: 989 -95.
Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose–raising alleles were associated with a measure of first-phase insulin secretion at P < 0.05 and provided new evidence, or the strongest evidence yet, that insulin secretion, intrinsic to the islet cells, is a key mechanism underlying the associations at the HNF1A, IGF2BP2, KCNQ1, HNF1B, VPS13C/C2CD4A, FAF1, PTPRD, AP3S2, KCNK16, MAEA, LPP, WFS1, and TMPRSS6 loci. The fasting glucose–raising allele near PDX1, a known key insulin transcription factor, was strongly associated with lower first-phase insulin secretion but has no evidence for an effect on type 2 diabetes risk. The diabetes risk allele at TCF7L2 was associated with a stronger effect on peak insulin response than on C-peptide–based insulin secretion rate, suggesting a possible additional role in hepatic insulin clearance or insulin processing. In summary, our study provides further insight into the mechanisms by which common genetic variation influences type 2 diabetes risk and glycemic traits.
PurposeTo increase the efficiency of retinal image grading, algorithms for automated grading have been developed, such as the IDx‐DR 2.0 device. We aimed to determine the ability of this device, incorporated in clinical work flow, to detect retinopathy in persons with type 2 diabetes.MethodsRetinal images of persons treated by the Hoorn Diabetes Care System (DCS) were graded by the IDx‐DR device and independently by three retinal specialists using the International Clinical Diabetic Retinopathy severity scale (ICDR) and EURODIAB criteria. Agreement between specialists was calculated. Results of the IDx‐DR device and experts were compared using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), distinguishing between referable diabetic retinopathy (RDR) and vision‐threatening retinopathy (VTDR). Area under the receiver operating characteristic curve (AUC) was calculated.ResultsOf the included 1415 persons, 898 (63.5%) had images of sufficient quality according to the experts and the IDx‐DR device. Referable diabetic retinopathy (RDR) was diagnosed in 22 persons (2.4%) using EURODIAB and 73 persons (8.1%) using ICDR classification. Specific intergrader agreement ranged from 40% to 61%. Sensitivity, specificity, PPV and NPV of IDx‐DR to detect RDR were 91% (95% CI: 0.69–0.98), 84% (95% CI: 0.81–0.86), 12% (95% CI: 0.08–0.18) and 100% (95% CI: 0.99–1.00; EURODIAB) and 68% (95% CI: 0.56–0.79), 86% (95% CI: 0.84–0.88), 30% (95% CI: 0.24–0.38) and 97% (95% CI: 0.95–0.98; ICDR). The AUC was 0.94 (95% CI: 0.88–1.00; EURODIAB) and 0.87 (95% CI: 0.83–0.92; ICDR). For detection of VTDR, sensitivity was lower and specificity was higher compared to RDR. AUC's were comparable.ConclusionAutomated grading using the IDx‐DR device for RDR detection is a valid method and can be used in primary care, decreasing the demand on ophthalmologists.
PurposePeople with type 2 diabetes (T2D) have a doubled morbidity and mortality risk compared with persons with normal glucose tolerance. Despite treatment, clinical targets for cardiovascular risk factors are not achieved. The Hoorn Diabetes Care System cohort (DCS) is a prospective cohort representing a comprehensive dataset on the natural course of T2D, with repeated clinical measures and outcomes. In this paper, we describe the design of the DCS cohort.ParticipantsThe DCS consists of persons with T2D in primary care from the West-Friesland region of the Netherlands. Enrolment in the cohort started in 1998 and this prospective dynamic cohort currently holds 12 673 persons with T2D.Findings to dateClinical measures are collected annually, with a high internal validity due to the centrally organised standardised examinations. Microvascular complications are assessed by measuring kidney function, and screening feet and eyes. Information on cardiovascular disease is obtained by 1) self-report, 2) electrocardiography and 3) electronic patient records. In subgroups of the cohort, biobanking and additional measurements were performed to obtain information on, for example, lifestyle, depression and genomics. Finally, the DCS cohort is linked to national cancer and all-cause mortality registers. A selection of published findings from the DCS includes identification of subgroups with distinct development of haemoglobin A1c, blood pressure and retinopathy, and their predictors; validation of a prediction model for personalised retinopathy screening; the assessment of the role of genetics in development and treatment of T2D, providing options for personalised medicine.Future plansWe will continue with the inclusion of persons with newly diagnosed T2D, follow-up of persons in the cohort and linkage to morbidity and mortality registries. Currently, we are involved in (inter)national projects on, among others, biomarkers and prediction models for T2D and complications and we are interested in collaborations with external researchers.Trial registrationISRCTN26257579
Four subgroups with distinct HbA1c trajectories were identified. More than 90 % reached and maintained good glycemic control (subgroup one and two). Patients within the two subgroups that showed a more unfavorable course of glycemic control were younger, had higher HbA1c levels and a longer diabetes duration at baseline.
OBJECTIVETo determine the diagnostic accuracy in a real-world primary care setting of a deep learning-enhanced device for automated detection of diabetic retinopathy (DR). RESEARCH DESIGN AND METHODSRetinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning-enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more [mom]DR) was compared with a reference standard. This reference standard consisted of grading according to the International Clinical Classification of DR by the Rotterdam Study reading center. We determined the diagnostic accuracy of the hybrid deep learningenhanced device (IDx-DR-EU-2.1) against the reference standard. RESULTSA total of 1,616 people with type 2 diabetes were imaged. The hybrid deep learning-enhanced device's sensitivity/specificity against the reference standard was, respectively, for vtDR 100% (95% CI 77.1-100)/97.8% (95% CI 96.8-98.5) and for mtmDR 79.4% (95% CI 66.5-87.9)/93.8% (95% CI 92.1-94.9). CONCLUSIONSThe hybrid deep learning-enhanced device had high diagnostic accuracy for the detection of both vtDR (although the number of vtDR cases was low) and mtmDR in a primary care setting against an independent reading center. This allows its' safe use in a primary care setting.With the growing prevalence of diabetes, the prevalence of diabetic retinopathy (DR) is rising as well. Screening for DR has proven to be effective in the prevention of visual loss and blindness from DR (1). National health authorities (2) and most professional organizations (3) recommend regular DR screening programs, which are usually integrated within regular diabetes care (4). Automated medical diagnosis has achieved parity with or even superiority to clinical experts' diagnosis for an increasing number of clinical tasks, including detection of DR (5-7), and can help to improve health care efficiency, affordability, and accessibility of DR screening. Moreover, automated diagnosis reduced the diagnostic variability that was common in expert review of medical images (8).
A large reduction in retinopathy screening was achieved using the model in this population of patients with a very low incidence of retinopathy. Considering the number of potentially missed cases of STR, there is room for improvement in the model. Use of the model for personalised screening may eventually help to reduce healthcare use and costs of diabetes care.
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