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.

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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

Aims/hypothesisThe DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT.MethodsPrediabetic participants (target sample size 2,200–2,700) and patients with newly diagnosed type 2 diabetes (target sample size ~1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires.Conclusions/interpretationDIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-014-3216-x) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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.