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.

OBJECTIVETo investigate the relationship among A1C, fasting plasma glucose (FPG), and 2-h postload plasma glucose in the Dutch general population and to evaluate the results of using A1C for screening and diagnosis of diabetes.RESEARCH DESIGN AND METHODSIn 2006–2007, 2,753 participants of the New Hoorn Study, aged 40–65 years, who were randomly selected from the population of Hoorn, the Netherlands, underwent an oral glucose tolerance test (OGTT). Glucose status (normal glucose metabolism [NGM], intermediate hyperglycemia, newly diagnosed diabetes, and known diabetes) was defined by the 2006 World Health Organization criteria. Spearman correlations were used to investigate the agreement between markers of hyperglycemia, and a receiver operating characteristic (ROC) curve was calculated to evaluate the use of A1C to identify newly diagnosed diabetes.RESULTSIn the total population, the correlations between fasting plasma glucose and A1C and between 2-h postload plasma glucose and A1C were 0.46 and 0.33, respectively. In patients with known diabetes, these correlations were 0.71 and 0.79. An A1C level of ≥5.8%, representing 12% of the population, had the highest combination of sensitivity (72%) and specificity (91%) for identifying newly diagnosed diabetes. This cutoff point would identify 72% of the patients with newly diagnosed diabetes and include 30% of the individuals with intermediate hyperglycemia.CONCLUSIONSIn patients with known diabetes, correlations between glucose and A1C are strong; however, moderate correlations were found in the general population. In addition, based on the diagnostic properties of A1C defined by ROC curve analysis, the advantage of A1C compared with OGTT for the diagnosis of diabetes is limited.

BackgroundThere is a well recognized association between depression and diabetes. However, there is little empirical data about the prevalence of depressive symptoms and anxiety among different groups of glucose metabolism in population based samples. The aim of this study was to determine whether the prevalence of increased levels of depression and anxiety is different between patients with type 2 diabetes and subjects with impaired glucose metabolism (IGM) and normal glucose metabolism (NGM).Methodology/Principal FindingsCross-sectional data from a population-based cohort study of 2667 residents, 1261 men and 1406 women aged 40–65 years from the Hoorn region, the Netherlands. Depressive symptoms and anxiety were measured using the Centre for Epidemiologic Studies Depression Scale (CES-D, score ≥16) and the Hospital Anxiety and Depression Scale – Anxiety Subscale (HADS-A, score ≥8), respectively. Glucose metabolism status was determined by oral glucose tolerance test. In the total study population the prevalence of depressive symptoms and anxiety for the NGM, IGM and type 2 diabetes were 12.5, 12.2 and 21.0% (P = 0.004) and 15.0, 15.3 and 19.9% (p = 0.216), respectively. In men, the prevalence of depressive symptoms was 7.7, 9.5 and 19.6% (p<0.001), and in women 16.4, 15.8 and 22.6 (p = 0.318), for participants with NGM, IGM and type 2 diabetes, respectively. Anxiety was not associated with glucose metabolism when stratified for sex. Intergroup differences (NGM vs. IGM and IGM vs. type 2 diabetes) revealed that higher prevalences of depressive symptoms are mainly manifested in participants with type 2 diabetes, and not in participants with IGM.ConclusionsDepressive symptoms, but not anxiety are associated with glucose metabolism. This association is mainly determined by a higher prevalence of depressive symptoms in participants with type 2 diabetes and not in participants with IGM.

Aims/hypothesisIn this study we aimed to replicate the previously reported association between the glycaemic response to metformin and the SNP rs11212617 at a locus that includes the ataxia telangiectasia mutated (ATM) gene in multiple additional populations.MethodsIncident users of metformin selected from the Diabetes Care System West-Friesland (DCS, n = 929) and the Rotterdam Study (n = 182) from the Netherlands, and the CARDS Trial (n = 254) from the UK were genotyped for rs11212617 and tested for an association with both HbA1c reduction and treatment success, defined as the ability to reach the treatment target of an HbA1c ≤7 % (53 mmol/mol). Finally, a meta-analysis including data from literature was performed.ResultsIn the DCS cohort, we observed an association between rs11212617 genotype and treatment success on metformin (OR 1.27, 95% CI 1.03, 1.58, p = 0.028); in the smaller Rotterdam Study cohort, a numerically similar but non-significant trend was observed (OR 1.45, 95% CI 0.87, 2.39, p = 0.15); while in the CARDS cohort there was no significant association. In meta-analyses of these three cohorts separately or combined with the previously published cohorts, rs11212617 genotype is associated with metformin treatment success (OR 1.24, 95% CI 1.04, 1.49, p = 0.016 and OR 1.25, 95% CI 1.33, 1.38, p = 7.8 × 10−6, respectively).Conclusions/interpretationA gene variant near ATM is significantly associated with metformin treatment response in type 2 diabetic patients from the Netherlands and the UK. This is the first robustly replicated common susceptibility locus found to be associated with metformin treatment response.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-012-2537-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.

Aims/hypothesis Variation in fasting plasma glucose (FPG) within the normal range is a known risk factor for the development of type 2 diabetes. Several reports have shown that genetic variation in the genes for glucokinase (GCK), glucokinase regulatory protein (GCKR), islet-specific glucose 6 phosphatase catalytic subunit-related protein (G6PC2) and melatonin receptor type 1B (MTNR1B) is associated with FPG. In this study we examined whether these loci also contribute to type 2 diabetes susceptibility. Methods A random selection from the Dutch New Hoorn Study was used for replication of the association with FGP (2,361 non-diabetic participants). For the genetic association study we extended the study sample with 2,628 participants with type 2 diabetes. Risk allele counting was used to calculate a four-gene risk allele score for each individual. Results Variants of the GCK, G6PC2 and MTNR1B genes but not GCKR were associated with FPG (all, p≤0.001; GCKR, p=0.23). Combining these four genes in a risk allele score resulted in an increase of 0.05 mmol/l (0.04-0.07) per additional risk allele (p=2×10 −13). Furthermore, participants with less than three or more than five risk alleles showed significantly different type 2 diabetes susceptibility compared with the most common group with four risk alleles respectively). The age at diagnosis was also significantly associated with the number of risk alleles (p=0.009). Conclusions A combined risk allele score for singlenucleotide polymorphisms in four known FPG loci is significantly associated with FPG and HbA 1c in a Dutch population-based sample of non-diabetic participants. Carriers of low or high numbers of risk alleles show significantly different risks for type 2 diabetes compared with the reference group.

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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.