The Maastricht Study is an extensive phenotyping study that focuses on the etiology of type 2 diabetes (T2DM), its classic complications, and its emerging comorbidities. The study uses state-of-the-art imaging techniques and extensive biobanking to determine health status in a population-based cohort of 10,000 individuals that is enriched with T2DM individuals. Enrollment started in November 2010 and is anticipated to last 5-7 years. The Maastricht Study is expected to become one of the most extensive phenotyping studies in both the general population and T2DM participants world-wide. The Maastricht study will specifically focus on possible mechanisms that may explain why T2DM accelerates the development and progression of classic complications, such as cardiovascular disease, retinopathy, neuropathy and nephropathy and of emerging comorbidities, such as cognitive decline, depression, and gastrointestinal, musculoskeletal and respiratory diseases. In addition, it will also examine the association of these variables with quality of life and use of health care resources. This paper describes the rationale, overall study design, recruitment strategy and methods of basic measurements, and gives an overview of all measurements that are performed within The Maastricht Study.

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Aims/hypothesisThe study investigated cross-sectional associations of total amount and patterns of sedentary behaviour with glucose metabolism status and the metabolic syndrome.MethodsWe included 2,497 participants (mean age 60.0 ± 8.1 years, 52% men) from The Maastricht Study who were asked to wear an activPAL accelerometer 24 h/day for 8 consecutive days. We calculated the daily amount of sedentary time, daily number of sedentary breaks and prolonged sedentary bouts (≥30 min), and the average duration of the sedentary bouts. To determine glucose metabolism status, participants underwent an oral glucose tolerance test. Associations of sedentary behaviour variables with glucose metabolism status and the metabolic syndrome were examined using multinomial logistic regression analyses.ResultsOverall, 1,395 (55.9%) participants had normal glucose metabolism, 388 (15.5%) had impaired glucose metabolism and 714 (28.6%) had type 2 diabetes. The odds ratio per additional hour of sedentary time was 1.22 (95% CI 1.13, 1.32) for type 2 diabetes and 1.39 (1.27, 1.53) for the metabolic syndrome. No significant or only weak associations were seen for the number of sedentary breaks, number of prolonged sedentary bouts or average bout duration with either glucose metabolism status or the metabolic syndrome.Conclusions/interpretationAn extra hour of sedentary time was associated with a 22% increased odds for type 2 diabetes and a 39% increased odds for the metabolic syndrome. The pattern in which sedentary time was accumulated was weakly associated with the presence of the metabolic syndrome. These results suggest that sedentary behaviour may play a significant role in the development and prevention of type 2 diabetes, although longitudinal studies are needed to confirm our findings.

Prediabetes, T2DM, and measures of hyperglycemia are independently associated with impaired microvascular function in the retina and skin. These findings support the concept that microvascular dysfunction precedes and thus may contribute to T2DM-associated cardiovascular disease and other complications, which may in part have a microvascular origin such as impaired cognition and heart failure.

Diabetes is a strong risk factor for vascular disease. There is compelling evidence that the relative risk of vascular disease associated with diabetes is substantially higher in women than men. The mechanisms that explain the sex difference have not been identified. However, this excess risk could be due to certain underlying biological differences between women and men. In addition to other cardiometabolic pathways, sex differences in body anthropometry and patterns of storage of adipose tissue may be of particular importance in explaining the sex differences in the relative risk of diabetes-associated vascular diseases. Besides biological factors, differences in the uptake and provision of health care could also play a role in women's greater excess risk of diabetic vascular complications. In this review, we will discuss the current knowledge regarding sex differences in both biological factors, with a specific focus on sex differences adipose tissue, and in health care provided for the prevention, management, and treatment of diabetes and its vascular complications. While progress has been made towards understanding the underlying mechanisms of women's higher relative risk of diabetic vascular complications, many uncertainties remain. Future research to understanding these mechanisms could contribute to more awareness of the sex-specific risk factors and could eventually lead to more personalized diabetes care. This will ensure that women are not affected by diabetes to a greater extent and will help to diminish the burden in both women and men.

Structural brain abnormalities are key risk factors for brain diseases, such as dementia, stroke, and depression, in type 2 diabetes. It is unknown whether structural brain abnormalities already occur in prediabetes. Therefore, we investigated whether both prediabetes and type 2 diabetes are associated with lacunar infarcts (LIs), white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and brain atrophy. RESEARCH DESIGN and METHODS We used data from 2,228 participants (1,373 with normal glucose metabolism [NGM], 347 with prediabetes, and 508 with type 2 diabetes (oversampled); mean age 59.2 6 8.2 years; 48.3% women) of the Maastricht Study, a population-based cohort study. Diabetes status was determined with an oral glucose tolerance test. Brain imaging was performed with 3 Tesla MRI. Results were analyzed with multivariable logistic and linear regression analyses. RESULTS Prediabetes and type 2 diabetes were associated with the presence of LIs (odds ratio 1.61 [95% CI 0.98-2.63] and 1.67 [1.04-2.68], respectively; P trend = 0.027), larger WMH (b 0.07 log10-transformed mL [log-mL] [95% CI 0.00-0.15] and 0.21 log-mL [0.14-0.28], respectively; P trend <0.001), and smaller white matter volumes (b 24.0 mL [27.3 to 20.6] and 27.2 mL [210.4 to 24.0], respectively; P trend <0.001) compared with NGM. Prediabetes was not associated with gray matter volumes or the presence of CMBs. CONCLUSIONS Prediabetes is associated with structural brain abnormalities, with further deterioration in type 2 diabetes. These results indicate that, in middle-aged populations, structural brain abnormalities already occur in prediabetes, which may suggest that the treatment of early dysglycemia may contribute to the prevention of brain diseases. Structural brain abnormalities are thought to be an important pathway through which type 2 diabetes causes brain diseases (1). Indeed, there is extensive evidence that type 2 diabetes is associated with an increased risk of brain diseases, such as stroke, dementia, and depression (1-9), and of structural brain abnormalities on MRI, such as lacunar infarcts (LIs), white matter hyperintensities (WMHs), and brain atrophy (10), which in turn are associated with an increased risk of stroke, dementia, and depression (11-13).

Inflammation and endothelial dysfunction are both associated with depressive disorder, independent of lifestyle factors. Our results might suggest that inflammation and endothelial dysfunction are involved in depression.

As accelerometers are commonly used for 24-h measurements of daily activity, methods for separating waking from sleeping time are necessary for correct estimations of total daily activity levels accumulated during the waking period. Therefore, an algorithm to determine wake and bed times in 24-h accelerometry data was developed and the agreement of this algorithm with self-report was examined. One hundred seventy-seven participants (aged 40-75 years) of The Maastricht Study who completed a diary and who wore the activPAL3™ 24 h/day, on average 6 consecutive days were included. Intraclass correlation coefficient (ICC) was calculated and the Bland-Altman method was used to examine associations between the self-reported and algorithm-calculated waking hours. Mean self-reported waking hours was 15.8 h/day, which was significantly correlated with the algorithm-calculated waking hours (15.8 h/day, ICC = 0.79, P = < 0.001). The Bland-Altman plot indicated good agreement in waking hours as the mean difference was 0.02 h (95% limits of agreement (LoA) = −1.1 to 1.2 h). The median of the absolute difference was 15.6 min (Q1-Q3 = 7.6-33.2 min), and 71% of absolute differences was less than 30 min. The newly developed automated algorithm to determine wake and bed times was highly associated with self-reported times, and can therefore be used to identify waking time in 24-h accelerometry data in large-scale epidemiological studies.