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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Biomedical data governance strategies should ensure that data are collected, stored, and used ethically and lawfully. However, research participants' preferences for how data should be governed is least studied. The DIRECT project collected substantial amounts of health and genetic information from patients at risk of, and with Type II Diabetes. We conducted a survey to understand participants' future data governance preferences. Results will inform the post-project data governance strategy. Methods A survey was distributed in Denmark, Sweden, The Netherlands, and UK. Results In total 855 surveys were returned. Ninety-seven percent were supportive of sharing data post-project, and 90% were happy to share data with universities, and 56% with commercial companies. The top 3 priorities for data sharing were: highly secure database, DIRECT researchers to monitor data used by other researchers, and researchers cannot identify participants. Respondents frequently suggested that a post-project Data Access Committee should involve: a DIRECT researcher, Diabetes clinician, patient representative, and a DIRECT participant. Conclusion Preferences of how data should be governed, and what data could be shared and with whom varied between countries. Researchers are considered as key custodians of 2 participant data. Engaging participants aids in designing governance to support their choices.

The purpose of this study was to explore and compare different countries in what motivated research participants' decisions whether to share their de-identified data. We investigated European DIRECT (Diabetes Research on Patient Stratification) research project participants' desire for control over sharing different types of their de-identified data, and with who data could be shared in the future after the project ends. A crosssectional survey was disseminated among DIRECT project participants. The results found that there was a significant association between country and attitudes towards advancing research, protecting privacy, and beliefs about risks and benefits to sharing data. When given the choice to have control, some participants (less than 50% overall) indicated that having control over what data is shared and with whom was important;and control over what data types are shared was less important than respondents deciding who data are shared with. Danish respondents indicated higher odds of desire to control data types shared, and Dutch respondents showed higher odds of desire to control who data will be shared with. Overall, what research participants expect in terms of control over data sharing needs to be considered and aligned with sharing for future research and re-use of data. Our findings show that even with de-identified data, respondents prioritise privacy above all else. This study argues to move research participants from passive participation in biomedical research to considering their opinions about data sharing and control of de-identified biomedical data.

Genome-Wide Association Study (GWAS) Higher Blood pressure Arthritides Neuropsychiatric conditions Malignancies Lower Anaemias Lipidaemias Ischaemic heart disease Genetically higher central obesity Highlights Variants in HFE and TMPRSS6 are associated with higher liver iron. There is genetic evidence that higher central obesity causes higher liver iron. Liver iron variants are not organ specific and associate with multiple diseases.

Patient-Reported Outcome Measures (PROMs) are important tools to assess outcomes relevant to patients, with Health-Related Quality Of Life (HRQOL) as an important construct to be measured. Many different HRQOL PROMs are used in the type 2 diabetes field, however a complete overview of these PROMs is currently lacking. We therefore aimed to systematically describe and classify the content of all PROMs that have specifically been developed or validated to measure (aspects of) HRQOL in people with type 2 diabetes. A literature search was performed in PubMed and EMBASE until 31 December 2021. Studies on the development or validation of a PROM measuring HRQOL, or aspects of HRQOL, in people with type 2 diabetes were included. Title and abstract and full-text screening were conducted by two independent researchers and data extraction was performed independently by one of the researchers. Data were extracted on language in which the PROM was developed, target population, construct(s) being measured, names of (sub)scales and number of items per (sub)scale. In addition, all PROMs and subscales were classified according to specific aspects of HRQOL based on the Wilson & Cleary model (symptom status, functional status, general health perceptions) to aid researchers in PROM selection. In total 220 studies were identified that developed or validated PROMs that measure (aspects of) HRQOL in people with type 2 diabetes. Of the 116 unique HRQOL PROMs, 91 (of the subscales) measured symptom status, 60 measured functional status and 26 measured general health perceptions. In addition, 16 of the PROMs (subscales) measured global quality of life. 61 of the 116 PROMs (subscales) also include characteristics of the individual (e.g. aspects of personality, coping) or environment (e.g. social or financial support) and patient-reported experience measures (PREMs, e.g. measure of a patient's perception of their personal experience of the healthcare they have received, e.g. treatment satisfaction), which are not part of the HRQOL construct. Only 9 of the 116 PROMs measure all aspects of HRQOL based on the Wilson & Cleary model. Finally, 8 of the 116 PROMs stating to measure HRQOL, measured no HRQOL construct. In conclusion, a large number of PROMs are available for people with type 2 diabetes, which intend to measure (aspects of) HRQOL. These PROMs measure a large variety of (sub)constructs, which are not all HRQOL constructs, with a small amount of PROMs not measuring HRQOL at all. There is a need for consensus on which aspects of HRQOL should be measured in people with type 2 diabetes and which PROMs to use in research and daily practice. PROSPERO: CRD42017071012. COMET database: http://www.comet-initiative.org/studies/details/956.

Objective. With depression being present in approximately 20% of people with type 2 diabetes mellitus (T2DM), we expect equally frequent prescription of antidepressants, anxiolytics, and hypnotics. Nevertheless, prescription data in people with T2DM is missing and the effect of depression on glycaemic control is contradictory. The aim of this study was to assess the prevalence of antidepressants, anxiolytics, and/or hypnotics use in a large, managed, primary care system cohort of people with T2DM and to determine the sociodemographic characteristics, comorbidities, T2DM medication, and metabolic control associated with its use. Method. The prevalence of antidepressants, anxiolytics, and/or hypnotics use in the years 2007–2012 was assessed in the Hoorn Diabetes Care System Cohort from the Netherlands. Results. From the 7016 people with T2DM, 500 people (7.1%) used antidepressants only, 456 people (6.5%) used anxiolytics and/or hypnotics only, and 254 people (3.6%) used a combination. Conclusion. We conclude that in our managed, primary care system 17% of all people with T2DM used antidepressants, anxiolytics, and/or hypnotics. Users of antidepressants, anxiolytics, and/or hypnotics were more often female, non-Caucasian, lower educated, and more often treated with insulin.

The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Full titleProcesses underlying glycemic deterioration in type 2 diabetes: An IMI DIRECT study