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Cytokines of the IL-1 family are important modulators of obesity-induced inflammation and the development of systemic insulin resistance. Here we show that IL-1 family member IL-37, recently characterized as an anti-inflammatory cytokine, ameliorates obesity-induced inflammation and insulin resistance. Mice transgenic for human IL-37 (IL-37tg) exhibit reduced numbers of adipose tissue macrophages, increased circulating levels of adiponectin and preserved glucose tolerance and insulin sensitivity after 16 weeks of HFD. In vitro treatment of adipocytes with recombinant IL-37 reduces adipogenesis and activates AMPK signalling. In humans, elevated steady-state IL-37 adipose tissue mRNA levels are positively correlated with insulin sensitivity and a lower inflammatory status of the adipose tissue. These findings reveal IL-37 as an important anti-inflammatory modulator during obesity-induced inflammation and insulin resistance in both mice and humans, and suggest that IL-37 is a potential target for the treatment of obesity-induced insulin resistance and type 2 diabetes. Inflammation is a hallmark of obesity and driver of various associated pathologies. Here the authors show that mice overexpressing the anti-inflammatory cytokine IL-37 are protected from the metabolic consequences of a high-fat diet, and that plasma levels of IL-37 correlate with insulin sensitivity in humans.

Nature Communications 5: Article number: 4711 (2014); Published: 3 September 2014; Updated: 12 January 2015. The financial support for this Article was not fully acknowledged. The Acknowledgements should have read: R.S. was supported by a Ruby Grant of the Dutch Diabetes Research Foundation. C.A.D. was supported by the National Institutes of Health (grant number AI15614).

Obesity is associated with chronic, low-grade inflammation arising from adipose tissue. Various members of the IL-1 family have been shown to modulate obesity-induced inflammation and thereby altering development of insulin resistance.

Researchers are increasingly using routine clinical data for care evaluations and feedback to patients and clinicians. The quality of these evaluations depends on the quality and completeness of the input data. We assessed the performance of an electronic health record (EHR)-based data mining algorithm, using the example of the smoking status in a cardiovascular population. As a reference standard, we used the questionnaire from the Utrecht Cardiovascular Cohort (UCC). To assess diagnostic accuracy, we calculated sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We analyzed 1,661 patients included in the UCC to January 18, 2019. Of those, 14% (n = 238) had missing information on smoking status in the UCC questionnaire. Data mining provided information on smoking status in 99% of the 1,661 participants. Diagnostic accuracy for current smoking was sensitivity 88%, specificity 92%, NPV 98%, and PPV 63%. From false positives, 85% reported they had quit smoking at the time of the UCC. Data mining showed great potential in retrieving information on smoking (a near complete yield). Its diagnostic performance is good for negative smoking statuses. The implications of misclassification with data mining are dependent on the application of the data.

Purpose We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare. Methods We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS. Results In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides ‘live’ information on CVRM within the environment of the EHR. The CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation. Conclusion Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth.