Conceptualizing type 2 diabetes and its management

Trend and pattern of using herbal medicines among people who are aware of their diabetes mellitus: results from National STEPs Surveys in 2005 to 2011 in Iran

Objectives

Use of traditional herbal medicines especially by those affected by chronic diseases, such as diabetes mellitus is important. The objective of this study was to assess trend and Pattern of using traditional herbal medicines by diabetic population in Iran.

Methods

The results of this study are extracted from the National Stepwise approach to chronic disease risk factor surveillance (STEPs), conducted in 2005- 2016 in Iran. A total of 3095 Iranian diabetic individuals, aged more than 25 years in 2005, 1470 diabetics in 2006, 1633 diabetics in 2007, 1652 diabetics in 2008, 1563 diabetics in 2009, and 1005 diabetics in 2011 were included in this study. We couldn't use data in 2016 because in 2016, traditional herbal use has not been questioned. First, a descriptive analysis of the study variables and prevalence of herbal use for each year, was performed. Thereafter, to determine which variables were independent predictors of adherence to herbal use, we performed multivariate logistic regression.

Results

Using traditional herbal medicines among Iranian adult population was increased from 11.1 (9.98-12.20) in 2005 to 23.5 (20.87-26.25) in 2011. The results show increase in herbal medicine use in all age groups, both urban and rural areas, and both male and female over time. Also, we found that using traditional herbal medicines was more common among female compared with male (24.2% versus 21.8%), older than middle-aged people (24.4% versus 15.9%), and people living in urban areas compared with rural area (24.13% versus 20.95%) in 2011.

Conclusion

The use of traditional herbs for treatment, alone or in combination with other therapies by the patients who have diabetes has increased over the time. Considering the high level of using traditional herbal medicines in treatment of diabetes and because of the possible herb-drug interactions, policymakers need to take appropriate interventions to control herb store and increase people's knowledge about the herbal usage.

Evaluating Effectiveness of Outpatient Monitoring in Type 2 Diabetes: The One-Year Experience in an Italian Group of Primary Care

Nowadays, chronic disease management is the primary challenge of the healthcare system. From 2015, in the Veneto region (Italy), patients with a diagnosis of type 2 diabetes mellitus (T2DM) have been included in the diagnostic-therapeutic pathway (PDTA) program, and their clinical condition has been continuously monitored. The aim of this retrospective study is to determine the effectiveness of PDTA intervention, alone or in combination with a specialized one, in subjects with diagnosis of T2DM. Clinical and behavioral characteristics were collected at baseline and after 1 year of follow-up. Two subgroups were considered: subjects enrolled in PDTA only and subjects enrolled in both the PDTA program and in the care plan proposed by the specialized medical center (CAD group). Longitudinal analysis showed a relevant positive effect of time on diastolic blood pressure, while CAD enrollment appears to be related to higher levels of glycated hemoglobin. When included together in the same model, interaction between time and CAD covariates results in completely nonsignificant effects. As long-term management of chronic disorders, such as T2DM, is often difficult due to disease characteristics and problems in healthcare organization, monitoring programs, such as PDTA, and specialized care programs, such as CAD, do not show a clinically relevant effect in the first year of follow-up. Therefore, they should be analyzed over a longer period. However, they should also carefully consider the need for adequate tools for data collection and sharing, in addition to the context of application, patient expectations and the need for a long-term educational program.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Enabling Guidelines for the Adoption of eHealth Solutions: Scoping Review

BACKGROUND

Globally, public health care is under increasing pressure, an economic burden currently amplified by the COVID-19 outbreak. With the recognition that universal health coverage improves the health of a population and reduces health inequalities, universal health coverage has been acknowledged as a priority goal. To meet the global needs in a population with increased chronic illness and longer life expectancy, the health care system is in dire need of new, emerging technologies. eHealth solutions as a method of delivery may have an impact on quality of care and health care costs. As such, it is important to study methods previously used to avoid suboptimal implementation and promote general guidelines to further develop eHealth solutions.

OBJECTIVE

This study aims to explore and thematically categorize a selected representation of early phase studies on eHealth technologies, focusing on papers that are under development or undergoing testing. Further, we want to assess enablers and barriers in terms of usability, scaling, and data management of eHealth implementation. The aim of this study to explore early development phase and feasibility studies was an intentional effort to provide applicable guidelines for evaluation at different stages of implementation.

METHODS

A structured search was performed in PubMed, MEDLINE, and Cochrane to identify and provide insight in current eHealth technology and methodology under development and gain insight in the future potential of eHealth technologies.

RESULTS

In total, 27 articles were included in this review. The clinical studies were categorized thematically by illness comparing 4 technology types deemed relevant: apps/web-based technology, sensor technology, virtual reality, and television. All eHealth assessment and implementation studies were categorized by their focus point: usability, scaling, or data management. Studies assessing the effect of eHealth were divided into feasibility studies, qualitative studies, and heuristic assessments. Studies focusing on usability (16/27) mainly addressed user involvement and learning curve in the adoption of eHealth, while the majority of scaling studies (6/27) focused on strategic and organizational aspects of upscaling eHealth solutions. Studies focusing on data management (5/27) addressed data processing and data sensitivity in adoption and diffusion of eHealth. Efficient processing of data in a secure manner, as well as user involvement and feedback, both throughout small studies and during upscaling, were the important enablers considered for successful implementation of eHealth.

CONCLUSIONS

eHealth interventions have considerable potential to improve lifestyle changes and adherence to treatment recommendations. To promote efficient implementation and scaling, user involvement to promote user-friendliness, secure and adaptable data management, and strategical considerations needs to be addressed early in the development process. eHealth should be assessed during its development into health services. The wide variation in interventions and methodology makes comparison of the results challenging and calls for standardization of methods.

Reconceptualising Person-Centered Service Models as Social Ecology Networks in Supporting Integrated Care

Efforts to address problems such as mental health, poverty, social exclusion, and chronic disease have often proven resistant to traditional policies or interventions. In this paper, we take up the challenge and present a pioneering new method of analysis in drawing on theoretical and methodological extensions of two prominent approaches, namely, social network analysis and developmental social ecology. Considered in combination, these two seemingly disparate approaches frame a powerful new way of thinking about person-centred care, as well as offer a methodologically more rigorous set of analytical tools. The conceptual model developed from this combination offers to bridge the apparent disconnect between service integration levels and patient needs in such a way as to direct optimal effort to interventions at the individual level and to provide a new innovative approach to the delivery of integrated care.

Which obesity phenotypes predict poor health-related quality of life in adult men and women? Tehran Lipid and Glucose Study

Purpose

This study aimed to explore the association between different obesity phenotypes and health-related quality of life (HRQoL) among Tehranian men and women.

Methods

The participants of this study were 2880 healthy adults (aged>19 years) who participated in Tehran Lipid and Glucose Study (TLGS). To obtain socio-demographic and HRQoL information, participants were interviewed by trained interviewers and were stratified by body mass index categories and metabolic status. Dysmetabolic status was defined as having either metabolic syndrome or diabetes according to the Joint Interim Statement definition and American Diabetes Association. Poor HRQoL was defined as the first quartile of HRQoL scores and logistic regression analysis was used to compare sex-specific odds ratios.

Results

Mean age of participants was 47.7±15.6 and 47.8±14.2 years in men and women respectively. The most and the least common obesity phenotypes were overweight-normal metabolic status and normal weight-dysmetabolic status, respectively. Only mean scores for physical HRQoL were significantly different among obesity phenotypes in both men and women (p<0.05). In addition, after adjusting for age, marital status, level of education, job status and physical activity, the odds of reporting poor physical HRQoL was significantly higher in men (OR: 1.960, 95% CI: 1.037–3.704; p<0.05) and women (OR: 2.887, 95% CI: 1.674–4.977; p<0.001) with obese-dysmetabolic status, compared to their counterparts with normal weight-normal metabolic status. However, except for overweight-normal metabolic women, who were less likely to report poor mental HRQoL (OR: 0.638, 95% CI: 0.415–0.981; p<0.05), none of the phenotypes were associated with poor mental HRQoL in either gender.

Conclusions

Compared to those with normal weight normal metabolic status, only obese dysmetabolic individuals were more likely to report poor physical HRQoL in both genders.

Three-Dimensional Bioreactor Technologies for the Cocultivation of Human Mesenchymal Stem/Stromal Cells and Beta Cells

Diabetes is a prominent health problem caused by the failure of pancreatic beta cells. One therapeutic approach is the transplantation of functional beta cells, but it is difficult to generate sufficient beta cells in vitro and to ensure these cells remain viable at the transplantation site. Beta cells suffer from hypoxia, undergo apoptosis, or are attacked by the host immune system. Human mesenchymal stem/stromal cells (hMSCs) can improve the functionality and survival of beta cells in vivo and in vitro due to direct cell contact or the secretion of trophic factors. Current cocultivation concepts with beta cells are simple and cannot exploit the favorable properties of hMSCs. Beta cells need a three-dimensional (3D) environment to function correctly, and the cocultivation setup is therefore more complex. This review discusses 3D cultivation forms (aggregates, capsules, and carriers) for hMSCs and beta cells and strategies for large-scale cultivation. We have determined process parameters that must be balanced and considered for the cocultivation of hMSCs and beta cells, and we present several bioreactor setups that are suitable for such an innovative cocultivation approach. Bioprocess engineering of the cocultivation processes is necessary to achieve successful beta cell therapy.

Real architecture For 3D Tissue (RAFT™) culture system improves viability and maintains insulin and glucagon production of mouse pancreatic islet cells

There is an unmet medical need for the improvement of pancreatic islet maintenance in culture. Due to restricted donor availability it is essential to ameliorate islet viability and graft engraftment. The aim of this study was to compare the standard tissue culture techniques with the advanced Real Architecture For 3D Tissue (RAFT™) culture system in terms of viability and hormone production. Here, we first report that islets embedded in RAFT™ collagen type I advanced tissue culture system maintain their tissue integrity better than in monolayer and suspension cultures. The Calcein violet assay and Annexin V/propidium-iodide staining show higher cell viability in the RAFT™ culture system. Quantitative real-time PCR data showed that RAFT™ increases insulin expression after 18 days in culture compared to traditional methods. Enhanced insulin and glucagon production was further verified by immunofluorescent staining in a time-course manner. These results indicate that RAFT™ tissue culture platform can be a promising tool to maintain pancreatic islet spheroid integrity and culture islets for downstream high throughput pharmacological studies ex vivo.