Diabetes and artificial intelligence (AI) beyond the closed loop: A review of the landscape, promise and challenges for AI-supported management and self-care for all diabetes types.

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.</p

Improving the fracture toughness properties of epoxy using graphene nanoplatelets at low filler content

This paper reports improvement in the fracture properties of epoxy nanocomposites using plasma functionalized graphene nanoplatelets (f-GNP) at low filler content. Various mechanical tests were performed on a series of f-GNP/epoxy at low nanofiller loading to assess the effect of the nanofiller on mechanical properties. Most importantly, a significant enhancement in fracture toughness is achieved without compromising the tensile and thermal properties of the nanocomposites. The fracture toughness of neat epoxy resin was increased by over 50% with the incorporation of 0.25 wt% f-GNP loading, obtaining a value of 245 J m−2, while the neat epoxy indicated a value of 162 J m−2. The glass transition temperature (Tg) and coefficient of thermal expansion (CTE) both showed a slight increase of 3% and 2%, respectively, both at 1 wt% f-GNP loading. These enhancements are competitive with current literature results on nanocomposites, but at significantly lower filler content. We therefore demonstrate that f-GNPs are capable of providing effective toughening of epoxy resins, while maintaining other tensile and thermal properties

Socioeconomic Deprivation and the Risk of Sight-Threatening Diabetic Retinopathy (STDR):A Population-Based Cohort Study in the U.K.

OBJECTIVETo evaluate the associations between socioeconomic deprivation and sight-threatening diabetic retinopathy (STDR) in individuals with type 1 (T1D) and type 2 diabetes (T2D).RESEARCH DESIGN AND METHODSData from 175,628 individuals with diabetes in the Health Improvement Network were used to assess the risk of STDR across Townsend Deprivation Index quantiles using Cox proportional hazard regression.RESULTSAmong individuals with T1D, the risk of STDR was three times higher (adjusted hazard ratio [aHR] 2.67, 95% CI 1.05–7.78) in the most deprived quintile compared with the least deprived quintile. In T2D, the most deprived quintile had a 28% higher risk (aHR 1.28; 95% CI 1.15–1.43) than the least deprived quintile.CONCLUSIONSIncreasing socioeconomic deprivation is associated with a higher risk of developing STDR in people with diabetes. This underscores persistent health disparities linked to poverty, even within a country offering free universal health care. Further research is needed to address health equity concerns in socioeconomically deprived regions

Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method

The prevalence of type 2 diabetes in North West London (NWL) is relatively high compared to other parts of the United Kingdom with outcomes suboptimal. This presents a need for more effective strategies to identify people living with type 2 diabetes who need additional support. An emerging subset of web-based interventions for diabetes self-management and population management has used artificial intelligence and machine learning models to stratify the risk of complications from diabetes and identify patients in need of immediate support. In this study, two prototype risk prediction tools on the MyWay Diabetes and MyWay Clinical platforms were evaluated with six clinicians and six people living with type 2 diabetes in NWL using the think aloud method. The results of the sessions with people living with type 2 diabetes showed that the concept of the tool was intuitive, however, more instruction on how to correctly use the risk prediction tool would be valuable. The feedback from the sessions with clinicians was that the data presented in the tool aligned with the key diabetes targets in NWL, and that this would be useful for identifying and inviting patients to the practice who are overdue for tests and at risk of complications. The findings of the evaluation have been used to support the development of the prototype risk predictions tools. This study demonstrates the value of conducting usability testing on web-based interventions designed to support the targeted management of type 2 diabetes in local communities

Risk of progression from pre‐diabetes to type 2 diabetes in a large UK adult cohort

Aims:  People with pre-diabetes are at high risk of progressing to type 2 diabetes. This progression is not well characterised by ethnicity, deprivation and age, which we describe in a large cohort of individuals with pre-diabetes. Methods:  A retrospective cohort study with The Health Improvement Network (THIN) database was conducted. Patients aged 18 years and over and diagnosed with pre-diabetes (HbA1c 42 mmol/mol (6.0%) to 48 mmol/mol (6.5% were included. Cox proportional hazards regression was used to calculate adjusted hazard rate ratios (aHR) for the risk of progression from pre-diabetes to type 2 diabetes for each of the exposure categories (ethnicity, deprivation (Townsend), age and body mass index (BMI)) separately. Results:  Of the baseline population with pre-diabetes (n=397853), South Asian (aHR 1.31; 95% CI 1.26-1.37) or Mixed-Race individuals (aHR 1.22; 95% CI 1.11-1.33) had an increased risk of progression to type 2 diabetes compared with those of white European ethnicity. Likewise, deprivation (aHR 1.17; 95% CI 1.14-1.20; most vs. least deprived) was associated with an increased risk of progression. Both younger (aHR 0.63; 95% CI 0.58-0.69; 18 to <30 years) and older individuals (aHR 0.85; 95% CI 0.84-0.87; ≥65 years) had a slower risk of progression from pre-diabetes to type 2 diabetes, than middle-aged (40 to <65 years) individuals. Conclusions:  South Asian or Mixed-Race individuals and people with social deprivation had an increased risk of progression from pre-diabetes to type 2 diabetes. Clinicians need to recognise the differing risk across their patient populations to implement appropriate prevention strategies