Diabetes mellitus-Progress and opportunities in the evolving epidemic.

Diabetes, a complex multisystem metabolic disorder characterized by hyperglycemia, leads to complications that reduce quality of life and increase mortality. Diabetes pathophysiology includes dysfunction of beta cells, adipose tissue, skeletal muscle, and liver. Type 1 diabetes (T1D) results from immune-mediated beta cell destruction. The more prevalent type 2 diabetes (T2D) is a heterogeneous disorder characterized by varying degrees of beta cell dysfunction in concert with insulin resistance. The strong association between obesity and T2D involves pathways regulated by the central nervous system governing food intake and energy expenditure, integrating inputs from peripheral organs and the environment. The risk of developing diabetes or its complications represents interactions between genetic susceptibility and environmental factors, including the availability of nutritious food and other social determinants of health. This perspective reviews recent advances in understanding the pathophysiology and treatment of diabetes and its complications, which could alter the course of this prevalent disorder.

Hyperglycemic Crises in Adults With Diabetes: A Consensus Report.

The American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD), Joint British Diabetes Societies for Inpatient Care (JBDS), American Association of Clinical Endocrinology (AACE), and Diabetes Technology Society (DTS) convened a panel of internists and diabetologists to update the ADA consensus statement on hyperglycemic crises in adults with diabetes, published in 2001 and last updated in 2009. The objective of this consensus report is to provide up-to-date knowledge about the epidemiology, pathophysiology, clinical presentation, and recommendations for the diagnosis, treatment, and prevention of diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) in adults. A systematic examination of publications since 2009 informed new recommendations. The target audience is the full spectrum of diabetes health care professionals and individuals with diabetes.

Hyperglycaemic crises in adults with diabetes: a consensus report.

The American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD), Joint British Diabetes Societies for Inpatient Care (JBDS), American Association of Clinical Endocrinology (AACE) and Diabetes Technology Society (DTS) convened a panel of internists and diabetologists to update the ADA consensus statement on hyperglycaemic crises in adults with diabetes, published in 2001 and last updated in 2009. The objective of this consensus report is to provide up-to-date knowledge about the epidemiology, pathophysiology, clinical presentation, and recommendations for the diagnosis, treatment and prevention of diabetic ketoacidosis (DKA) and hyperglycaemic hyperosmolar state (HHS) in adults. A systematic examination of publications since 2009 informed new recommendations. The target audience is the full spectrum of diabetes healthcare professionals and individuals with diabetes.

Optimized Protocol for Generating Functional Pancreatic Insulin-secreting Cells from Human Pluripotent Stem Cells.

Human pluripotent stem cells (hPSCs) can differentiate into any kind of cell, making them an excellent alternative source of human pancreatic ß-cells. hPSCs can either be embryonic stem cells (hESCs) derived from the blastocyst or induced pluripotent cells (hiPSCs) generated directly from somatic cells using a reprogramming process. Here a video-based protocol is presented to outline the optimal culture and passage conditions for hPSCs, prior to their differentiation and subsequent generation of insulin-producing pancreatic cells. This methodology follows the six-stage process for ß-cell directed differentiation, wherein hPSCs differentiate into definitive endoderm (DE), primitive gut tube, posterior foregut fate, pancreatic progenitors, pancreatic endocrine progenitors, and ultimately pancreatic ß-cells. It is noteworthy that this differentiation methodology takes a period of 27 days to generate human pancreatic ß-cells. The potential of insulin secretion was evaluated through two experiments, which included immunostaining and glucose-stimulated insulin secretion.

Diabetes and Multiple Long-term Conditions: A Review of Our Current Global Health Challenge.

Use of effective treatments and management programs is leading to longer survival of people with diabetes. This, in combination with obesity, is thus contributing to a rise in people living with more than one condition, known as multiple long-term conditions (MLTC or multimorbidity). MLTC is defined as the presence of two or more long-term conditions, with possible combinations of physical, infectious, or mental health conditions, where no one condition is considered as the index. These include a range of conditions such as cardiovascular diseases, cancer, chronic kidney disease, arthritis, depression, dementia, and severe mental health illnesses. MLTC has major implications for the individual such as poor quality of life, worse health outcomes, fragmented care, polypharmacy, poor treatment adherence, mortality, and a significant impact on health care services. MLTC is a challenge, where interventions for prevention and management are lacking a robust evidence base. The key research directions for diabetes and MLTC from a global perspective include system delivery and care coordination, lifestyle interventions and therapeutic interventions.

Precision subclassification of type 2 diabetes: a systematic review.

BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion.

BACKGROUND: Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. METHODS: Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. RESULTS: There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. CONCLUSIONS: We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

Some diabetes types, called monogenic diabetes, are caused by changes in a single gene. It is important to know who has this kind of diabetes because treatment can differ from that of other types of diabetes. Some treatments also work better than others for specific types, and some people can for example change from insulin injections to tablets. In addition, relatives can be offered a test to see if they are at risk. Genetic testing is needed to diagnose monogenic diabetes but is expensive, so it's not possible to test every person with diabetes for it. We evaluated published research on who should be tested and what test to use. Based on this, we provide recommendations for doctors and health care providers on how to implement genetic testing for monogenic diabetes.

The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries.

Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.

Current insights and emerging trends in early-onset type 2 diabetes.

Type 2 diabetes diagnosed in childhood or early adulthood is termed early-onset type 2 diabetes. Cases of early-onset type 2 diabetes are increasing rapidly globally, alongside rising obesity. Compared with a diagnosis later in life, an earlier-onset diagnosis carries an unexplained excess risk of microvascular complications, adverse cardiovascular outcomes, and earlier death. Women with early-onset type 2 diabetes also have a higher risk of adverse pregnancy outcomes. The high burden of complications renders individuals with early-onset type 2 diabetes at future risk of multimorbidity and interventions to reverse these concerning trends should be a priority. Within the early-onset cohort, disease pathophysiology and interventions have been better studied in paediatric-onset (<19 years) type 2 diabetes compared to adults; however, young adults aged 19-39 years (a larger number proportionally) are not well characterised and are also invisible in the current evidence base supporting management, which is derived from trials in later-onset type 2 diabetes. Young adults with type 2 diabetes face challenges in self-management that older individuals are less likely to experience (being in education or of working age, higher diabetes distress, and possible obesity-related stigma and diabetes-related stigma). There is a major research gap as to the optimal strategies to deploy in managing type 2 diabetes in adolescents and young adults, given that current models of care appear to not work as well in this age group. In the face of manifold risk factors (obesity, female sex, social deprivation, non-White European ethnicity, and genetic risk factors) prevention strategies with tailored lifestyle interventions, where needed, are likely to have greater success, but more evidence is needed. In this Review, we draw on evidence from both adolescents and young adults to provide a contemporary update on the current insights and emerging trends in early-onset type 2 diabetes.

Newly detected diabetes during the COVID-19 pandemic: What have we learnt?

The SARS-CoV-2 pandemic has had an unprecedented effect on global health, mortality and healthcare provision. Diabetes has emerged as a key disease entity over the pandemic period, influencing outcomes from COVID-19 but also a tantalising hypothesis that the virus itself may be inducing diabetes. An uptick in diabetes cases over the pandemic has been noted for both type 1 diabetes (in children) and type 2 diabetes but understanding how this increase in incidence relates to the pandemic is challenging. It remains unclear whether indirect effects of the pandemic on behaviour, lifestyle and health have contributed to the increase; whether the virus itself has somehow mediated new-onset diabetes or whether other factors such as stress hyperglycaemic of steroid treatment during COVID-19 infection have played a roll. Within the myriad possibilities are some real challenges in interpreting epidemiological data, assigning diabetes type and understanding what in vitro data are telling us. In this review article we address the issue of newly-diagnosed diabetes during the pandemic, reviewing both epidemiological and basic science data and bringing together both strands of this emerging story.

Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK.

BACKGROUND: A rise in the incidence of some autoimmune disorders has been described. However, contemporary estimates of the overall incidence of autoimmune diseases and trends over time are scarce and inconsistent. We aimed to investigate the incidence and prevalence of 19 of the most common autoimmune diseases in the UK, assess trends over time, and by sex, age, socioeconomic status, season, and region, and we examine rates of co-occurrence among autoimmune diseases. METHODS: In this UK population-based study, we used linked primary and secondary electronic health records from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex and ethnicity. Eligible participants were men and women (no age restriction) with acceptable records, approved for Hospital Episodes Statistics and Office of National Statistics linkage, and registered with their general practice for at least 12 months during the study period. We calculated age and sex standardised incidence and prevalence of 19 autoimmune disorders from 2000 to 2019 and used negative binomial regression models to investigate temporal trends and variation by age, sex, socioeconomic status, season of onset, and geographical region in England. To characterise co-occurrence of autoimmune diseases, we calculated incidence rate ratios (IRRs), comparing incidence rates of comorbid autoimmune disease among individuals with a first (index) autoimmune disease with incidence rates in the general population, using negative binomial regression models, adjusted for age and sex. FINDINGS: Among the 22 009 375 individuals included in the study, 978 872 had a new diagnosis of at least one autoimmune disease between Jan 1, 2000, and June 30, 2019 (mean age 54·0 years [SD 21·4]). 625 879 (63·9%) of these diagnosed individuals were female and 352 993 (36·1%) were male. Over the study period, age and sex standardised incidence rates of any autoimmune diseases increased (IRR 2017-19 vs 2000-02 1·04 [95% CI 1·00-1·09]). The largest increases were seen in coeliac disease (2·19 [2·05-2·35]), Sjogren's syndrome (2·09 [1·84-2·37]), and Graves' disease (2·07 [1·92-2·22]); pernicious anaemia (0·79 [0·72-0·86]) and Hashimoto's thyroiditis (0·81 [0·75-0·86]) significantly decreased in incidence. Together, the 19 autoimmune disorders examined affected 10·2% of the population over the study period (1 912 200 [13·1%] women and 668 264 [7·4%] men). A socioeconomic gradient was evident across several diseases, including pernicious anaemia (most vs least deprived area IRR 1·72 [1·64-1·81]), rheumatoid arthritis (1·52 [1·45-1·59]), Graves' disease (1·36 [1·30-1·43]), and systemic lupus erythematosus (1·35 [1·25-1·46]). Seasonal variations were observed for childhood-onset type 1 diabetes (more commonly diagnosed in winter) and vitiligo (more commonly diagnosed in summer), and regional variations were observed for a range of conditions. Autoimmune disorders were commonly associated with each other, particularly Sjögren's syndrome, systemic lupus erythematosus, and systemic sclerosis. Individuals with childhood-onset type 1 diabetes also had significantly higher rates of Addison's disease (IRR 26·5 [95% CI 17·3-40·7]), coeliac disease (28·4 [25·2-32·0]), and thyroid disease (Hashimoto's thyroiditis 13·3 [11·8-14·9] and Graves' disease 6·7 [5·1-8·5]), and multiple sclerosis had a particularly low rate of co-occurrence with other autoimmune diseases. INTERPRETATION: Autoimmune diseases affect approximately one in ten individuals, and their burden continues to increase over time at varying rates across individual diseases. The socioeconomic, seasonal, and regional disparities observed among several autoimmune disorders in our study suggest environmental factors in disease pathogenesis. The inter-relations between autoimmune diseases are commensurate with shared pathogenetic mechanisms or predisposing factors, particularly among connective tissue diseases and among endocrine diseases. FUNDING: Research Foundation Flanders.

A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes.

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.

Systematic review of precision subclassification of type 2 diabetes.

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

How Can Point-of-Care Technologies Support In-Hospital Diabetes Care?

People with diabetes admitted to hospital are at risk of diabetes related complications including hypoglycaemia and diabetic ketoacidosis. Point-of-care (POC) tests undertaken at the patient bedside, for glucose, ketones, and other analytes, are a key component of monitoring people with diabetes, to ensure safety. POC tests implemented with a quality framework are critical to ensuring accuracy and veracity of results and preventing erroneous clinical decision making. POC results can be used for self-management of glucose levels in those well-enough and/or by healthcare professionals to identify unsafe levels. Connectivity of POC results to electronic health records further offers the possibility of utilising these results proactively to identify patients 'at risk' in real-time and for audit purposes. In this article, the key considerations when implementing POC tests for diabetes in-patient management are reviewed and potential to drive improvements using networked glucose and ketone measurements are discussed. In summary, new advances in POC technology should allow people with diabetes and the teams looking after them whilst in hospital to integrate to provide safe and effective care.

Variation in the Current Use of Technology to Support Diabetes Management in UK Hospitals: Results of a Survey of Health Care Professionals.

BACKGROUND: There has been a significant increase in the use of wearable diabetes technologies in the outpatient setting over recent years, but this has not consistently translated into inpatient use. METHODS: An online survey was undertaken to understand the current use of technology to support inpatient diabetes care in the United Kingdom. RESULTS: Responses were received from 42 different organizations representing 104 hospitals across the United Kingdom. Significant variation was found between organizations in the use of technology to support safe, effective inpatient diabetes care. Benefits of the use of technology were reported, and areas of good practice identified. CONCLUSION: Technology supports good inpatient diabetes care, but there is currently variation in its use. Guidance has been developed which should drive improvements in the use of technology and hence improvements in the safety and effectiveness of inpatient diabetes care. Key recommendations include implementation of this guidance (especially for continuous glucose monitoring), ensuring specialist support is available for the use of wearable diabetes technology in hospital, optimizing information sharing across the health care system, and making full use of data from networked glucose and ketone meters.

Continuous Glucose Monitoring Within Hospital: A Scoping Review and Summary of Guidelines From the Joint British Diabetes Societies for Inpatient Care.

Increasing numbers of people, particularly with type 1 diabetes (T1D), are using wearable technologies. That is, continuous subcutaneous insulin infusion (CSII) pumps, continuous glucose monitoring (CGM) systems, and hybrid closed-loop systems, which combine both these elements. Given over a quarter of all people admitted to hospital have diabetes, there is a need for clinical guidelines for when people using them are admitted to hospital. The Joint British Diabetes Societies for Inpatient Care (JBDS-IP) provide a scoping review and summary of guidelines on the use of diabetes technology in people with diabetes admitted to hospital.JBDS-IP advocates enabling people who can self-manage and use their own diabetes technology to continue doing so as they would do out of hospital. Whilst people with diabetes are recommended to achieve a target of 70% time within range (3.9-10.0 mmol/L [70-180 mg/dL]), this can be very difficult to achieve whilst unwell. We therefore recommend targeting hypoglycemia prevention as a priority, keeping time below 3.9 mmol/L (70 mg/dL) at < 1%, being aware of looming hypoglycemia if glucose is between 4.0 and 5.9 mmol/L (72-106 mg/dL), and consider intervening, particularly if there is a downward CGM trend arrow.Health care organizations need clear local policies and guidance to support individuals using diabetes technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting. The current set of guidelines is divided into two parts. Part 1, which follows below, outlines the guidance for use of CGM in hospital. The second part outlines guidance for use of CSII and hybrid closed-loop in hospital.

Addressing health inequalities in diabetes through research: Recommendations from Diabetes UK's 2022 health inequalities in diabetes workshop.

AIMS: To develop a position statement which identifies research priorities to address health inequalities in diabetes and provides recommendations to researchers and research funders on how best to conduct research in these areas. METHODS: A two-day research workshop was conducted bringing together research experts in diabetes, research experts in health inequalities, healthcare professionals and people living with diabetes. RESULTS: The following key areas were identified as needing increased focus: How can we improve patient and public involvement and engagement to make diabetes research more inclusive of and relevant to diverse communities? How can we improve research design so that the people who could benefit most are represented? How can we use theories from implementation science to facilitate the uptake of research findings into routine practice to reach the populations with highest need? How can we collate and evaluate local innovation projects and disseminate best practice around tackling health inequalities in diabetes? How can we best collect and use data to address health inequalities in diabetes, including the harnessing of real-world and routinely collected data? How could research funders allocate funds to best address health inequalities in diabetes? How do we ensure the research community is representative of the general population? CONCLUSIONS: This position statement outlines recommendations to address the urgent need to tackle health inequalities in diabetes through research and calls on the diabetes research community to act upon these recommendations to ensure future research works to eliminate unfair and avoidable disparities in health.

Insulin Pumps and Hybrid Close Loop Systems Within Hospital: A Scoping Review and Practical Guidance From the Joint British Diabetes Societies for Inpatient Care.

This article is the second of a two-part series providing a scoping review and summary of the Joint British Diabetes Societies for Inpatient Care (JBDS-IP) guidelines on the use of diabetes technology in people with diabetes admitted to hospital. The first part reviewed the use of continuous glucose monitoring (CGM) in hospital. In this article, we focus on the use of continuous subcutaneous insulin infusion (CSII; insulin pumps) and hybrid closed-loop systems in hospital. JBDS-IP advocates enabling people who can self-manage and are willing and capable of using CSII to continue doing so as they would do out of hospital. CSII should be discontinued if the individual is critically ill or hemodynamically unstable. For individuals on hybrid closed-loop systems, the system should be discontinued from auto-mode, and may be used individually (as CGM only or CSII only, if criteria are met). Continuing in closed-loop mode may only be done so under specialist guidance from the Diabetes Team, where the diabetes teams are comfortable and knowledgeable about the specific devices used. Health care organizations need to have clear local policies and guidance to support individuals using these wearable technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting.