Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers.

Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images. Our system, tested on the UK Biobank and DRSSW datasets, achieved AUROC scores of 0.83 (95% CI: 0.79-0.85) and 0.93 (95% CI: 0.9-0.95), respectively. These results not only highlight our system's advantage over established benchmarks but also underscore the predictive power of retinal biomarkers in assessing stroke risk and the unique effectiveness of each biomarker. Additionally, the correlation between retinal biomarkers and cardiovascular diseases broadens the potential application of our system, making it a versatile tool for predicting a wide range of cardiovascular conditions.

Responses to Basal Insulin Glargine (300 U/mL and 100 U/mL) with or Without Pre-prandial Insulin in Pre-treated Subphenotypes of Type 2 Diabetes: Insights from a Post Hoc Analysis.

INTRODUCTION: This study aimed to evaluate glycemic outcomes in subphenotypes of type 2 diabetes (T2D) with HbA1c > 7.0%, previously on basal insulin (pre-BI) alone (≥ 42 U/day) or on basal-bolus therapy (pre-BB), and who were switched to either basal insulin glargine 300 U/mL (IGlar-300) or 100 U/mL (IGlar-100), with or without pre-prandial insulin. METHODS: Participants from EDITION 2 (pre-BI, n = 785), and EDITION 1 (pre-BB, n = 792) trials were assigned retrospectively to subphenotypes of T2D: severe insulin deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity diabetes (MOD), and severe insulin resistant diabetes (SIRD). Key efficacy and safety parameters were analyzed at baseline, and after 26 weeks, for IGlar-300 and IGlar-100 pooled groups according to subphenotypes. Outcomes were also compared with insulin-naïve subphenotypes on oral antihyperglycemic drugs (OADs) from the EDITION 3 trial (pre-OAD, n = 858). RESULTS: Pre-BI and pre-BB treated subphenotypes with SIDD had a higher mean HbA1c (8.9% and 9.1%) at baseline compared to those of MARD (7.7% and 7.8%) and MOD (8.1% and 8.2%) and after 26 weeks remained above target HbA1c (7.7% and 8.0%) despite mean glargine doses of 0.7 to 1.0 U/kg/day and pre-prandial insulin use in the pre-BB SIDD subgroup. Pre-BB treated individuals with MARD and MOD achieved lower HbA1c levels (6.9% and 7.2%) than the pre-BI groups (7.3% and 7.5%) despite similar mean FPG levels (123-130 mg/dL). Only 19-22% of participants with SIDD achieved HbA1c < 7.0% compared to 33-51% with MARD and MOD, respectively. Pre-BI and pre-BB treated subphenotypes experienced more hypoglycemia than pre-OAD treated subphenotypes. CONCLUSION: Individuals with T2D assigned post hoc to the SIDD subphenotype achieved suboptimal glycemic control with glargine regimens including basal-bolus therapy, alerting clinicians to improve further diabetes treatment, particularly post-prandial glycemic control, in individuals with SIDD.

Treatment responses to basal insulin glargine 300 U/ml and glargine 100 U/ml in newly defined subphenotypes of type 2 diabetes: A post hoc analysis of the EDITION 3 randomized clinical trial.

INTRODUCTION: To compare responses to basal insulin glargine 300 U/ml (IGlar-300) and 100 U/ml (IGlar-100) in newly defined subphenotypes of type 2 diabetes. METHODS: Insulin-naive participants (n = 858) from the EDITION 3 trial were assigned to subphenotypes 'Mild Age-Related Diabetes (MARD)', 'Mild Obesity Diabetes (MOD)', 'Severe Insulin Resistant Diabetes (SIRD)' and 'Severe Insulin Deficient Diabetes (SIDD)'. Key variables were analysed at baseline and 26 weeks. RESULTS: Participants were comprised of MOD 56.1% (n = 481), SIDD 22.1% (n = 190), MARD 18.2% (n = 156) and SIRD 3.0% (n = 26). After 26 weeks a similar decrease in glycated haemoglobin (HbA1c) and fasting plasma glucose (FPG) of 16-19 mmol/mol and 1.4-1.7 mmol/L, respectively, occurred in MARD and MOD with both insulins. SIDD had the most elevated HbA1c and FPG (80-83 mmol/mol/11.1-11.4 mmol/L) and reduction in both HbA1c and FPG was greater with IGlar-100 than with IGlar-300 (-18 vs. -15 mmol/mol and -1.6 vs. -1.3 mmol/L, respectively; each p = .03). In SIDD, despite receiving the highest basal insulin doses, HbA1c decline (57-60 mmol/mol/7.3-7.6%) was suboptimal at week 26. In MOD and SIDD lower incidences with IGlar-300 were found for level 1 nocturnal hypoglycaemia [odds ratio (OR) 0.59, 95% confidence intervals (CI) 0.36-0.97; OR 0.49, 95% CI 0.24-0.99]. In addition, fewer level 2 hypoglycaemia episodes occurred at any time with IGlar-300 in SIDD (OR 0.31, 95% CI 0.13-0.77). CONCLUSION: Both insulins produce comparable outcomes in type 2 diabetes subphenotypes, but in SIDD, add-on treatment to basal insulin is required to achieve glycaemic targets.

Response to insulin glargine 100 U/mL treatment in newly-defined subgroups of type 2 diabetes: Post hoc pooled analysis of insulin-naïve participants from nine randomised clinical trials.

AIMS: To assess insulin glargine 100 U/mL (IGlar-100) treatment outcomes according to newly-defined subgroups of type 2 diabetes mellitus (T2DM). METHODS: Insulin-naïve T2DM participants (n = 2684) from nine randomised clinical trials initiating IGlar-100 were pooled and assigned to subgroups "Mild Age-Related Diabetes (MARD)", "Mild Obesity Diabetes (MOD)", "Severe Insulin Resistant Diabetes (SIRD)", and "Severe Insulin Deficient Diabetes (SIDD)", according to age at onset of diabetes, baseline HbA1c, BMI, and fasting C-peptide using sex-specific nearest centroid approach. HbA1c, FPG, hypoglycemia, insulin dose, and body weight were analysed at baseline and 24 weeks. RESULTS: Subgroup distribution was MARD 15.3 % (n = 411), MOD 39.8 % (n = 1067), SIRD 10.5 % (n = 283), SIDD 34.4 % (n = 923). From baseline HbA1c 8.0-9.6% adjusted least square mean reductions after 24 weeks were similar between subgroups (1.4-1.5 %). SIDD was less likely to achieve HbA1c < 7.0 % (OR: 0.40 [0.29, 0.55]) than MARD. While the final IGlar-100 dose (0.36 U/kg) in MARD was lower than in other subgroups (0.46-0.50 U/kg), it had the highest hypoglycemia risk. SIRD had lowest hypoglycemia risk and SIDD exhibited greatest body weight gain. CONCLUSIONS: IGlar-100 lowered hyperglycemia similarly in all T2DM subgroups, but level of glycemic control, insulin dose, and hypoglycemia risk differed between subgroups.

Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings.

Background: Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening. Methods: Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007-2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India. Findings: A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 - 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival. Interpretation: We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation. Funding: This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

Distribution and characteristics of newly-defined subgroups of type 2 diabetes in randomised clinical trials: Post hoc cluster assignment analysis of over 12,000 study participants.

AIMS: Newly-defined subgroups of type 2 diabetes mellitus (T2DM) have been reported from real-world cohorts but not in detail from randomised clinical trials (RCTs). METHODS: T2DM participants, uncontrolled on different pre-study therapies (n = 12.738; 82 % Caucasian; 44 % with diabetes duration > 10 years) from 14 RCTs, were assigned to new subgroups according to age at onset of diabetes, HbA1c, BMI, and fasting C-peptide using the nearest centroid approach. Subgroup distribution, characteristics and influencing factors were analysed. RESULTS: In both, pooled and single RCTs, "mild-obesity related diabetes" predominated (45 %) with mean BMI of 35 kg/m2. "Severe insulin-resistant diabetes" was found least often (4.6 %) and prevalence of "mild age-related diabetes" (23.9 %) was mainly influenced by age at onset of diabetes and age cut-offs. Subgroup characteristics were widely comparable to those from real-world cohorts, but all subgroups showed higher frequencies of diabetes-related complications which were associated with longer diabetes duration. A high proportion of "severe insulin-deficient diabetes" (25.4 %) was identified with poor pre-study glycaemic control. CONCLUSIONS: Classification of RCT participants into newly-defined diabetes subgroups revealed the existence of a heterogeneous population of T2DM. For future RCTs, subgroup-based randomisation of T2DM will better define the target population and relevance of the outcomes by avoiding clinical heterogeneity.

Insulin: evolution of insulin formulations and their application in clinical practice over 100 years.

The first preparation of insulin extracted from a pancreas and made suitable for use in humans after purification was achieved 100 years ago in Toronto, an epoch-making achievement, which has ultimately provided a life-giving treatment for millions of people worldwide. The earliest animal-derived formulations were short-acting and contained many impurities that caused adverse reactions, thereby limiting their therapeutic potential. However, since then, insulin production and purification improved with enhanced technologies, along with a full understanding of the insulin molecule structure. The availability of radio-immunoassays contributed to the unravelling of the physiology of glucose homeostasis, ultimately leading to the adoption of rational models of insulin replacement. The introduction of recombinant DNA technologies has since resulted in the era of both rapid- and long-acting human insulin analogues administered via the subcutaneous route which better mimic the physiology of insulin secretion, leading to the modern basal-bolus regimen. These advances, in combination with improved education and technologies for glucose monitoring, enable people with diabetes to better meet individual glycaemic goals with a lower risk of hypoglycaemia. While the prevalence of diabetes continues to rise globally, it is important to recognise the scientific endeavour that has led to insulin remaining the cornerstone of diabetes management, on the centenary of its first successful use in humans.

Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.

BACKGROUND: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. METHODS: We have conducted a case-control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. RESULTS: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. CONCLUSIONS: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

Impact of insecticide programs on pests, the predatory mite Phytoseiulus persimilis, and staked tomato profitability.

BACKGROUND: Various insecticides are available to manage diverse pest complexes in commercial vegetable production, but knowledge gaps exist regarding their overall performance in pest suppression, profitability, and compatibility with biological control. We conducted trials in staked tomatoes in western North Carolina in 2017-2018 to compare how different insecticide programs managed key pests and their interactions with Phytoseiulus persimilis Athias-Henriot, a predator of the twospotted spider mite (TSSM, Tetranychus urticae Koch). Treatments compared no insecticides to broad-spectrum ('hard') foliar applications, selective ('soft') foliar applications, and to chemigation of selective systemic insecticides. Treatment efficacy was based on pest control, net profitability, and Environmental Impact Quotient (EIQ) ratings comparing environmental and human health risks. RESULTS: All programs similarly suppressed aphids, while flower thrips and flea beetle populations were low and unaffected by program. Only the 'hard' foliar program suppressed TSSM (including P. persimilis), which quickly rebounded. This program inhibited biological control, and eventual TSSM suppression was likely caused by P. persimilis immigrating from nearby plots. All programs were generally equivalent in reducing fruit damage. Net profits were similar among insecticide programs, which were significantly greater than in untreated plots. Yield and profit trends were similar both years, but impacted by record flooding in 2018. CONCLUSION: Safer and more selective chemigation and 'soft' foliar programs were as effective and economical as the 'hard' foliar program, while also conserving biological control. These results support existing research demonstrating that improved insecticide use can be integrated with biological control for more profitable and environmentally sustainable vegetable production. © 2022 Society of Chemical Industry.

One-hundred year evolution of prandial insulin preparations: From animal pancreas extracts to rapid-acting analogs.

The first insulin preparation injected in humans in 1922 was short-acting, extracted from animal pancreas, contaminated by impurities. Ever since the insulin extracted from animal pancreas has been continuously purified, until an unlimited synthesis of regular human insulin (RHI) became possible in the '80s using the recombinant-DNA (rDNA) technique. The rDNA technique then led to the designer insulins (analogs) in the early '90s. Rapid-acting insulin analogs were developed to accelerate the slow subcutaneous (sc) absorption of RHI, thus lowering the 2-h post-prandial plasma glucose (PP-PG) and risk for late hypoglycemia as comparing with RHI. The first rapid-acting analog was lispro (in 1996), soon followed by aspart and glulisine. Rapid-acting analogs are more convenient than RHI: they improve early PP-PG, and 24-h PG and A1C as long as basal insulin is also optimized; they lower the risk of late PP hypoglycemia and they allow a shorter time-interval between injection and meal. Today rapid-acting analogs are the gold standard prandial insulins. Recently, even faster analogs have become available (faster aspart, ultra-rapid lispro) or are being studied (Biochaperone lispro), making additional gains in lowering PP-PG. Rapid-acting analogs are recommended in all those with type 1 and type 2 diabetes who need prandial insulin replacement.

The pathophysiology of glucose intolerance in newly diagnosed, untreated T2DM.

AIMS: The two predominant pathophysiological defects resulting in glucose intolerance are beta-cell dysfunction and insulin insensitivity. This study aimed to re-examine beta-cell function and insulin sensitivity across a continuum from normal glucose tolerance (NGT) to early type 2 diabetes (T2DM) employing highly specific insulin, C-peptide and intact proinsulin assays. MATERIALS AND METHODS: A total of 104 persons with NGT, 85 with impaired glucose tolerance (IGT) and 554 with newly diagnosed T2DM were investigated. Following an overnight fast, all underwent a 4-h standardised mixed meal tolerance test (MTT), and on a second day, a sub-group underwent a frequently sampled insulin-modified intravenous glucose tolerance test (FSIVGTT) over a 3-h period. The participants were stratified according to fasting glucose and BMI for analysis. RESULTS: The MTT revealed that increasing FPG was accompanied by progressively elevated and delayed postprandial glucose peaks. In parallel, following an initial compensatory increase in fasting and postprandial insulin responses there followed a progressive demise in overall beta-cell secretory capacity. FSIVGTT demonstrated a major reduction in the early insulin response to IV glucose in persons with IGT accompanied by a dramatic fall in insulin sensitivity. Beyond pre-diabetes, ever-increasing fasting and postprandial hyperglycaemia resulted predominantly from a progressively decreasing beta-cell secretory function. CONCLUSION: This study utilising improved assay technology re-affirms that beta-cell dysfunction is evident throughout the spectrum of glucose intolerance, whereas the predominant fall in insulin sensitivity occurs early in its evolution.

Evaluation of a New Neural Network Classifier for Diabetic Retinopathy.

BACKGROUND: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. METHODS: The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. RESULTS: The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. CONCLUSION: This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders' productivity and improve the final outcome accuracy of the screening process.

Incidence of diabetic retinopathy in newly diagnosed subjects with type 2 diabetes mellitus over 5 years: Contribution of Β-cell function.

AIMS: Identifying and modulating risk factors is essential to prevent visual impairment due to diabetic retinopathy (DR). This study examines incident DR with metabolic and hormonal factors in newly-diagnosed, treatment naïve, individuals with Type2 Diabetes Mellitus (T2DM), over a 5 year period from diagnosis. METHODS: 233 T2DM subjects underwent serial DR screening using digital photography and standardised Meal Tolerance Tests at diagnosis and after 1, 2 and 5 years. Subjects (179) with no DR throughout the 5-year study period were compared with those who developed DR (54). RESULTS: Of 233 subjects, 54(23.2%) developed DR by 5 years, background DR in 50(93%) and exudative maculopathy in 4(7%) individuals. Of these subjects, 12(22%) developed DR after 1 year, 15(28%) after 2 years and 27(50%) after 5 years. At baseline, those with DR at 5 years had higher HbA1c (p = 0.017), higher fasting plasma glucose (PG) (p = 0.031) and postprandial PG (p = 0.009). They were associated with reduced basal ß-cell secretory function (M0) (p = 0.025), lower (p = 0.000) postprandial ß-cell responsiveness (M1) and ß-cell function (HOMA-B) (p = 0.044). CONCLUSIONS: There is an independent association between glycaemic control and ß-cell dysfunction at the time of diagnosis of T2DM, with incident DR over a follow-up period of 5 years.

Insulin Centennial: Milestones influencing the development of insulin preparations since 1922.

During 1921 to 1922, a team effort by Banting, Macleod, Collip and Best isolated and purified insulin and demonstrated its life-giving properties, giving rise to the birth of insulin therapy. In the early years (1922-1950), priorities revolved around the manufacture of insulin to meet demand, improving purity to avoid allergic reactions, establishing insulin standards and increasing its duration of action to avoid multiple daily injections. Shortly after the emergence of insulin, Joslin and Allen advocated the need to achieve and maintain good glycaemic control to realize its full potential. Although this view was opposed by some during a dark period in the history of insulin, it was subsequently endorsed some 60 years later endorsed by the Diabetes Control and Complications Trial and United Kingdom Prospective Diabetes Study. Major scientific advances by the Nobel Laureates Sanger, Hodgkin, Yalow and Gilbert and also by Steiner have revolutionized the understanding of diabetes and facilitated major advances in insulin therapy. The more recent advent of recombinant technology over the last 40 years has provided the potential for unlimited source of insulin, and the ability to generate various insulin 'analogues', in an attempt to better replicate normal insulin secretory patterns. The emerging biosimilars now provide the opportunity to improve availability at a lower cost.

Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes.

Prediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007-2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999-1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82-0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

A retrospective epidemiological study of type 1 diabetes mellitus in wales, UK between 2008 and 2018.

INTRODUCTION: Studies of prevalence and the demographic profile of type 1 diabetes are challenging because of the relative rarity of the condition, however, these outcomes can be determined using routine healthcare data repositories. Understanding the epidemiology of type 1 diabetes allows for targeted interventions and care of this life-affecting condition. OBJECTIVES: To describe the prevalence, incidence and demographics of persons with type 1 diabetes diagnosed in Wales, UK, using the Secure Anonymised Information Linkage (SAIL) Databank. METHODS: Data derived from primary and secondary care throughout Wales available in the SAIL Databank were used to identify people with type 1 diabetes to determine the prevalence and incidence of type 1 diabetes over a 10 year period (2008-18) and describe the demographic and clinical characteristics of this population by age, socioeconomic deprivation and settlement type. The seasonal variation in incidence rates was also examined. RESULTS: The prevalence of type 1 diabetes in 2018 was 0.32% in the whole population, being greater in men compared to women (0.35% vs 0.28% respectively); highest in those aged 15-29 years (0.52%) and living in the most socioeconomically deprived areas (0.38%). The incidence of type 1 diabetes over 10 years was 14.0 cases/100,000 people/year for the whole population of Wales. It was highest in children aged 0-14 years (33.6 cases/100,000 people/year) and areas of high socioeconomic deprivation (16.8 cases/100,000 people/year) and least in those aged 45-60 years (6.5 cases/100,000 people/year) and in areas of low socioeconomic deprivation (11.63 cases/100,000 people/year). A seasonal trend in the diagnoses of type 1 diabetes was observed with higher incidence in winter months. CONCLUSION: This nation-wide retrospective epidemiological study using routine data revealed that the incidence of type 1 diabetes in Wales was greatest in those aged 0-14 years with a higher incidence and prevalence in the most deprived areas. These findings illustrate the need for health-related policies targeted at high deprivation areas to include type 1 diabetes in their remit.

Characteristics of repeat non-attenders at Diabetes Eye Screening Wales, a national community-based diabetes-related retinopathy screening service, during 2003-2018.

AIMS: To understand factors associated with repeat non-attendance at screening for diabetes-related retinopathy. METHODS: Retrospective observational study using anonymised data from Diabetic Eye Screening Wales for people with a full history of screening invitations and attendances was linked with primary and secondary care records held in the Secure Anonymised Information Linkage Databank. Repeat non-attendance was defined as no record of attendance during any 36-month period despite three cycles of annual screening invitations. The associations between repeat non-attendance and potential risk factors were examined using multivariable logistic regression analysis, stratified according to type 1 and type 2 diabetes. RESULTS: A total of 18% with type 1 diabetes (1146/6513) and 8% with type 2 diabetes (12,475/156,525) were repeat non-attenders. Participants attending their very first appointment were least likely to become repeat non-attenders [odds ratio (95% confidence interval)]: type 1 diabetes: 0.12 (0.09, 0.17) and type 2 diabetes: 0.08 (0.07, 0.09). For both types of diabetes, those of a younger age, living in areas of higher deprivation and subject to multiple house moves were at greater risk of becoming repeat non-attenders. CONCLUSION/INTERPRETATION: A more tailored approach is needed for the younger population, those living in areas of higher deprivation and/or undergoing multiple residential relocation and to ensure attendance at their initial appointment to minimise future repeat non-attendance.

Impact of age at type 2 diabetes mellitus diagnosis on mortality and vascular complications: systematic review and meta-analyses.

AIMS/HYPOTHESIS: Few studies examine the association between age at diagnosis and subsequent complications from type 2 diabetes. This paper aims to summarise the risk of mortality, macrovascular complications and microvascular complications associated with age at diagnosis of type 2 diabetes. METHODS: Data were sourced from MEDLINE and All EBM (Evidence Based Medicine) databases from inception to July 2018. Observational studies, investigating the effect of age at diabetes diagnosis on macrovascular and microvascular diabetes complications in adults with type 2 diabetes were selected according to pre-specified criteria. Two investigators independently extracted data and evaluated all studies. If data were not reported in a comparable format, data were obtained from authors, presented as minimally adjusted ORs (and 95% CIs) per 1 year increase in age at diabetes diagnosis, adjusted for current age for each outcome of interest. The study protocol was recorded with PROSPERO International Prospective Register of Systematic Reviews (CRD42016043593). RESULTS: Data from 26 observational studies comprising 1,325,493 individuals from 30 countries were included. Random-effects meta-analyses with inverse variance weighting were used to obtain the pooled ORs. Age at diabetes diagnosis was inversely associated with risk of all-cause mortality and macrovascular and microvascular disease (all p < 0.001). Each 1 year increase in age at diabetes diagnosis was associated with a 4%, 3% and 5% decreased risk of all-cause mortality, macrovascular disease and microvascular disease, respectively, adjusted for current age. The effects were consistent for the individual components of the composite outcomes (all p < 0.001). CONCLUSIONS/INTERPRETATION: Younger, rather than older, age at diabetes diagnosis was associated with higher risk of mortality and vascular disease. Early and sustained interventions to delay type 2 diabetes onset and improve blood glucose levels and cardiovascular risk profiles of those already diagnosed are essential to reduce morbidity and mortality. Graphical abstract.

Risk factors for having diabetic retinopathy at first screening in persons with type 1 diabetes diagnosed under 18 years of age.

OBJECTIVE: To determine the risk factors for having diabetic retinopathy (DR) in children and young people (CYP) with type 1 diabetes (T1DM) at first screening. METHODS: Records from the Diabetes Eye Screening Wales (DESW) service for people in Wales, UK, with T1DM diagnosed under age 18 years were combined with other electronic health record (EHR) data in the Secure Anonymised Information Linkage (SAIL) Databank. Data close to the screening date were collected, and risk factors derived from multivariate, multinomial logistic regression modelling. RESULTS: Data from 4172 persons, with median (lower quartile, upper quartile) age 16.3 (13.0, 22.3) years and duration of diabetes 6.6 (2.3, 12.3) years were analysed. 62.6% (n = 2613) had no DR, 26.7% (n = 1112) background DR, and 10.7% (n = 447) had referable DR (RDR). No RDR was observed under 19 years of age. Factors associated with an increased risk of DR were diabetes duration, elevated HbA1c, and diastolic blood pressure. People diagnosed with T1DM at 12 years or older had an additional risk for each year they had diabetes compared to those diagnosed before age 12 controlling for the diabetes duration (odds ratios 1.23 and 1.34, respectively). CONCLUSIONS: This study found that 37.4% of the study cohort had DR at first screening, the risk being greater the longer the duration of diabetes or higher the HbA1c and diastolic blood pressure. In addition, people diagnosed at 12 years of age or over were more likely to have DR with each additional year with diabetes.

Combining diabetic foot and retinopathy screening: A step in the right direction? - a feasibility study.

OBJECTIVES: Peripheral artery disease is a major cardiovascular disease affecting more than 200 million people globally and up to 4 times more frequent in the diabetic population. It can lead to lower extremity amputations or revascularisation and is associated with an increased risk of myocardial infarction, stroke and early mortality. This novel cross-sectional study aimed to explore the feasibility and acceptability of incorporating diabetic foot screening at routine diabetic retinopathy screening appointments. METHODS: Participants underwent foot screening during the interval between pupil dilatation and retinal photography as part of the eye screening procedure. Lower limb arterial assessment included ankle brachial index, pulse volume waveform and protective light touch sensation. RESULTS: Of 364 participants invited, 88% (n = 321) met the inclusion criteria. About 26.4% (n = 86) had asymptomatic peripheral artery disease and 3% (n = 10) had peripheral sensory neuropathy. Binary logistical regression analysis identified age (p < 0.005), existing coronary heart disease (p < 0.005) and gender (p = 0.03) as predictors of peripheral artery disease. CONCLUSION: Incorporating foot examination during eye screening appointments is feasible and was well received by participants and staff alike. Undiagnosed early peripheral artery disease was evident in a third of the study population emphasising the benefit of introducing foot surveillance into eye screening appointments for the early identification of lower limb arterial disease and peripheral sensory neuropathy.