Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme

Background/AimsNational guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.MethodsWe consider 21346 and 247233 people with T1DM and T2DM respectively each contributing on average 4.8 and 4.4 screening intervals of which 1339 and 4675 intervals concluded with a referable screening episode. Information extracted from fundus images using DL were used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed.ResultsThe DL predictor increased the AUC in comparison to a predictor using current DR grades from 0.809 to 0.87 for T1DM and from 0.825 to 0.87 for T2DM. Expected sojourn time – the time from becoming referable to being rescreened - was found to be 3.4 (T1DM) and 2.7 (T2DM) weeks less for a DL-derived policy compared to the current recall policy.ConclusionsWe showed that, compared to using the current retinopathy grade, DL of fundus images significantly improves the prediction of incident referable retinopathy before the next screening episode. This can impact screening recall interval policy positively, for example, by reducing the expected time with referable disease for a fixed workload - which we show as an exemplar. Additionally, it could be used to optimise workload for a fixed sojourn time

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland:A nationwide cohort study 2016–2022

Aims: We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016–2022, stratifying by sociodemographics. Methods: Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016–2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv. Results: Rates for those under 20 with type 1 diabetes reached their minimum at the 2020–2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20–49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08–1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27–2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08–2.62) in type 2 diabetes. Conclusions: Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.</p

The effect of dapagliflozin on glycaemic control and other cardiovascular disease risk factors in type 2 diabetes mellitus:a real-world observational study

Aims/hypothesis: Dapagliflozin, a sodium–glucose cotransporter 2 (SGLT2) inhibitor, is indicated for improving glycaemic control in type 2 diabetes mellitus. Whether its effects on HbA1c and other variables, including safety outcomes, in clinical trials are obtained in real-world practice needs to be established. Methods: We used data from the comprehensive national diabetes register, the Scottish Care Information-Diabetes (SCI-Diabetes) collaboration database, available from 2004 to mid-2016. Data within this database were linked to mortality data from the General Registrar, available from the Information Services Division (ISD) of the National Health Service in Scotland. We calculated crude within-person differences between pre- and post-drug-initiation values of HbA1c, BMI, body weight, systolic blood pressure (SBP) and eGFR. We used mixed-effects regression models to adjust for within-person time trajectories in these measures. For completeness, we evaluated safety outcomes, cardiovascular disease events, lower-limb amputation and diabetic ketoacidosis, focusing on cumulative exposure effects, using Cox proportional hazard models, though power to detect such effects was limited. Results: Among 8566 people exposed to dapagliflozin over a median of 210 days the crude within-person change in HbA1c was −10.41 mmol/mol (−0.95%) after 3 months’ exposure. The crude change after 12 months was −12.99 mmol/mol (−1.19%) but considering the expected rise over time in HbA1c gave a dapagliflozin-exposure-effect estimate of −15.14 mmol/mol (95% CI −15.87, −14.41) (−1.39% [95% CI −1.45, −1.32]) at 12 months that was maintained thereafter. A drop in SBP of −4.32 mmHg (95% CI −4.84, −3.79) on exposure within the first 3 months was also maintained thereafter. Reductions in BMI and body weight stabilised by 6 months at −0.82 kg/m2 (95% CI −0.87, −0.77) and −2.20 kg (95% CI −2.34, −2.06) and were maintained thereafter. eGFR declined initially by −1.81 ml min−1 [1.73 m]−2 (95% CI −2.10, −1.52) at 3 months but varied thereafter. There were no significant effects of cumulative drug exposure on safety outcomes. Conclusions/interpretation: Dapagliflozin exposure was associated with reductions in HbA1c, SBP, body weight and BMI that were at least as large as in clinical trials. Dapagliflozin also prevented the expected rise in HbA1c and SBP over the period of study

Prescribing paradigm shift? Applying the 2019 European Society of Cardiology-led guidelines on ‘diabetes, pre-diabetes, and cardiovascular disease’ to assess eligibility for sodium-glucose co-transporter-2 inhibitors or glucagon-like peptide-1 receptor agonists as first-line monotherapy (or add-on to metformin monotherapy) in type 2 diabetes in Scotland

Objective: In 2019, the European Society of Cardiology led and released new guidelines for diabetes cardiovascular risk management, reflecting recent evidence of cardiovascular disease (CVD) reduction with sodium–glucose cotransporter 2 inhibitors (SGLT-2is) and some glucagon-like peptide 1 receptor agonists (GLP-1RAs) in type 2 diabetes (T2D). A key recommendation is that all those with T2D who are (antihyperglycemic) drug naïve or on metformin monotherapy should be CVD risk stratified and an SGLT-2i or a GLP-1RA initiated in all those at high or very high risk, irrespective of glycated hemoglobin. We assessed the impact of these guidelines in Scotland were they introduced as is.Research design and methods: Using a nationwide diabetes register in Scotland, we did a cross-sectional analysis, using variables in our register for risk stratification at 1 January 2019. We were conservative in our definitions, assuming the absence of a risk factor where data were not available. The risk classifications were applied to people who were drug naïve or on metformin monotherapy and the anticipated prescribing change calculated.Results: Of the 265,774 people with T2D in Scotland, 53,194 (20.0% of those with T2D) were drug naïve, and56,906(21.4%) were on metformin monotherapy. Of these, 74.5%and72.4%, respectively, were estimated as at least high risk given the guideline risk definitions.Conclusions: Thus, 80,830 (30.4%) of all those with T2D (n 5 265,774) would start one of these drug classes according to table 7 and figure 3 of the guideline. The sizeable impact on drug budgets, enhanced clinical monitoring, and the trade-off with reduced CVD-related health care costs will need careful consideration.</p

Effect of serum sample storage temperature on metabolomic and proteomic biomarkers

Prospective biomarker studies can be used to identify biomarkers predictive of disease onset. However, if serum biomarkers are measured years after their collection, the storage conditions might affect analyte concentrations. Few data exists concerning which metabolites and proteins are affected by storage at - 20 degrees C vs - 80 degrees C. Our objectives were to document analytes affected by storage of serum samples at - 20 degrees C vs - 80 degrees C, and to identify those indicative of the storage temperature. We utilized liquid chromatography tandem mass spectrometry and Luminex to quantify 300 analytes from serum samples of 16 Finnish individuals with type 1 diabetes, with split-aliquot samples stored at - 80 degrees C and - 20 degrees C for a median of 4.2 years. Results were validated in 315 Finnish and 916 Scottish individuals with type 1 diabetes, stored at -20 degrees C and at - 80 degrees C, respectively. After quality control, we analysed 193 metabolites and proteins of which 120 were apparently unaffected and 15 clearly susceptible to storage at - 20 degrees C vs - 80 degrees C. Further, we identified serum glutamate/glutamine ratio greater than 0.20 as a biomarker of storage at - 20 degrees C vs - 80 degrees C. The results provide a catalogue of analytes unaffected and affected by storage at - 20 degrees C vs - 80 degrees C and biomarkers indicative of suboptimal storage.Peer reviewe