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Identified genetic loci for C-peptide and age at diagnosis (AAD) in individuals with type 1 diabetes (T1D) explain only a small proportion of their variation. Here, we aimed to perform large metagenome-wide association studies (GWAS) of C-peptide and AAD in T1D; and to identify the HLA allele/haplotypes associated with C-peptide and AAD. 7,252 and 7,923 European individuals with T1D were included in C-peptide and AAD GWAS, respectively. HLA-DQB1*06:02 which is strongly protective against T1D was associated with higher C-peptide. HLA-DQB1*03:02, HLADRB1*03:01 and HLA-A*24:02 which increase T1D risk were independently associated with younger AAD. HLA-DR3-DR4 haplotype combination, the strongest T1D susceptibility factor, was associated with younger AAD. Outside HLA region, rs115673528 on Chr5 (GABRG2) was associated with C-peptide, and an indel, rs111970692, on Chr15 within CTSH, a known T1D locus, was associated with AAD. Genetically predicted CTSH expression, methylation and protein levels were associated with AAD; Mendelian randomization analysis suggested that higher levels of procathepsin H reduce AAD. In conclusion, some HLA allele/haplotypes associated with T1D also contribute to variability of C-peptide and AAD. Outside HLA, T1D loci are generally not associated with C-peptide or AAD. CTSH could be a potential therapeutic target to delay development/progression of type 1 diabetes., (© 2024 by the American Diabetes Association.)

Background: Population-based incidence data on young-adult-onset type 1 diabetes and type 2 diabetes are limited. We aimed to examine secular trends in the incidence of diagnosed type 1 diabetes and type 2 diabetes with an age of onset between 15 and 39 years., Methods: In this multicountry aggregate data analysis, we assembled eight administrative datasets from high-income jurisdictions and countries (Australia, Denmark, Finland, Hungary, Japan, Scotland, South Korea, and Spain [Catalonia]) that had appropriate data available from an international diabetes consortium (GLOBODIAB) describing incidence by diabetes type among people aged 15-39 years from 2000 to 2020. We modelled type 1 diabetes and type 2 diabetes incidence rates using Poisson regression including age and calendar time by sex., Findings: During the years 2000-20, there were 349 591 incident diabetes (both types) cases from 346 million person-years of follow-up among people aged 15-39 years. Over time, there was no statistically significant change in the incidence of type 1 diabetes in Hungary and Japan. The incidence of type 1 diabetes significantly increased in Australia, Denmark, Finland, Scotland, South Korea, and Spain, with annual changes ranging from 0·5% to 6·0%. The incidence of type 2 diabetes significantly increased in four of eight jurisdictions (Denmark, Finland, Japan, and South Korea), with annual increases from 2·0% to 8·5%. The magnitude of increase in incidence of type 2 diabetes was greater in Asian than non-Asian jurisdictions. There was no statistically significant change in type 2 diabetes incidence in Australia and Hungary. The incidence of type 2 diabetes significantly decreased in Scotland and Spain, with annual changes of -0·7% and -1·5%, respectively., Interpretation: There is variability in the trajectory of the incidence of young-adult-onset type 2 diabetes among high-income countries or jurisdictions, with a greater evidence of increase in Asian than non-Asian countries. Evolving trends in the incidence of type 1 and type 2 diabetes in young adults call for the ongoing surveillance of diabetes incidence and a greater research focus on this population., Funding: US Centers for Disease Control and Prevention, Diabetes Australia Research Programme, and Victoria State Government Operational Infrastructure Support Programme., Competing Interests: Declaration of interests BC has received stock or stock options from Novo Nordisk. HMC has received payments or honoraria for speakers bureaus from Novo Nordisk; has been supported for participation on an Advisory Board from Novo Nordisk and Bayer AG; and has received stock or stock options from Roche Pharmaceuticals and Bayer AG. ZK is employed by MSD Pharma Hungary, outside the current work. YN received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Sanofi, Daiichi Sankyo, and DeSC Healthcare. DM has received consulting fees from AB Biotics, Amarna, Ferrer, Eli Lilly, MSD, Novo Nordisk, and Sanofi; and has received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Abbott, Amgen, AstraZeneca, Gilead, Eli Lilly, Menarini, Novo Nordisk, and Sanofi. JES has received consulting fees from AstraZeneca, Sanofi, Novo Nordisk, MSD, Eli Lilly, Pfizer, and GSK; and has also received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from AstraZeneca, Mylan, Sanofi, Boehringer Ingelheim, Zuellig, and Abbott., (Copyright © 2024 Published by Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Aims: Prescribing of antidepressant and antipsychotic drugs in general populations has increased in the United Kingdom, but prescribing trends in people with type 2 diabetes (T2D) have not previously been investigated. The aim of this study was to describe time trends in annual prevalence of antidepressant and antipsychotic drug prescribing in adult patients with T2D., Methods: We conducted repeated annual cross-sectional analysesof a population-based diabetes registry with 99% coverage, derived from primary and secondary care data in Scotland, from 2004 to 2021. For each cross-sectional calendar year time period, we calculated the prevalence of antidepressant and antipsychotic drug prescribing, overall and by sociodemographic characteristics and drug subtype., Results: The number of patients with a T2D diagnosis in Scotland increased from 161 915 in 2004 to 309 288 in 2021. Prevalence of antidepressant and antipsychotic prescribing in patients with T2D increased markedly between 2004 and 2021 (from 20.0 per 100 person-years to 33.3 per 100 person-years and from 2.8 per 100 person-years to 4.7 per 100 person-years, respectively). We observed this pattern for all drug subtypes except for first-generation antipsychotics, prescribing of which remained largely stable. The degree of increase, as well as the overall prevalence of prescribing, differed by age, sex, socioeconomic status and subtype of drug class., Conclusions: There has been a marked increase in the prevalence of antidepressant and antipsychotic prescribing in patients with T2D in Scotland. Further research should identify the reasons for this increase, including indication for use and the extent to which this reflects increases in incident prescribing rather than increased duration., (© 2024 The Author(s). British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Objective: In this study we examine whether hospitalized coronavirus disease 2019 (COVID-19) pneumonia increases long-term cardiovascular mortality more than other hospitalized pneumonias in people with type 2 diabetes and aim to quantify the relative cardiovascular disease (CVD) mortality risks associated with COVID-19 versus non-COVID-19 pneumonia., Research Design and Methods: With use of the SCI-Diabetes register, two cohorts were identified: individuals with type 2 diabetes in 2016 and at the 2020 pandemic onset. Hospital and death records were linked for determination of pneumonia exposure and CVD deaths. Poisson regression estimated rate ratios (RRs) for CVD death associated with both pneumonia types, with adjustment for confounders. Median follow-up durations were 1,461 days (2016 cohort) and 700 days (2020 cohort)., Results: The adjusted RR for CVD death following non-COVID-19 pneumonia was 5.51 (95% CI 5.31-5.71) prepandemic and 7.3 (6.86-7.76) during the pandemic. For COVID-19 pneumonia, the RR was 9.13 (8.55-9.75). Beyond 30 days post pneumonia, the RRs converged, to 4.24 (3.90-4.60) for non-COVID-19 and 3.35 (3.00-3.74) for COVID-19 pneumonia, consistent even with exclusion of prior CVD cases., Conclusions: Hospitalized pneumonia, irrespective of causal agent, marks an increased risk for CVD death immediately and over the long-term. COVID-19 pneumonia poses a higher CVD death risk than other pneumonias in the short-term, but this distinction diminishes over time. These insights underscore the need for including pneumonia in CVD risk assessments, with particular attention to the acute impact of COVID-19 pneumonia., (© 2024 by the American Diabetes Association.)

Background/aims: Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM., Methods: Retinal images, quality assurance (QA) data and routine DR grades were obtained from national datasets in 179 944 patients for years 2006-2016. QA grades were available for 744 images. We developed a deep learning-based algorithm to detect whether either eye contained ungradable images or any DR. The sensitivity and specificity were evaluated against consensus QA grades and routine grades., Results: Images used in QA which were ungradable or with DR were detected by deep learning with better specificity compared with manual graders (p<0.001) and with iGradingM (p<0.001) at the same sensitivities. Any DR according to the DES final grade was detected with 89.19% (270 392/303 154) sensitivity and 77.41% (500 945/647 158) specificity. Observable disease and referable disease were detected with sensitivities of 96.58% (16 613/17 201) and 98.48% (22 600/22 948), respectively. Overall, 43.84% of screening episodes would require manual grading., Conclusion: A deep learning-based system for DR grading was evaluated in QA data and images from 11 years in 50% of people attending a national DR screening programme. The system could reduce the manual grading workload at the same sensitivity compared with the current automated grading system., Competing Interests: Competing interests: HMC is the principal investigator on the JDRF UK grant. The employment of ADF and JM was with this funding. HMC and PMM have declared stock options in Bayer AG and Roche Pharmaceuticals. HMC has received grants from AstraZeneca LP, Regeneron, Pfizer, Novo Nordisk and Eli Lilly and Company, and is on the advisory panels or boards of Novo Nordisk, Eli Lilly and Company, Regeneron, Novartis Pharmaceuticals, Bayer AG and Sanofi-Aventis. HMC has received payments for speakers bureaus and honoraria from Eli Lilly and Company, Regeneron and Novartis Pharmaceuticals., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Aims/hypothesis: The aim of this study was to compare cardiovascular risk management among people with type 2 diabetes according to severe mental illness (SMI) status., Methods: We used linked electronic data to perform a retrospective cohort study of adults diagnosed with type 2 diabetes in Scotland between 2004 and 2020, ascertaining their history of SMI from hospital admission records. We compared total cholesterol, systolic BP and HbA 1c target level achievement 1 year after diabetes diagnosis, and receipt of a statin prescription at diagnosis and 1 year thereafter, by SMI status using logistic regression, adjusting for sociodemographic factors and clinical history., Results: We included 291,644 individuals with type 2 diabetes, of whom 1.0% had schizophrenia, 0.5% had bipolar disorder and 3.3% had major depression. People with SMI were less likely to achieve cholesterol targets, although this difference did not reach statistical significance for all disorders. However, people with SMI were more likely to achieve systolic BP targets compared to those without SMI, with effect estimates being largest for schizophrenia (men: adjusted OR 1.72; 95% CI 1.49, 1.98; women: OR 1.64; 95% CI 1.38, 1.96). HbA 1c target achievement differed by SMI disorder and sex. Among people without previous CVD, statin prescribing was similar or better in those with vs those without SMI at diabetes diagnosis and 1 year later. In people with prior CVD, SMI was associated with lower odds of statin prescribing at diabetes diagnosis (schizophrenia: OR 0.54; 95% CI 0.43, 0.68, bipolar disorder: OR 0.75; 95% CI 0.56, 1.01, major depression: OR 0.92; 95% CI 0.83, 1.01), with this difference generally persisting 1 year later., Conclusions/interpretation: We found disparities in cholesterol target achievement and statin prescribing by SMI status. This reinforces the importance of clinical review of statin prescribing for secondary prevention of CVD, particularly among people with SMI., (© 2024. The Author(s).)

Background/aims: National 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., Methods: We consider 21 346 and 247 233 people with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (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 was used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed., Results: The DL predictor increased the area under the receiver operating characteristic curve 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 with the current recall policy., Conclusions: We showed that, compared with 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., Competing Interests: Competing interests: HMC is principal investigator on the JDRF grant detailed in the paper. The employment of AF and JM was with this funding. HMC and PMM have declared stock options in Bayer and Roche Pharmaceuticals. HMC has received grants from AstraZeneca, Regeneron, Pfizer, Novo Nordisk and Eli Lilly and Company and is on advisory panels or boards of Novo Nordisk, Eli Lilly and Company, Regeneron, Novartis Pharmaceuticals, Bayer and Sanofi Aventis. HMC has received payments for Speakers Bureaus and honoraria from Eli Lilly and Company, Regeneron and Novartis Pharmaceuticals., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: P.M.M. declares stock ownership in the following: Bayer and Roche Pharmaceuticals. H.M.C. declares grants from Juvenile Diabetes Research Foundation International, Diabetes UK, IQVIA, Chief Scientist Office, Medical Research Council (UK Research and Innovation), and European Union Commission, honoraria from Novo Nordisk, advisory board fees paid through her institution from Novo Nordisk and Bayer, and stock ownership in Bayer and Roche Pharmaceuticals. S.H. declares consulting fees paid through his institution from Vertex Pharma, Zucara Pharmaceuticals, and Zealand Pharma, honoraria from Medtronic and Novo Nordisk, and has served as Chair of DSMB for Eli Lilly development programme and as a Member of Novo Nordisk Independent Advisory Board. R.J.M. declares honoraria from Sanofi and Novo Nordisk for lectures and presentations, travel support from Sanofi, and is a non-executive member of NHS Tayside Health Board. F.W.G. declares advisory board fees from Abbott Diabetes and Insulet, speaker fees from Abbott Diabetes, Insulet, Novo Nordisk, and Lilly, and travel support from Abbott Diabetes and Novo Nordisk. S.P. declares grants from Novo Nordisk, Lilly, and Amgen, travel support from Novo Nordisk, and has participated on an Advisory board for Roche Pharmaceuticals. N.S. has consulted for and/or received speaker honoraria from Abbott Laboratories, Abbvie, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Menarini-Ricerche, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and received grant support paid to his University from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work. No other potential conflicts of interest relevant to this article were reported., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Aims: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD)., Methods: DL models were trained to jointly predict future CVD risk and CVD risk factors and used to output a DL score. Poisson regression models including clinical risk factors with and without a DL score were fitted to study cohorts with 2,072 and 38,730 incident CVD events in type 1 (T1DM) and type 2 diabetes (T2DM) respectively., Results: DL scores were independently associated with incident CVD with adjusted standardised incidence rate ratios of 1.14 (P = 3 × 10 -04 95 % CI (1.06, 1.23)) and 1.16 (P = 4 × 10 -33 95 % CI (1.13, 1.18)) in T1DM and T2DM cohorts respectively. The differences in predictive performance between models with and without a DL score were statistically significant (differences in test log-likelihood 6.7 and 51.1 natural log units) but the increments in C-statistics from 0.820 to 0.822 and from 0.709 to 0.711 for T1DM and T2DM respectively, were small., Conclusions: These results show that in people with diabetes, retinal photographs contain information on future CVD risk. However for this to contribute appreciably to clinical prediction of CVD further approaches, including exploitation of serial images, need to be evaluated., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The "omnigenic" hypothesis postulates that the polygenic effects of common SNPs on a typical complex trait are mediated through trans-effects on expression of a relatively sparse set of effector ("core") genes. We tested this hypothesis in a study of 4,964 cases of type 1 diabetes (T1D) and 7,497 controls by using summary statistics to calculate aggregated (excluding the HLA region) trans-scores for gene expression in blood. From associations of T1D with aggregated trans-scores, nine putative core genes were identified, of which three-STAT1, CTLA4 and FOXP3-are genes in which variants cause monogenic forms of autoimmune diabetes. Seven of these genes affect the activity of regulatory T cells, and two are involved in immune responses to microbial lipids. Four T1D-associated genomic regions could be identified as master regulators via trans-effects on gene expression. These results support the sparse effector hypothesis and reshape our understanding of the genetic architecture of T1D., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: The Scottish Diabetes Research Network (SDRN)-diabetes research platform was established to combine disparate electronic health record data into research-ready linked datasets for diabetes research in Scotland. The resultant cohort, 'The SDRN-National Diabetes Dataset (SDRN-NDS)', has many uses, for example, understanding healthcare burden and socioeconomic trends in disease incidence and prevalence, observational pharmacoepidemiology studies and building prediction tools to support clinical decision making., Participants: We estimate that >99% of those diagnosed with diabetes nationwide are captured into the research platform. Between 2006 and mid-2020, the cohort comprised 472 648 people alive with diabetes at any point in whom there were 4 million person-years of follow-up. Of the cohort, 88.1% had type 2 diabetes, 8.8% type 1 diabetes and 3.1% had other types (eg, secondary diabetes). Data are captured from all key clinical encounters for diabetes-related care, including diabetes clinic, primary care and podiatry and comprise clinical history and measurements with linkage to blood results, microbiology, prescribed and dispensed drug and devices, retinopathy screening, outpatient, day case and inpatient episodes, birth outcomes, cancer registry, renal registry and causes of death., Findings to Date: There have been >50 publications using the SDRN-NDS. Examples of recent key findings include analysis of the incidence and relative risks for COVID-19 infection, drug safety of insulin glargine and SGLT2 inhibitors, life expectancy estimates, evaluation of the impact of flash monitors on glycaemic control and diabetic ketoacidosis and time trend analysis showing that diabetic ketoacidosis (DKA) remains a major cause of death under age 50 years. The findings have been used to guide national diabetes strategy and influence national and international guidelines., Future Plans: The comprehensive SDRN-NDS will continue to be used in future studies of diabetes epidemiology in the Scottish population. It will continue to be updated at least annually, with new data sources linked as they become available., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: We report the first study to estimate the socioeconomic gap in period life expectancy (LE) and life years spent with and without complications in a national cohort of individuals with type 1 diabetes., Methods: This retrospective cohort study used linked healthcare records from SCI-Diabetes, the population-based diabetes register of Scotland. We studied all individuals aged 50 and older with a diagnosis of type 1 diabetes who were alive and residing in Scotland on 1 January 2013 (N = 8591). We used the Scottish Index of Multiple Deprivation (SIMD) 2016 as an area-based measure of socioeconomic deprivation. For each individual, we constructed a history of transitions by capturing whether individuals developed retinopathy/maculopathy, cardiovascular disease, chronic kidney disease, and diabetic foot, or died throughout the study period, which lasted until 31 December 2018. Using parametric multistate survival models, we estimated total and state-specific LE at an attained age of 50., Results: At age 50, remaining LE was 22.2 years (95% confidence interval (95% CI): 21.6 - 22.8) for males and 25.1 years (95% CI: 24.4 - 25.9) for females. Remaining LE at age 50 was around 8 years lower among the most deprived SIMD quintile when compared with the least deprived SIMD quintile: 18.7 years (95% CI: 17.5 - 19.9) vs. 26.3 years (95% CI: 24.5 - 28.1) among males, and 21.2 years (95% CI: 19.7 - 22.7) vs. 29.3 years (95% CI: 27.5 - 31.1) among females. The gap in life years spent without complications was around 5 years between the most and the least deprived SIMD quintile: 4.9 years (95% CI: 3.6 - 6.1) vs. 9.3 years (95% CI: 7.5 - 11.1) among males, and 5.3 years (95% CI: 3.7 - 6.9) vs. 10.3 years (95% CI: 8.3 - 12.3) among females. SIMD differences in transition rates decreased marginally when controlling for time-updated information on risk factors such as HbA1c, blood pressure, BMI, or smoking., Conclusions: In addition to societal interventions, tailored support to reduce the impact of diabetes is needed for individuals from low socioeconomic backgrounds, including access to innovations in management of diabetes and the prevention of complications., Competing Interests: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. TMC reports grants from Diabetes UK Grant: 18/0005786, during the conduct of the study. SHW reports meeting attendance supported by Novo Nordisk, outside the submitted work. RJM reports personal fees from Sanofi, personal fees from NovoNordisk, outside the submitted work. NS reports personal fees from Amgen, personal fees from AstraZeneca, grants and personal fees from Boehringer Ingelheim, personal fees from Eli Lilly, personal fees from Novo Nordisk, personal fees from Pfizer, personal fees from Sanofi, outside the submitted work; HMC reports grants, personal fees and other from Eli Lilly and Company, during the conduct of the study; grants from AstraZeneca LP, other from Novartis Pharmaceuticals, grants from Regeneron, grants from Pfizer Inc, other from Roche Pharmaceuticals, other from Sanofi Aventis, grants and personal fees from Novo Nordisk, outside the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials. No other disclosures were reported.

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 °C vs - 80 °C. Our objectives were to document analytes affected by storage of serum samples at - 20 °C vs - 80 °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 °C and - 20 °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 °C and at - 80 °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 °C vs - 80 °C. Further, we identified serum glutamate/glutamine ratio greater than 0.20 as a biomarker of storage at - 20 °C vs - 80 °C. The results provide a catalogue of analytes unaffected and affected by storage at - 20 °C vs - 80 °C and biomarkers indicative of sub-optimal storage., (© 2022. The Author(s).)

Aims/hypothesis: We assessed the real-world effect of flash monitor (FM) usage on HbA 1c levels and diabetic ketoacidosis (DKA) and severe hospitalised hypoglycaemia (SHH) rates among people with type 1 diabetes in Scotland and across sociodemographic strata within this population., Methods: This study was retrospective, observational and registry based. Using the national diabetes registry, 14,682 individuals using an FM at any point between 2014 and mid-2020 were identified. Within-person change from baseline in HbA 1c following FM initiation was modelled using linear mixed models accounting for within-person pre-exposure trajectory. DKA and SHH events were captured through linkage to hospital admission and mortality data. The difference in DKA and SHH rates between FM-exposed and -unexposed person-time was assessed among users, using generalised linear mixed models with a Poisson likelihood. In a sensitivity analysis, we tested whether changes in these outcomes were seen in an age-, sex- and baseline HbA 1c -matched sample of non-users over the same time period., Results: Prevalence of ever-FM use was 45.9% by mid-2020, with large variations by age and socioeconomic status: 64.3% among children aged <13 years vs 32.7% among those aged ≥65 years; and 54.4% vs 36.2% in the least-deprived vs most-deprived quintile. Overall, the median (IQR) within-person change in HbA 1c in the year following FM initiation was -2.5 (-9.0, 2.5) mmol/mol (-0.2 [-0.8, 0.2]%). The change varied widely by pre-usage HbA 1c : -15.5 (-31.0, -4.0) mmol/mol (-1.4 [-2.8, -0.4]%) in those with HbA 1c  > 84 mmol/mol [9.8%] and 1.0 (-2.0, 5.5) mmol/mol (0.1 [-0.2, 0.5]%) in those with HbA 1c  < 54 mmol/mol (7.1%); the corresponding estimated fold change (95% CI) was 0.77 (0.76, 0.78) and 1.08 (1.07, 1.09). Significant reductions in HbA 1c were found in all age bands, sexes and socioeconomic strata, and regardless of prior/current pump use, completion of a diabetes education programme or early FM adoption. Variation between the strata of these factors beyond that driven by differing HbA 1c at baseline was slight. No change in HbA 1c in matched non-users was observed in the same time period (median [IQR] within-person change = 0.5 [-5.0, 5.5] mmol/mol [0.0 (-0.5, 0.5)%]). DKA rates decreased after FM initiation overall and in all strata apart from the adolescents. Estimated overall reduction in DKA event rates (rate ratio) was 0.59 [95% credible interval (CrI) 0.53, 0.64]) after FM vs before FM initiation, accounting for pre-exposure trend. Finally, among those at higher risk for SHH, estimated reduction in event rates was rate ratio 0.25 (95%CrI 0.20, 0.32) after FM vs before FM initiation., Conclusions/interpretation: FM initiation is associated with clinically important reductions in HbA 1c and striking reduction in DKA rate. Increasing uptake among the socioeconomically disadvantaged offers considerable potential for tightening the current socioeconomic disparities in glycaemia-related outcomes., (© 2021. The Author(s).)

Objective: Whether advances in the management of type 1 diabetes are reducing rates of diabetic ketoacidosis (DKA) is unclear. We investigated time trends in DKA rates in a national cohort of individuals with type 1 diabetes monitored for 14 years, overall and by socioeconomic characteristics., Research Design and Methods: All individuals in Scotland with type 1 diabetes who were alive and at least 1 year old between 1 January 2004 and 31 December 2018 were identified using the national register ( N = 37,939). DKA deaths and hospital admissions were obtained through linkage to Scottish national death and morbidity records. Bayesian regression was used to test for DKA time trends and association with risk markers, including socioeconomic deprivation., Results: There were 30,427 DKA admissions and 472 DKA deaths observed over 393,223 person-years at risk. DKA event rates increased over the study period (incidence rate ratio [IRR] per year 1.058 [95% credibility interval 1.054-1.061]). Males had lower rates than females (IRR male-to-female 0.814 [0.776-0.855]). DKA incidence rose in all age-groups other than 10- to 19-year-olds, in whom rates were the highest, but fell over the study. There was a large socioeconomic differential (IRR least-to-most deprived quintile 0.446 [0.406-0.490]), which increased during follow-up. Insulin pump use or completion of structured education were associated with lower DKA rates, and antidepressant and methadone prescription were associated with higher DKA rates., Conclusions: DKA incidence has risen since 2004, except in 10- to 19-year-olds. Of particular concern are the strong and widening socioeconomic disparities in DKA outcomes. Efforts to prevent DKA, especially in vulnerable groups, require strengthening., (© 2021 by the American Diabetes Association.)

Aims/hypothesis: We aimed to report current rates of CVD in type 1 diabetes and to develop a CVD risk prediction tool for type 1 diabetes., Methods: A cohort of 27,527 people with type 1 diabetes without prior CVD was derived from the national register in Scotland. Incident CVD events during 199,552 person-years of follow-up were ascertained using hospital admissions and death registers. A Poisson regression model of CVD was developed and then validated in the Swedish National Diabetes Register (n = 33,183). We compared the percentage with a high 10 year CVD risk (i.e., ≥10%) using the model with the percentage eligible for statins using current guidelines by age., Results: The age-standardised rate of CVD per 100,000 person-years was 4070 and 3429 in men and women, respectively, with type 1 diabetes in Scotland, and 4014 and 3956 in men and women in Sweden. The final model was well calibrated (Hosmer-Lemeshow test p > 0.05) and included a further 22 terms over a base model of age, sex and diabetes duration (C statistic 0.82; 95% CI 0.81, 0.83). The model increased the base model C statistic from 0.66 to 0.80, from 0.60 to 0.75 and from 0.62 to 0.68 in those aged <40, 40-59 and ≥ 60 years, respectively (all p values <0.005). The model required minimal calibration in Sweden and had a C statistic of 0.85. Under current guidelines, >90% of those aged 20-39 years and 100% of those ≥40 years with type 1 diabetes were eligible for statins, but it was not until age 65 upwards that 100% had a modelled risk of CVD ≥10% in 10 years., Conclusions/interpretation: A prediction tool such as that developed here can provide individualised risk predictions. This 10 year CVD risk prediction tool could facilitate patient discussions regarding appropriate statin prescribing. Apart from 10 year risk, such discussions may also consider longer-term CVD risk, the potential for greater benefits from early vs later statin intervention, the potential impact on quality of life of an early CVD event and evidence on safety, all of which could influence treatment decisions, particularly in younger people with type 1 diabetes., (© 2021. The Author(s).)

We investigated associations of quantitative levels of N-glycans with hemoglobin A1c (HbA1c), renal function and renal function decline in type 1 diabetes. We measured 46 total N-glycan peaks (GPs) on 1565 serum samples from the Scottish Diabetes Research Network Type 1 Bioresource Study (SDRNT1BIO) and a pool of healthy donors. Quantitation of absolute abundance of each GP used 2AB-labeled mannose-3 as a standard. We studied cross-sectional associations of GPs and derived measures with HbA1c, albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR), and prospective associations with incident albuminuria and final eGFR. All GPs were 1.4 to 3.2 times more abundant in SDRTN1BIO than in the healthy samples. Absolute levels of all GPs were slightly higher with higher HbA1c, with strongest associations for triantennary trigalactosylated disialylated, triantennary trigalactosylated trisialylated structures with core or outer arm fucose, and tetraantennary tetragalactosylated trisialylated glycans. Most GPs showed increased abundance with worsening ACR. Lower eGFR was associated with higher absolute GP levels, most significantly with biantennary digalactosylated disialylated glycans with and without bisect, triantennary trigalactosylated trisialylated glycans with and without outer arm fucose, and core fucosylated biantennary monogalactosylated monosialylated glycans. Although several GPs were inversely associated prospectively with final eGFR, cross-validated multivariable models did not improve prediction beyond clinical covariates. Elevated HbA1c is associated with an altered N-glycan profile in type 1 diabetes. Although we could not establish GPs to be prognostic of future renal function decline independently of HbA1c, further studies to evaluate their impact in the pathogenesis of diabetic kidney disease are warranted., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aims/hypothesis: The aim of this work was to map the number of prescribed drugs over age, sex and area-based socioeconomic deprivation, and to examine the association between the number of drugs and particular high-risk drug classes with adverse health outcomes among a national cohort of individuals with type 1 diabetes., Methods: Utilising linked healthcare records from the population-based diabetes register of Scotland, we identified 28,245 individuals with a diagnosis of type 1 diabetes on 1 January 2017. For this population, we obtained information on health status, predominantly reflecting diabetes-related complications, and information on the total number of drugs and particular high-risk drug classes prescribed. We then studied the association of these baseline-level features with hospital admissions for falls, diabetic ketoacidosis (DKA), and hypoglycaemia or death within the subsequent year using multivariate Cox proportional hazards models., Results: Not considering insulin and treatment for hypoglycaemia, the mean number of prescribed drugs was 4.00 (SD 4.35). The proportion of individuals being prescribed five or more drugs at baseline consistently increased with age (proportion [95% CI]: 0-19 years 2.04% [1.60, 2.49]; 40-49 years 28.50% [27.08, 29.93]; 80+ years 76.04% [67.73, 84.84]). Controlling for age, sex, area-based socioeconomic deprivation and health status, each additional drug at baseline was associated with an increase in the hazard for hospitalisation for falls, hypoglycaemia and death but not for DKA admissions (HR [95% CI]: falls 1.03 [1.01, 1.06]; DKA 1.01 [1.00, 1.03]; hypoglycaemia 1.05 [1.02, 1.07]; death 1.04 [1.02, 1.06]). We found a number of drug classes to be associated with an increased hazard of one or more of these adverse health outcomes, including antithrombotic/anticoagulant agents, corticosteroids, opioids, antiepileptics, antipsychotics, hypnotics and sedatives, and antidepressants., Conclusions: Polypharmacy is common among the Scottish population with type 1 diabetes and is strongly patterned by sociodemographic factors. The number of prescribed drugs and the prescription of particular high-risk drug classes are strong markers of an increased risk of adverse health outcomes, including acute complications of diabetes.

Aims/hypothesis: Our aim was to assess the use of continuous subcutaneous insulin infusion (CSII) in people with type 1 diabetes in Scotland and its association with glycaemic control, as measured by HbA 1c levels, frequency of diabetic ketoacidosis (DKA) and severe hospitalised hypoglycaemia (SHH), overall and stratified by baseline HbA 1c ., Methods: We included 4684 individuals with type 1 diabetes from the national Scottish register, who commenced CSII between 2004 and 2019. We presented crude within-person differences from baseline HbA 1c over time since initiation, crude DKA and SHH event-rates pre-/post-CSII exposure. We then used mixed models to assess the significance of CSII exposure, taking into account: (1) the diffuse nature of the intervention (i.e. structured education often precedes initiation); (2) repeated within-person measurements; and (3) background time-trends occurring pre-intervention., Results: HbA 1c decreased after CSII initiation, with a median within-person change of -5.5 mmol/mol (IQR -12.0, 0.0) (-0.5% [IQR -1.1, 0.0]). Within-person changes were most substantial in those with the highest baseline HbA 1c , with median -21.0 mmol/mol (-30.0, -11.0) (-1.9% [-2.7, -1.0]) change in those with a baseline >84 mmol/mol (9.8%) within a year of exposure, that was sustained: -19.0 mmol/mol (-27.6, -6.5) (-1.7% [-2.5, -0.6]) at ≥5 years. Statistical significance and magnitude of change were supported by the mixed models results. The crude DKA event-rate was significantly lower in post-CSII person-time compared with pre-CSII person-time: 49.6 events (95% CI 46.3, 53.1) per 1000 person-years vs 67.9 (64.1, 71.9); rate ratio from Bayesian mixed models adjusting for pre-exposure trend: 0.61 (95% credible interval [CrI] 0.47, 0.77; posterior probability of reduction pp = 1.00). The crude overall SHH event-rate in post-CSII vs pre-CSII person-time was also lower: 17.8 events (95% CI 15.8, 19.9) per 1000 person-years post-exposure vs 25.8 (23.5, 28.3) pre-exposure; rate ratio from Bayesian mixed models adjusting for pre-exposure trend: 0.67 (95% CrI 0.45, 1.01; pp = 0.97)., Conclusions/interpretation: CSII therapy was associated with marked falls in HbA 1c especially in those with high baseline HbA 1c . CSII was independently associated with reduced DKA and SHH rates. CSII appears to be an effective option for intensive insulin therapy in people with diabetes for improving suboptimal glycaemic control.

Objective: End-stage kidney disease (ESKD) is a life-threatening complication of diabetes that can be prevented or delayed by intervention. Hence, early detection of people at increased risk is essential., Research Design and Methods: From a population-based cohort of 5,460 clinically diagnosed Danish adults with type 1 diabetes followed from 2001 to 2016, we developed a prediction model for ESKD accounting for the competing risk of death. Poisson regression analysis was used to estimate the model on the basis of information routinely collected from clinical examinations. The effect of including an extended set of predictors (lipids, alcohol intake, etc.) was further evaluated, and potential interactions identified in a survival tree analysis were tested. The final model was externally validated in 9,175 adults from Denmark and Scotland., Results: During a median follow-up of 10.4 years (interquartile limits 5.1; 14.7), 303 (5.5%) of the participants (mean [SD] age 42.3 [16.5] years) developed ESKD, and 764 (14.0%) died without having developed ESKD. The final ESKD prediction model included age, male sex, diabetes duration, estimated glomerular filtration rate, micro- and macroalbuminuria, systolic blood pressure, hemoglobin A 1c , smoking, and previous cardiovascular disease. Discrimination was excellent for 5-year risk of an ESKD event, with a C-statistic of 0.888 (95% CI 0.849; 0.927) in the derivation cohort and confirmed at 0.865 (0.811; 0.919) and 0.961 (0.940; 0.981) in the external validation cohorts from Denmark and Scotland, respectively., Conclusions: We have derived and validated a novel, high-performing ESKD prediction model for risk stratification in the adult type 1 diabetes population. This model may improve clinical decision making and potentially guide early intervention., (© 2021 by the American Diabetes Association.)

Genome-wide association studies (GWAS) and linkage studies have had limited success in identifying genome-wide significantly linked regions or risk loci for diabetic nephropathy (DN) in individuals with type 1 diabetes (T1D). As GWAS cohorts have grown, they have also included more documented and undocumented familial relationships. Here we computationally inferred and manually curated pedigrees in a study cohort of >6,000 individuals with T1D and their relatives without diabetes. We performed a linkage study for 177 pedigrees consisting of 452 individuals with T1D and their relatives using a genome-wide genotyping array with >300,000 single nucleotide polymorphisms and PSEUDOMARKER software. Analysis resulted in genome-wide significant linkage peaks on eight chromosomal regions from five chromosomes (logarithm of odds score >3.3). The highest peak was localized at the HLA region on chromosome 6p, but whether the peak originated from T1D or DN remained ambiguous. Of other significant peaks, the chromosome 4p22 region was localized on top of ARHGAP24 , a gene associated with focal segmental glomerulosclerosis, suggesting this gene may play a role in DN as well. Furthermore, rare variants have been associated with DN and chronic kidney disease near the 4q25 peak, localized on top of CCSER1 ., (© 2021 by the American Diabetes Association.)

Aims: To examine candidate insulin resistance single nucleotide polymorphisms (SNPs) for associations with glycemic control, insulin resistance, BMI, and complications in an observational type 1 diabetes (T1D) cohort: the Pittsburgh Epidemiology of Diabetes Complications (EDC) study., Methods: In 422 European-ancestry participants, we assessed associations using additive models between 15 candidate SNPs and 25-year mortality, cardiovascular disease, microalbuminuria, overt nephropathy and proliferative retinopathy, and 25-year mean HbA1c, estimated glucose disposal rate (eGDR, inverse measure of insulin resistance), and BMI., Results: The A allele of rs12970134 was associated with higher mean HbA1c (β = +0.34 ± 0.09, p = 0.00009) and nominally associated with worse eGDR (p = 0.02). Further analyses suggest the HbA1c association may be modified by diabetes therapy regimen: rs12970134 AA genotype was associated with higher HbA1c under non-intensive therapy conditions (<3 insulin injections/day or monitoring blood glucose<3 times/day [p = 0.004]), but not under intensive therapy (≥3 injections/day or insulin pump and monitoring glucose≥3 times/day [p = 0.71]). There were no significant associations between any SNPs and BMI or complications., Conclusions: rs12970134, near MC4R, is strongly associated with HbA1c in this cohort. Further exploration of this genomic region is warranted, as it may hold promise for discovering new therapeutic targets to improve glycemic control in T1D., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The objective of this study was to investigate the relation of severe COVID-19 to prior drug prescribing., Methods: Severe cases were defined by entry to critical care or fatal outcome. For this matched case-control study (REACT-SCOT), all 4251 cases of severe COVID-19 in Scotland since the start of the epidemic were matched for age, sex and primary care practice to 36,738 controls from the population register. Records were linked to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days., Results: Severe COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in a care home, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.8, 13.3), and in those without any of the conditions designated as conferring increased risk of COVID-19. Of 17 drug classes postulated at the start of the epidemic to be "medications compromising COVID", all were associated with increased risk of severe COVID-19 and these associations were present in those without any of the designated risk conditions. The fraction of cases in the population attributable to exposure to these drug classes was 38%. The largest effect was for antipsychotic agents: rate ratio 4.18 (3.42, 5.11). Other drug classes with large effects included proton pump inhibitors (rate ratio 2.20 (1.72, 2.83) for = 2 defined daily doses/day), opioids (3.66 (2.68, 5.01) for = 50 mg morphine equivalent/day) and gabapentinoids. These associations persisted after adjusting for covariates and were stronger with recent than with non-recent exposure., Conclusions: Severe COVID-19 is associated with polypharmacy and with drugs that cause sedation, respiratory depression, or dyskinesia; have anticholinergic effects; or affect the gastrointestinal system. These associations are not easily explained by co-morbidity. Measures to reduce the burden of mortality and morbidity from COVID-19 should include reinforcing existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy., Registration: ENCEPP number EUPAS35558.

Background: We aimed to ascertain the cumulative risk of fatal or critical care unit-treated COVID-19 in people with diabetes and compare it with that of people without diabetes, and to investigate risk factors for and build a cross-validated predictive model of fatal or critical care unit-treated COVID-19 among people with diabetes., Methods: In this cohort study, we captured the data encompassing the first wave of the pandemic in Scotland, from March 1, 2020, when the first case was identified, to July 31, 2020, when infection rates had dropped sufficiently that shielding measures were officially terminated. The participants were the total population of Scotland, including all people with diabetes who were alive 3 weeks before the start of the pandemic in Scotland (estimated Feb 7, 2020). We ascertained how many people developed fatal or critical care unit-treated COVID-19 in this period from the Electronic Communication of Surveillance in Scotland database (on virology), the RAPID database of daily hospitalisations, the Scottish Morbidity Records-01 of hospital discharges, the National Records of Scotland death registrations data, and the Scottish Intensive Care Society and Audit Group database (on critical care). Among people with fatal or critical care unit-treated COVID-19, diabetes status was ascertained by linkage to the national diabetes register, Scottish Care Information Diabetes. We compared the cumulative incidence of fatal or critical care unit-treated COVID-19 in people with and without diabetes using logistic regression. For people with diabetes, we obtained data on potential risk factors for fatal or critical care unit-treated COVID-19 from the national diabetes register and other linked health administrative databases. We tested the association of these factors with fatal or critical care unit-treated COVID-19 in people with diabetes, and constructed a prediction model using stepwise regression and 20-fold cross-validation., Findings: Of the total Scottish population on March 1, 2020 (n=5 463 300), the population with diabetes was 319 349 (5·8%), 1082 (0·3%) of whom developed fatal or critical care unit-treated COVID-19 by July 31, 2020, of whom 972 (89·8%) were aged 60 years or older. In the population without diabetes, 4081 (0·1%) of 5 143 951 people developed fatal or critical care unit-treated COVID-19. As of July 31, the overall odds ratio (OR) for diabetes, adjusted for age and sex, was 1·395 (95% CI 1·304-1·494; p<0·0001, compared with the risk in those without diabetes. The OR was 2·396 (1·815-3·163; p<0·0001) in type 1 diabetes and 1·369 (1·276-1·468; p<0·0001) in type 2 diabetes. Among people with diabetes, adjusted for age, sex, and diabetes duration and type, those who developed fatal or critical care unit-treated COVID-19 were more likely to be male, live in residential care or a more deprived area, have a COVID-19 risk condition, retinopathy, reduced renal function, or worse glycaemic control, have had a diabetic ketoacidosis or hypoglycaemia hospitalisation in the past 5 years, be on more anti-diabetic and other medication (all p<0·0001), and have been a smoker (p=0·0011). The cross-validated predictive model of fatal or critical care unit-treated COVID-19 in people with diabetes had a C-statistic of 0·85 (0·83-0·86)., Interpretation: Overall risks of fatal or critical care unit-treated COVID-19 were substantially elevated in those with type 1 and type 2 diabetes compared with the background population. The risk of fatal or critical care unit-treated COVID-19, and therefore the need for special protective measures, varies widely among those with diabetes but can be predicted reasonably well using previous clinical history., Funding: None., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: The objectives of this study were to identify risk factors for severe coronavirus disease 2019 (COVID-19) and to lay the basis for risk stratification based on demographic data and health records., Methods and Findings: The design was a matched case-control study. Severe COVID-19 was defined as either a positive nucleic acid test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the national database followed by entry to a critical care unit or death within 28 days or a death certificate with COVID-19 as underlying cause. Up to 10 controls per case matched for sex, age, and primary care practice were selected from the national population register. For this analysis-based on ascertainment of positive test results up to 6 June 2020, entry to critical care up to 14 June 2020, and deaths registered up to 14 June 2020-there were 36,948 controls and 4,272 cases, of which 1,894 (44%) were care home residents. All diagnostic codes from the past 5 years of hospitalisation records and all drug codes from prescriptions dispensed during the past 240 days were extracted. Rate ratios for severe COVID-19 were estimated by conditional logistic regression. In a logistic regression using the age-sex distribution of the national population, the odds ratios for severe disease were 2.87 for a 10-year increase in age and 1.63 for male sex. In the case-control analysis, the strongest risk factor was residence in a care home, with rate ratio 21.4 (95% CI 19.1-23.9, p = 8 × 10-644). Univariate rate ratios for conditions listed by public health agencies as conferring high risk were 2.75 (95% CI 1.96-3.88, p = 6 × 10-9) for type 1 diabetes, 1.60 (95% CI 1.48-1.74, p = 8 × 10-30) for type 2 diabetes, 1.49 (95% CI 1.37-1.61, p = 3 × 10-21) for ischemic heart disease, 2.23 (95% CI 2.08-2.39, p = 4 × 10-109) for other heart disease, 1.96 (95% CI 1.83-2.10, p = 2 × 10-78) for chronic lower respiratory tract disease, 4.06 (95% CI 3.15-5.23, p = 3 × 10-27) for chronic kidney disease, 5.4 (95% CI 4.9-5.8, p = 1 × 10-354) for neurological disease, 3.61 (95% CI 2.60-5.00, p = 2 × 10-14) for chronic liver disease, and 2.66 (95% CI 1.86-3.79, p = 7 × 10-8) for immune deficiency or suppression. Seventy-eight percent of cases and 52% of controls had at least one listed condition (51% of cases and 11% of controls under age 40). Severe disease was associated with encashment of at least one prescription in the past 9 months and with at least one hospital admission in the past 5 years (rate ratios 3.10 [95% CI 2.59-3.71] and 2.75 [95% CI 2.53-2.99], respectively) even after adjusting for the listed conditions. In those without listed conditions, significant associations with severe disease were seen across many hospital diagnoses and drug categories. Age and sex provided 2.58 bits of information for discrimination. A model based on demographic variables, listed conditions, hospital diagnoses, and prescriptions provided an additional 1.07 bits (C-statistic 0.804). A limitation of this study is that records from primary care were not available., Conclusions: We have shown that, along with older age and male sex, severe COVID-19 is strongly associated with past medical history across all age groups. Many comorbidities beyond the risk conditions designated by public health agencies contribute to this. A risk classifier that uses all the information available in health records, rather than only a limited set of conditions, will more accurately discriminate between low-risk and high-risk individuals who may require shielding until the epidemic is over., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests:HC receives research support and honoraria and is a member of advisory panels or speaker bureaus for Sanofi Aventis, Regeneron, Novartis, Novo-Nordisk and Eli Lilly. HC receives or has recently received non-binding research support from AstraZeneca and Novo-Nordisk. SH received honoraria from Gilead. TMC is a Diabetes UK ‘Sir George Alberti Clinical Research Fellow’ (Grant number: 18/0005786), although the views represented in this article are his own and not those of Diabetes UK. CR reports grants from Public Health Scotland, grants from MRC, during the conduct of the study; and Member of Chief Medical Officer of Scotland Scientific Advisory Group for COVID19 Member of SPI-M a subgroup of the UK Scientific Advisory Group for Epidemics Member of MHRA Advisory Group for Vaccine Safety. All other co-authors declare that no competing interest exists.

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, and 56,906 (21.4%) were on metformin monotherapy. Of these, 74.5% and 72.4%, 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 = 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., (© 2020 by the American Diabetes Association.)

Aims/hypothesis: We aimed to examine whether crude mortality and mortality relative to the general population below 50 years of age have improved in recent years in those with type 1 diabetes., Methods: Individuals with type 1 diabetes aged below 50 and at least 1 year old at any time between 2004 and 2017 in Scotland were identified using the national register. Death data were obtained by linkage to Scottish national death registrations. Indirect age standardisation was used to calculate sex-specific standardised mortality ratios (SMRs). Poisson regression was used to test for calendar-time effects as incidence rate ratios (IRRs)., Results: There were 1138 deaths in 251,143 person-years among 27,935 people with type 1 diabetes. There was a significant decline in mortality rate over time (IRR for calendar year 0.983 [95% CI 0.967, 0.998], p = 0.03), but the SMR remained approximately stable at 3.1 and 3.6 in men and 4.09 and 4.16 in women for 2004 and 2017, respectively. Diabetic ketoacidosis or coma (DKAoC) accounted for 22% of deaths and the rate did not decline significantly (IRR 0.975 [95% CI 0.94, 1.011], p = 0.168); 79.3% of DKAoC deaths occurred out of hospital. Circulatory diseases accounted for 27% of deaths and did decline significantly (IRR 0.946 [95% CI 0.914, 0.979], p = 0.002)., Conclusions/interpretation: Absolute mortality has fallen, but the relative impact of type 1 diabetes on mortality below 50 years has not improved. There is scope to improve prevention of premature circulatory diseases and DKAoC and to develop more effective strategies for enabling people with type 1 diabetes to avoid clinically significant hyper- or hypoglycaemia. Graphical abstract.

Aim: To investigate the effect of DAFNE and continuous subcutaneous insulin infusion in clinical practice., Methods: Within NHS Lothian, continuous subcutaneous insulin infusion started in 2004 and DAFNE education began in 2006. We extracted anonymized data from the national database for all those aged > 18 years with type 1 diabetes having a Dose Adjustment For Normal Eating course or continuous subcutaneous insulin infusion start date (n = 4617)., Results: In total, 956 persons received DAFNE education, and 505 had received an insulin pump, 208 of whom had DAFNE education followed by insulin pump. Mean (SD) HbA 1c before DAFNE education was 68 (15) mmol/mol (8.4% [1.4%]) and 66 (13) mmol/mol (8.2% [1.2%]) before continuous subcutaneous insulin infusion. In the year following DAFNE education, the mean fall in within-person HbA 1c was 3.8 mmol/mol (95% CI 4.0 to 3.4; 0.3% [0.4% to 0.3%]). Those with the poorest control (HbA 1c ≥ 85 mmol/mol [9.9%]) experienced the largest decline (15.7 mmol/mol [1.4%]). Those in the lowest HbA 1c band at initiation (< 53 mmol/mmol [7.0%]) experienced a rise. In the year following continuous subcutaneous insulin infusion initiation there was a mean fall in within-person HbA 1c of 6.6 mmol/mol (6.8 to 6.4; 0.6% [0.6% to 0.6%]). In those with the poorest control (HbA 1c ≥ 85 mmol/mol [9.9%]), the mean fall in HbA 1c was 22.2 mmol/mol (23 to 21; 2.0% [2.1% to 1.9%]). Continuous subcutaneous insulin infusion effectiveness was not different with or without DAFNE education. The effects of both interventions were sustained over 5 years., Conclusions: Both DAFNE education and insulin pump therapy had the greatest effect on HbA 1c in those with higher baseline values. There was little difference to attained HbA 1c when Dose Adjustment For Normal Eating education was introduced before insulin pump therapy., (© 2019 Diabetes UK.)

Objective: To assess the contemporaneous prevalence of diabetic peripheral neuropathy (DPN) in people with type 1 diabetes (T1D) in Scotland and study its cross-sectional association with risk factors and other diabetic complications., Research Design and Methods: We analyzed data from a large representative sample of adults with T1D ( N = 5,558). We assessed the presence of symptomatic neuropathy using the dichotomized (≥4) Michigan Neuropathy Screening Instrument Patient Questionnaire score. Logistic regression models were used to investigate associations between DPN and risk factors, as well as with other complications., Results: The burden of DPN is substantial with 13% prevalence overall. Adjusting for attained age, diabetes duration, and sex, the odds of DPN increased mainly with waist-to-hip ratio, lipids, poor glycemic control (odds ratio 1.51 [95% CI 1.21-1.89] for levels of 75 vs. 53 mmol/mol), ever versus never smoking (1.67 [1.37-2.03]), and worse renal function (1.96 [1.03-3.74] for estimated glomerular filtration rate levels <30 vs. ≥90 mL/min/1.73 m 2 ). The odds significantly decreased with higher HDL cholesterol (0.77 [0.66-0.89] per mmol/L). Living in more deprived areas was associated with DPN (2.17 [1.78-2.65]) for more versus less deprived areas adjusted for other risk factors. Finally, individuals with prevalent DPN were much more likely than others to have other diabetes complications., Conclusions: Diabetic neuropathy remains substantial, particularly affecting those in the most socioeconomically deprived groups. Those with clinically manifest neuropathy also have a higher burden of other complications and elevated levels of modifiable risk factors. These data suggest that there is considerable scope to reduce neuropathy rates and narrow the socioeconomic differential by better risk factor control., (© 2020 by the American Diabetes Association.)

Aims/hypothesis: We examined whether candidate biomarkers in serum or urine can improve the prediction of renal disease progression in type 1 diabetes beyond prior eGFR, comparing their performance with urinary albumin/creatinine ratio (ACR)., Methods: From the population-representative Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) we sampled 50% and 25% of those with starting eGFR below and above 75 ml min -1 [1.73 m] -2 , respectively (N = 1629), and with median 5.1 years of follow-up. Multiplexed ELISAs and single molecule array technology were used to measure nine serum biomarkers and 13 urine biomarkers based on our and others' prior work using large discovery and candidate studies. Associations with final eGFR and with progression to <30 ml min -1 [1.73] m -2 , both adjusted for baseline eGFR, were tested using linear and logistic regression models. Parsimonious biomarker panels were identified using a penalised Bayesian approach, and their performance was evaluated through tenfold cross-validation and compared with using urinary ACR and other clinical record data., Results: Seven serum and seven urine biomarkers were strongly associated with either final eGFR or progression to <30 ml min -1 [1.73 m] -2 , adjusting for baseline eGFR and other covariates (all at p<2.3 × 10 -3 ). Of these, associations of four serum biomarkers were independent of ACR for both outcomes. The strongest associations with both final eGFR and progression to <30 ml min -1 [1.73 m] -2 were for serum TNF receptor 1, kidney injury molecule 1, CD27 antigen, α-1-microglobulin and syndecan-1. These serum associations were also significant in normoalbuminuric participants for both outcomes. On top of baseline covariates, the r 2 for prediction of final eGFR increased from 0.702 to 0.743 for serum biomarkers, and from 0.702 to 0.721 for ACR alone. The area under the receiver operating characteristic curve for progression to <30 ml min -1 [1.73 m] -2 increased from 0.876 to 0.953 for serum biomarkers, and to 0.911 for ACR alone. Other urinary biomarkers did not outperform ACR., Conclusions/interpretation: A parsimonious panel of serum biomarkers easily measurable along with serum creatinine may outperform ACR for predicting renal disease progression in type 1 diabetes, potentially obviating the need for urine testing.

Aims/hypothesis: The aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes., Methods: We used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumin/creatinine ratio (ACR) at recruitment and linked the data to the national electronic healthcare records., Results: Median age was 44.1 years and diabetes duration 20.9 years. The prevalence of CKD stages G1, G2, G3 and G4 and end-stage renal disease (ESRD) was 64.0%, 29.3%, 5.4%, 0.6%, 0.7%, respectively. Micro/macroalbuminuria prevalence was 8.6% and 3.0%, respectively. The incidence rate of ESRD was 2.5 (95% CI 1.9, 3.2) per 1000 person-years. The majority (59%) of those with chronic kidney disease stages G3-G5 did not have albuminuria on the day of recruitment or previously. Over 11.6 years of observation, the median annual decline in eGFR was modest at -1.3 ml min -1 [1.73 m] -2  year -1 (interquartile range [IQR]: -2.2, -0.4). However, 14% experienced a more significant loss of at least 3 ml min -1 [1.73 m] -2 . These decliners had more cardiovascular disease (OR 1.9, p = 5 × 10 -5 ) and retinopathy (OR 1.3 p = 0.02). Adding HbA 1c , prior cardiovascular disease, recent mean eGFR and prior trajectory of eGFR to a model with age, sex, diabetes duration, current eGFR and ACR maximised the prediction of final eGFR (r 2 increment from 0.698 to 0.745, p < 10 -16 ). Attempting to model nonlinearity in eGFR decline or to detect latent classes of decliners did not improve prediction., Conclusions: These data show much lower levels of kidney disease than historical estimates. However, early identification of those destined to experience significant decline in eGFR remains challenging.

Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation ( N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

Background: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown., Methods: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function., Results: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain ( COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition ( BMP7) or renal biology ( COLEC11 and DDR1 )., Conclusions: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment., (Copyright © 2019 by the American Society of Nephrology.)

Aims/hypothesis: We aimed to identify a sparse panel of biomarkers for improving the prediction of renal disease progression in type 1 diabetes., Methods: We considered 859 individuals recruited from the Scottish Diabetes Research Network Type 1 Bioresource (SDRNT1BIO) and 315 individuals from the Finnish Diabetic Nephropathy (FinnDiane) study. All had an entry eGFR between 30 and 75 ml min -1 [1.73 m] -2 , with those from FinnDiane being oversampled for albuminuria. A total of 297 circulating biomarkers (30 proteins, 121 metabolites, 146 tryptic peptides) were measured in non-fasting serum samples using the Luminex platform and LC electrospray tandem MS (LC-MS/MS). We investigated associations with final eGFR adjusted for baseline eGFR and with rapid progression (a loss of more than 3 ml min -1 [1.73 m] -2  year -1 ) using linear and logistic regression models. Panels of biomarkers were identified using a penalised Bayesian approach, and their performance was evaluated through 10-fold cross-validation and compared with using clinical record data alone., Results: For final eGFR, 16 proteins and 30 metabolites or tryptic peptides showed significant association in SDRNT1BIO, and nine proteins and five metabolites or tryptic peptides in FinnDiane, beyond age, sex, diabetes duration, study day eGFR and length of follow-up (all at p < 10 -4 ). The strongest associations were with CD27 antigen (CD27), kidney injury molecule 1 (KIM-1) and α1-microglobulin. Including the Luminex biomarkers on top of baseline covariates increased the r 2 for prediction of final eGFR from 0.47 to 0.58 in SDRNT1BIO and from 0.33 to 0.48 in FinnDiane. At least 75% of the increment in r 2 was attributable to CD27 and KIM-1. However, using the weighted average of historical eGFR gave similar performance to biomarkers. The LC-MS/MS platform performed less well., Conclusions/interpretation: Among a large set of associated biomarkers, a sparse panel of just CD27 and KIM-1 contains most of the predictive information for eGFR progression. The increment in prediction beyond clinical data was modest but potentially useful for oversampling individuals with rapid disease progression into clinical trials, especially where there is little information on prior eGFR trajectories.

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 HbA 1c 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 HbA 1c , 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 HbA 1c 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 HbA 1c 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/m 2 (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 HbA 1c , SBP, body weight and BMI that were at least as large as in clinical trials. Dapagliflozin also prevented the expected rise in HbA 1c and SBP over the period of study.

Objective: Poorer glycemic control in type 1 diabetes may alter N-glycosylation patterns on circulating glycoproteins, and these alterations may be linked with diabetic kidney disease (DKD). We investigated associations between N-glycans and glycemic control and renal function in type 1 diabetes., Research Design and Methods: Using serum samples from 818 adults who were considered to have extreme annual loss in estimated glomerular filtration rate (eGFR; i.e., slope) based on retrospective clinical records, from among 6,127 adults in the Scottish Diabetes Research Network Type 1 Bioresource Study, we measured total and IgG-specific N-glycan profiles. This yielded a relative abundance of 39 total (GP) and 24 IgG (IGP) N-glycans. Linear regression models were used to investigate associations between N-glycan structures and HbA 1c , albumin-to-creatinine ratio (ACR), and eGFR slope. Models were adjusted for age, sex, duration of type 1 diabetes, and total serum IgG., Results: Higher HbA 1c was associated with a lower relative abundance of simple biantennary N-glycans and a higher relative abundance of more complex structures with more branching, galactosylation, and sialylation (GP12, 26, 31, 32, and 34, and IGP19 and 23; all P < 3.79 × 10 -4 ). Similar patterns were seen for ACR and greater mean annual loss of eGFR, which were also associated with fewer of the simpler N-glycans (all P < 3.79 × 10 -4 )., Conclusions: Higher HbA 1c in type 1 diabetes is associated with changes in the serum N-glycome that have elsewhere been shown to regulate the epidermal growth factor receptor and transforming growth factor-β pathways that are implicated in DKD. Furthermore, N-glycans are associated with ACR and eGFR slope. These data suggest that the role of altered N-glycans in DKD warrants further investigation., (© 2017 by the American Diabetes Association.)