80 results found
Misra S, Ke C, Srinivasan S, et al., 2023, Current insights and emerging trends in early-onset type 2 diabetes., Lancet Diabetes Endocrinol
Type 2 diabetes diagnosed in childhood or early adulthood is termed early-onset type 2 diabetes. Cases of early-onset type 2 diabetes are increasing rapidly globally, alongside rising obesity. Compared with a diagnosis later in life, an earlier-onset diagnosis carries an unexplained excess risk of microvascular complications, adverse cardiovascular outcomes, and earlier death. Women with early-onset type 2 diabetes also have a higher risk of adverse pregnancy outcomes. The high burden of complications renders individuals with early-onset type 2 diabetes at future risk of multimorbidity and interventions to reverse these concerning trends should be a priority. Within the early-onset cohort, disease pathophysiology and interventions have been better studied in paediatric-onset (<19 years) type 2 diabetes compared to adults; however, young adults aged 19-39 years (a larger number proportionally) are not well characterised and are also invisible in the current evidence base supporting management, which is derived from trials in later-onset type 2 diabetes. Young adults with type 2 diabetes face challenges in self-management that older individuals are less likely to experience (being in education or of working age, higher diabetes distress, and possible obesity-related stigma and diabetes-related stigma). There is a major research gap as to the optimal strategies to deploy in managing type 2 diabetes in adolescents and young adults, given that current models of care appear to not work as well in this age group. In the face of manifold risk factors (obesity, female sex, social deprivation, non-White European ethnicity, and genetic risk factors) prevention strategies with tailored lifestyle interventions, where needed, are likely to have greater success, but more evidence is needed. In this Review, we draw on evidence from both adolescents and young adults to provide a contemporary update on the current insights and emerging trends in early-onset type 2 diabetes.
Hirani D, Salem V, Khunti K, et al., 2023, Newly detected diabetes during the COVID-19 pandemic: What have we learnt?, Best Pract Res Clin Endocrinol Metab, Vol: 37
The SARS-CoV-2 pandemic has had an unprecedented effect on global health, mortality and healthcare provision. Diabetes has emerged as a key disease entity over the pandemic period, influencing outcomes from COVID-19 but also a tantalising hypothesis that the virus itself may be inducing diabetes. An uptick in diabetes cases over the pandemic has been noted for both type 1 diabetes (in children) and type 2 diabetes but understanding how this increase in incidence relates to the pandemic is challenging. It remains unclear whether indirect effects of the pandemic on behaviour, lifestyle and health have contributed to the increase; whether the virus itself has somehow mediated new-onset diabetes or whether other factors such as stress hyperglycaemic of steroid treatment during COVID-19 infection have played a roll. Within the myriad possibilities are some real challenges in interpreting epidemiological data, assigning diabetes type and understanding what in vitro data are telling us. In this review article we address the issue of newly-diagnosed diabetes during the pandemic, reviewing both epidemiological and basic science data and bringing together both strands of this emerging story.
Conrad N, Misra S, Verbakel JY, et al., 2023, Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK., Lancet, Vol: 401, Pages: 1878-1890
BACKGROUND: A rise in the incidence of some autoimmune disorders has been described. However, contemporary estimates of the overall incidence of autoimmune diseases and trends over time are scarce and inconsistent. We aimed to investigate the incidence and prevalence of 19 of the most common autoimmune diseases in the UK, assess trends over time, and by sex, age, socioeconomic status, season, and region, and we examine rates of co-occurrence among autoimmune diseases. METHODS: In this UK population-based study, we used linked primary and secondary electronic health records from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex and ethnicity. Eligible participants were men and women (no age restriction) with acceptable records, approved for Hospital Episodes Statistics and Office of National Statistics linkage, and registered with their general practice for at least 12 months during the study period. We calculated age and sex standardised incidence and prevalence of 19 autoimmune disorders from 2000 to 2019 and used negative binomial regression models to investigate temporal trends and variation by age, sex, socioeconomic status, season of onset, and geographical region in England. To characterise co-occurrence of autoimmune diseases, we calculated incidence rate ratios (IRRs), comparing incidence rates of comorbid autoimmune disease among individuals with a first (index) autoimmune disease with incidence rates in the general population, using negative binomial regression models, adjusted for age and sex. FINDINGS: Among the 22 009 375 individuals included in the study, 978 872 had a new diagnosis of at least one autoimmune disease between Jan 1, 2000, and June 30, 2019 (mean age 54·0 years [SD 21·4]). 625 879 (63·9%) of these diagnosed individuals were female and 352 993 (36·1%) were male. Over the study period, age and sex standa
Avari P, Lumb A, Flanagan D, et al., 2023, Insulin Pumps and Hybrid Close Loop Systems Within Hospital: A Scoping Review and Practical Guidance From the Joint British Diabetes Societies for Inpatient Care., J Diabetes Sci Technol, Vol: 17, Pages: 625-634
This article is the second of a two-part series providing a scoping review and summary of the Joint British Diabetes Societies for Inpatient Care (JBDS-IP) guidelines on the use of diabetes technology in people with diabetes admitted to hospital. The first part reviewed the use of continuous glucose monitoring (CGM) in hospital. In this article, we focus on the use of continuous subcutaneous insulin infusion (CSII; insulin pumps) and hybrid closed-loop systems in hospital. JBDS-IP advocates enabling people who can self-manage and are willing and capable of using CSII to continue doing so as they would do out of hospital. CSII should be discontinued if the individual is critically ill or hemodynamically unstable. For individuals on hybrid closed-loop systems, the system should be discontinued from auto-mode, and may be used individually (as CGM only or CSII only, if criteria are met). Continuing in closed-loop mode may only be done so under specialist guidance from the Diabetes Team, where the diabetes teams are comfortable and knowledgeable about the specific devices used. Health care organizations need to have clear local policies and guidance to support individuals using these wearable technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting.
Avari P, Lumb A, Flanagan D, et al., 2023, Continuous Glucose Monitoring Within Hospital: A Scoping Review and Summary of Guidelines From the Joint British Diabetes Societies for Inpatient Care., J Diabetes Sci Technol, Vol: 17, Pages: 611-624
Increasing numbers of people, particularly with type 1 diabetes (T1D), are using wearable technologies. That is, continuous subcutaneous insulin infusion (CSII) pumps, continuous glucose monitoring (CGM) systems, and hybrid closed-loop systems, which combine both these elements. Given over a quarter of all people admitted to hospital have diabetes, there is a need for clinical guidelines for when people using them are admitted to hospital. The Joint British Diabetes Societies for Inpatient Care (JBDS-IP) provide a scoping review and summary of guidelines on the use of diabetes technology in people with diabetes admitted to hospital.JBDS-IP advocates enabling people who can self-manage and use their own diabetes technology to continue doing so as they would do out of hospital. Whilst people with diabetes are recommended to achieve a target of 70% time within range (3.9-10.0 mmol/L [70-180 mg/dL]), this can be very difficult to achieve whilst unwell. We therefore recommend targeting hypoglycemia prevention as a priority, keeping time below 3.9 mmol/L (70 mg/dL) at < 1%, being aware of looming hypoglycemia if glucose is between 4.0 and 5.9 mmol/L (72-106 mg/dL), and consider intervening, particularly if there is a downward CGM trend arrow.Health care organizations need clear local policies and guidance to support individuals using diabetes technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting. The current set of guidelines is divided into two parts. Part 1, which follows below, outlines the guidance for use of CGM in hospital. The second part outlines guidance for use of CSII and hybrid closed-loop in hospital.
Misra S, Wagner R, Ozkan B, et al., 2023, Systematic review of precision subclassification of type 2 diabetes., medRxiv
Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.
Murphy R, Colclough K, Pollin TI, et al., 2023, A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes., medRxiv
UNLABELLED: Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes. PLAN LANGUAGE SUMMARY: Since monogenic diabetes misclassification can occur and lead to missed opportunities for optimal management, and several diagnostic technologies are available, we systematically review the yield of monogenic diabetes using different criteria to select people with diabetes for genetic testing and the technologies used.
Lumb A, Misra S, Rayman G, et al., 2023, Variation in the Current Use of Technology to Support Diabetes Management in UK Hospitals: Results of a Survey of Health Care Professionals, JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, ISSN: 1932-2968
Misra S, Avari P, Lumb A, et al., 2023, How Can Point-of-Care Technologies Support In-Hospital Diabetes Care?, JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, Vol: 17, Pages: 509-516, ISSN: 1932-2968
Flanagan D, Avari P, Choudhary P, et al., 2023, Using Technology to Improve Diabetes Care in Hospital: The Challenge and the Opportunity, JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, Vol: 17, Pages: 503-508, ISSN: 1932-2968
Chan J, Blane D, Choudhary P, et al., 2022, Addressing health inequalities in diabetes through research: Recommendations from Diabetes UK's 2022 health inequalities in diabetes workshop, DIABETIC MEDICINE, ISSN: 0742-3071
Misra S, DiMeglio LA, 2022, COVID-19 and Incident Type 1 Diabetes: Deciphering the Associations, DIABETES, Vol: 71, Pages: 2480-2482, ISSN: 0012-1797
Khunti K, Valabhji J, Misra S, 2022, Diabetes and the COVID-19 pandemic, DIABETOLOGIA, Vol: 66, Pages: 255-266, ISSN: 0012-186X
Thomas NJ, Walkey HC, Kaur A, et al., 2022, The relationship between islet autoantibody status and the genetic risk of type 1 diabetes in adult-onset type 1 diabetes, Diabetologia, Vol: 66, Pages: 310-320, ISSN: 0012-186X
Aims/hypothesisThe reason for the observed lower rate of islet autoantibody positivity in clinician-diagnosed adult-onset vs childhood-onset type 1 diabetes is not known. We aimed to explore this by assessing the genetic risk of type 1 diabetes in autoantibody-negative and -positive children and adults.MethodsWe analysed GAD autoantibodies, insulinoma-2 antigen autoantibodies and zinc transporter-8 autoantibodies (ZnT8A) and measured type 1 diabetes genetic risk by genotyping 30 type 1 diabetes-associated variants at diagnosis in 1814 individuals with clinician-diagnosed type 1 diabetes (1112 adult-onset, 702 childhood-onset). We compared the overall type 1 diabetes genetic risk score (T1DGRS) and non-HLA and HLA (DR3-DQ2, DR4-DQ8 and DR15-DQ6) components with autoantibody status in those with adult-onset and childhood-onset diabetes. We also measured the T1DGRS in 1924 individuals with type 2 diabetes from the Wellcome Trust Case Control Consortium to represent non-autoimmune diabetes control participants.ResultsThe T1DGRS was similar in autoantibody-negative and autoantibody-positive clinician-diagnosed childhood-onset type 1 diabetes (mean [SD] 0.274 [0.034] vs 0.277 [0.026], p=0.4). In contrast, the T1DGRS in autoantibody-negative adult-onset type 1 diabetes was lower than that in autoantibody-positive adult-onset type 1 diabetes (mean [SD] 0.243 [0.036] vs 0.271 [0.026], p<0.0001) but higher than that in type 2 diabetes (mean [SD] 0.229 [0.034], p<0.0001). Autoantibody-negative adults were more likely to have the more protective HLA DR15-DQ6 genotype (15% vs 3%, p<0.0001), were less likely to have the high-risk HLA DR3-DQ2/DR4-DQ8 genotype (6% vs 19%, p<0.0001) and had a lower non-HLA T1DGRS (p<0.0001) than autoantibody-positive adults. In contrast to children, autoantibody-negative adults were more likely to be male (75% vs 59%), had a higher BMI (27 vs 24 kg/m2) and were less likely to have other autoimmune conditions (2% vs 10%) than autoantib
Misra S, 2022, Rise in diabetic ketoacidosis during the COVID-19 pandemic: several questions remain., Lancet Diabetes Endocrinol, Vol: 10, Pages: 763-765
Misra S, Holman N, Barron E, et al., 2022, Characteristics and care of young people with type 2 diabetes included in the national diabetes audit datasets for England, DIABETIC MEDICINE, Vol: 40, ISSN: 0742-3071
Grace SL, Bowden J, Walkey HC, et al., 2022, Islet autoantibody level distribution in Type 1 diabetes and their association with genetic and clinical characteristics, Journal of Clinical Endocrinology and Metabolism, Vol: 107, Pages: E4341-E4349, ISSN: 0021-972X
ContextThe importance of the autoantibody level at diagnosis of type 1 diabetes (T1D) is not clear.ObjectiveWe aimed to assess the association of glutamate decarboxylase (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A) autoantibody levels with clinical and genetic characteristics at diagnosis of T1D.MethodsWe conducted a prospective, cross-sectional study. GADA, IA-2A, and ZnT8A were measured in 1644 individuals with T1D at diagnosis using radiobinding assays. Associations between autoantibody levels and the clinical and genetic characteristics for individuals were assessed in those positive for these autoantibodies. We performed replication in an independent cohort of 449 people with T1D.ResultsGADA and IA-2A levels exhibited a bimodal distribution at diagnosis. High GADA level was associated with older age at diagnosis (median 27 years vs 19 years, P = 9 × 10−17), female sex (52% vs 37%, P = 1 × 10−8), other autoimmune diseases (13% vs 6%, P = 3 × 10−6), and HLA-DR3-DQ2 (58% vs 51%, P = .006). High IA-2A level was associated with younger age of diagnosis (median 17 years vs 23 years, P = 3 × 10−7), HLA-DR4-DQ8 (66% vs 50%, P = 1 × 10−6), and ZnT8A positivity (77% vs 52%, P = 1 × 10−15). We replicated our findings in an independent cohort of 449 people with T1D where autoantibodies were measured using enzyme-linked immunosorbent assays.ConclusionIslet autoantibody levels provide additional information over positivity in T1D at diagnosis. Bimodality of GADA and IA-2A autoantibody levels highlights the novel aspect of heterogeneity of T1D. This may have implications for T1D prediction, treatment, and pathogenesis.
Misra S, Gable D, Khunti K, et al., 2022, Developing services to support the delivery of care to people with early-onset type 2 diabetes, DIABETIC MEDICINE, Vol: 39, ISSN: 0742-3071
Misra S, Florez JC, 2022, Extending precision medicine tools to populations at high risk of type 2 diabetes, PLOS MEDICINE, Vol: 19, ISSN: 1549-1277
Eng PC, Distaso W, Durreshahwar H, et al., 2022, The benefit of dexamethasone in patients with COVID-19 infection is preserved in patients with diabetes., Diabetes, Obesity and Metabolism: a journal of pharmacology and therapeutics, Vol: 24, Pages: 1385-1389, ISSN: 1462-8902
Dexamethasone significantly reduces mortality1 and is now standard treatment for patients with COVID-19 who require supplemental oxygen and/or mechanical ventilation. However, supraphysiological doses of glucocorticoids may exacerbate dysglycaemia and precipitate hyperglycaemic complications, particularly in those with or at risk of Type 2 diabetes2. The RECOVERY trial1 reported a low incidence of hyperglycaemic complications (2/1996, 0.1%), although the real-world incidence is likely to be much higher3. Type 2 diabetes itself increases the risk of severe COVID-194, and hyperglycaemia independently predicts poor outcomes5. We investigated the possibility that patients with diabetes may derive less survival benefit from steroid therapy in the setting of severe COVID-19 infection
Thomas NJ, Dennis JM, Sharp SA, et al., 2021, DR15-DQ6 remains dominantly protective against type 1 diabetes throughout the first five decades of life (vol 64, pg 2258, 2021), DIABETOLOGIA, Vol: 65, Pages: 258-258, ISSN: 0012-186X
Misra S, Barron E, Vamos E, et al., 2021, Temporal trends in emergency admissions for diabetic ketoacidosis in people with diabetes in England before and during the COVID-19 pandemic: a population-based study, The Lancet Diabetes and Endocrinology, ISSN: 2213-8595
BACKGROUND: Diabetic ketoacidosis (DKA) has been reported to be increasing in frequency during the COVID-19 pandemic. We aimed to examine the rates of DKA hospital admissions and the patient demographics associated with DKA during the pandemic compared with in prepandemic years. METHODS: Using a comprehensive, multiethnic, national dataset, the Secondary Uses Service repository, we extracted all emergency hospital admissions in England coded with DKA from March 1 to June 30, 2020 (first wave of the pandemic), July 1 to Oct 31, 2020 (post-first wave), and Nov 1, 2020, to Feb 28, 2021 (second wave), and compared these with DKA admissions in the equivalent periods in 2017-20. We also examined baseline characteristics, mortality, and trends in patients who were admitted with DKA. FINDINGS: There were 8553 admissions coded with DKA during the first wave, 8729 during the post-first wave, and 10 235 during the second wave. Compared with preceding years, DKA admissions were 6% (95% CI 4-9; p<0·0001) higher in the first wave of the pandemic (from n=8048), 6% (3-8; p<0·0001) higher in the post-first wave (from n=8260), and 7% (4-9; p<0·0001) higher in the second wave (from n=9610). In the first wave, DKA admissions reduced by 19% (95% CI 16-21) in those with pre-existing type 1 diabetes (from n=4965 to n=4041), increased by 41% (35-47) in those with pre-existing type 2 diabetes (from n=2010 to n=2831), and increased by 57% (48-66) in those with newly diagnosed diabetes (from n=1072 to n=1681). Compared with prepandemic, type 2 diabetes DKA admissions were similarly common in older individuals and men but were higher in those of non-White ethnicities during the first wave. The increase in newly diagnosed DKA admissions occurred across all age groups and these were significantly increased in men and people of non-White ethnicities. In the post-first wave, DKA admissions did not return to the baseline level of previous years; DKA admissions w
Thomas NJ, Dennis JM, Sharp SA, et al., 2021, DR15-DQ6 remains dominantly protective against type 1 diabetes throughout the first five decades of life, Diabetologia, Vol: 64, Pages: 2258-2265, ISSN: 0012-186X
Aims/hypothesisAmong white European children developing type 1 diabetes, the otherwise common HLA haplotype DR15-DQ6 is rare, and highly protective. Adult-onset type 1 diabetes is now known to represent more overall cases than childhood onset, but it is not known whether DR15-DQ6 is protective in older-adult-onset type 1 diabetes. We sought to quantify DR15-DQ6 protection against type 1 diabetes as age of onset increased.MethodsIn two independent cohorts we assessed the proportion of type 1 diabetes cases presenting through the first 50 years of life with DR15-DQ6, compared with population controls. In the After Diabetes Diagnosis Research Support System-2 (ADDRESS-2) cohort (n = 1458) clinician-diagnosed type 1 diabetes was confirmed by positivity for one or more islet-specific autoantibodies. In UK Biobank (n = 2502), we estimated type 1 diabetes incidence rates relative to baseline HLA risk for each HLA group using Poisson regression. Analyses were restricted to white Europeans and were performed in three groups according to age at type 1 diabetes onset: 0–18 years, 19–30 years and 31–50 years.ResultsDR15-DQ6 was protective against type 1 diabetes through to age 50 years (OR < 1 for each age group, all p < 0.001). The following ORs for type 1 diabetes, relative to a neutral HLA genotype, were observed in ADDRESS-2: age 5–18 years OR 0.16 (95% CI 0.08, 0.31); age 19–30 years OR 0.10 (0.04, 0.23); and age 31–50 years OR 0.37 (0.21, 0.68). DR15-DQ6 also remained highly protective at all ages in UK Biobank. Without DR15-DQ6, the presence of major type 1 diabetes high-risk haplotype (either DR3-DQ2 or DR4-DQ8) was associated with increased risk of type 1 diabetes.Conclusions/interpretationHLA DR15-DQ6 confers dominant protection from type 1 diabetes across the first five decades of life.
Ansari S, Abdel-Malek M, Kenkre J, et al., 2021, The use of whole blood capillary samples to measure 15 analytes for a home-collect biochemistry service during the SARS-CoV-2 pandemic: A proposed model from North West London Pathology, ANNALS OF CLINICAL BIOCHEMISTRY, Vol: 58, Pages: 411-421, ISSN: 0004-5632
Takis PG, Jiménez B, Al-Saffar NMS, et al., 2021, A computationally lightweight algorithm for deriving reliable metabolite panel measurements from 1D 1H NMR., Analytical Chemistry, Vol: 93, Pages: 4995-5000, ISSN: 0003-2700
Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.
Thomas NJ, Walkey HC, Kaur A, et al., 2021, The absence of islet autoantibodies in clinically diagnosed older-adult onset type 1 diabetes suggests an alternative pathology, advocating for routine testing in this age group
<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Islet autoantibodies at diagnosis are not well studied in older-adult onset (>30years) type 1 diabetes due to difficulties of accurate diagnosis. We used a type 1 diabetes genetic risk score (T1DGRS) to identify type 1 diabetes aiming to evaluate the prevalence and pattern of autoantibodies in older-adult onset type 1 diabetes.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We used a 30 variant T1DGRS in 1866 white-European individuals to genetically confirm a clinical diagnosis of new onset type 1 diabetes. We then assessed the prevalence and pattern of GADA, IA2A and ZnT8A within genetically consistent type 1 diabetes across three age groups (<18years (n=702), 18-30years (n=524) and >30years (n=588)).</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>In autoantibody positive cases T1DGRS was consistent with 100% type 1 diabetes in each age group. Conversely in autoantibody negative cases, T1DGRS was consistent with 93%(56/60) of <18years, 55%(37/67) of 18-30years and just 23%(34/151) of >30years having type 1 diabetes. Restricting analysis to genetically consistent type 1 diabetes showed similar proportions of positive autoantibodies across age groups (92% <18years, 92% 18-30years, 93% >30years)[p=0.87]. GADA was the most common autoantibody in older-adult onset type 1 diabetes, identifying 95% of autoantibody positive cases versus 72% in those <18years.</jats:p></jats:sec><jats:sec><jats:title>Interpretation</jats:title><jats:p>Older adult-onset type 1 diabetes has identical rates but different patterns of positive autoantibodies to childhood onset. In clinically suspected type 1 diabetes in older-adults, absence of autoantibodies strongly su
Misra S, Khozoee B, Huang-Jiawei P, et al., 2021, Comparison of diabetic ketoacidosis in adults, during the SARS-CoV-2 outbreak and over the same time period for the 3 preceding years, Diabetes Care, Vol: 44, Pages: e29-e31, ISSN: 0149-5992
Izzi-Engbeaya C, Distaso W, Amin A, et al., 2021, Adverse outcomes in COVID-19 and diabetes – a retrospective cohort study from three London Teaching hospitals, BMJ Open Diabetes Research and Care, Vol: 9, Pages: 1-10, ISSN: 2052-4897
INTRODUCTION: Patients with diabetes mellitus admitted to hospital with COVID-19 have poorer outcomes. However, the drivers for this are not fully elucidated. We performed detailed characterisation of COVID-19 patients to determine clinical and biochemical factors that may be the drivers of poorer outcomes. RESEARCH DESIGN AND METHODS: Retrospective cohort study of 889 consecutive inpatients diagnosed with COVID-19 between 9th March 2020 and 22nd April 2020 in a large London NHS Trust. Unbiased multivariate logistic regression analysis was performed to determine variables that were independently and significantly associated with increased risk of death and/or ICU admission within 30 days of COVID-19 diagnosis. RESULTS: 62% of patients in our cohort were of non-White ethnic backgrounds and the diabetes prevalence was 38%. 323 (36%) patients met the primary outcome of death/admission to the intensive care unit (ICU) within 30 days of COVID-19 diagnosis. Male gender, lower platelet count, advancing age and higher Clinical Frailty Scale (CFS) score (but not diabetes) independently predicted poor outcomes on multivariate analysis. Antiplatelet medication was associated with a lower risk of death/ICU admission. Factors that were significantly and independently associated with poorer outcomes in patients with diabetes were co-existing ischaemic heart disease, increasing age and lower platelet count. CONCLUSIONS: In this large study of a diverse patient population, comorbidity (i.e. diabetes with ischaemic heart disease; increasing CFS score in older patients) were major determinants of poor outcomes with COVID-19. Antiplatelet medication should be evaluated in randomised clinical trials amongst high-risk patient groups.
Muniangi-Muhitu H, Akalestou E, Salem V, et al., 2020, Covid-19 and diabetes: a complex bidirectional relationship, Frontiers in Endocrinology, Vol: 11, ISSN: 1664-2392
Covid-19 is a recently-emerged infectious disease caused by the novel severe acute respiratory syndrome coronavirus SARS-CoV2. SARS-CoV2 differs from previous coronavirus infections (SARS and MERS) due to its high infectivity (reproduction value, R0, typically 2-4) and pre- or asymptomatic transmission, properties that have contributed to the current global Covid-19 pandemic. Identified risk factors for disease severity and death from SARS-Cov2 infection include older age, male sex, diabetes, obesity and hypertension. The reasons for these associations are still largely obscure. Evidence is also emerging that SARS-CoV2 infection exacerbates the underlying pathophysiology of hyperglycemia in people with diabetes. Here, we discuss potential mechanisms through which diabetes may affect the risk of more severe outcomes in Covid-19 and, additionally, how diabetic emergencies and longer term pathology may be aggravated by infection with the virus. We consider roles for the immune system, the observed phenomenon of microangiopathy in severe Covid-19 infection and the potential for direct viral toxicity on metabolically-relevant tissues including pancreatic beta cells and targets of insulin action.
Misra S, Hassanali N, Bennett AJ, et al., 2020, Response to comment on Misra et al. homozygous hypomorphic HNF1A alleles are a novel cause of young-onset diabetes and result in sulfonylurea-sensitive diabetes. diabetes care 2020;43:909-912, Diabetes Care, Vol: 43, Pages: E155-E156, ISSN: 0149-5992
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