Diabetes and endocrinology
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Research outputs from the Diabetes/Endocrine service department at the RD&E.
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Item Type 1 diabetes and frailty: A scoping review(Wiley, 2024-05-01) Golding, J. A.; Yong, E. S. T.; Hope, S. V.; Wright, J. E.; Levett, T. J.; Chakera, A. J.AIMS: Advances in type 1 diabetes management are enabling more to reach older ages. Frailty is known to complicate type 2 diabetes. However, frailty in people with type 1 diabetes has not been extensively researched. This review summarises the available evidence on frailty in those with type 1 diabetes. METHODS: A systematic search strategy was applied to multiple databases (Medline, Embase, CINAHL and Cochrane) including grey literature (Scopus, OAIster, OpenGrey, dissertation and thesis database). All evidence types were considered. English articles published after 2001 were eligible. For inclusion, participants must have been over 55 with type 1 diabetes. Frailty must have been clearly defined or assessed. The results were synthesised into a descriptive format to identify key themes. RESULTS: Of 233 papers subject to full-text review, 23 were included. Older adult diabetes research frequently does not specify the type of diabetes; 100 articles were excluded for this reason. No articles were found specifically researching frailty in older adults with type 1 diabetes. Fourteen different definitions and nine assessments of frailty were outlined. Generally, the papers supported relaxation of glucose targets and greater adoption of diabetes technology. CONCLUSIONS: This review highlights the paucity of evidence in older adults with type 1 diabetes and frailty. Consensus on standardised definitions and assessments of frailty would aid future research, which is urgently needed as more people with type 1 diabetes reach older ages. Identifying and addressing the key issues in this population is vital to support individuals through the challenges of ageing.Item Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review(Nature Research, 2024-04-01) Felton, J. L.; Redondo, M. J.; Oram, R. A.; Speake, C.; Long, S. A.; Onengut-Gumuscu, S.; Rich, S. S.; Monaco, G. S. F.; Harris-Kawano, A.; Perez, D.; Saeed, Z.; Hoag, B.; Jain, R.; Evans-Molina, C.; DiMeglio, L. A.; Ismail, H. M.; Dabelea, D.; Johnson, R. K.; Urazbayeva, M.; Wentworth, J. M.; Griffin, K. J.; Sims, E. K.BACKGROUND: Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS: We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS: Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS: Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops. Islet autoantibodies are markers found in the blood when insulin-producing cells in the pancreas become damaged and can be used to predict future development of type 1 diabetes. We evaluated published literature to determine whether characteristics of islet antibodies (type, levels, numbers) could improve prediction and help understand differences in how individuals with type 1 diabetes respond to treatments. We found existing evidence shows that islet autoantibody type and number are most useful to predict disease progression before diagnosis. In addition, the age when islet autoantibodies first appear strongly influences rate of progression. These findings provide important information for patients and care providers on how islet autoantibodies can be used to understand future type 1 diabetes development and to identify individuals who have the potential to benefit from intervention or prevention therapy. eng MJH Life Sciences and as a consultant for DRI Healthcare. C.E.M. reported serving on advisory boards for Provention Bio, Isla Technologies, MaiCell Technologies, Avotres, DiogenyX, and Neurodon; receiving in-kind research support from Bristol Myers Squibb and Nimbus Pharmaceuticals; and receiving investigator initiated grants from Lilly Pharmaceuticals and Astellas Pharmaceuticals. L.A.D. reports research support to institution from Dompe, Lilly, Mannkind, Provention, Zealand and consulting relationships with Abata and Vertex. R.A.O. had a UK MRC Confidence in concept grant to develop a T1D GRS biochip with Randox Ltd, and has ongoing research funding from Randox R&D. No other authors report any relevant conflicts of interest.Item A practical evidence-based approach to management of type 2 diabetes in children and young people (CYP): UK consensus(Biomed Central, 2024-04-01) White, B.; Ng, S. M.; Agwu, J. C.; Barrett, T. G.; Birchmore, N.; Kershaw, M.; Drew, J.; Kavvoura, F.; Law, J.; Moudiotis, C.; Procter, E.; Paul, P.; Regan, F.; Reilly, P.; Sachdev, P.; Sakremath, R.; Semple, C.; Sharples, K.; Skae, M.; Timmis, A.; Williams, E.; Wright, N.; Soni, A.BACKGROUND: Type 2 diabetes in young people is an aggressive disease with a greater risk of complications leading to increased morbidity and mortality during the most productive years of life. Prevalence in the UK and globally is rising yet experience in managing this condition is limited. There are no consensus guidelines in the UK for the assessment and management of paediatric type 2 diabetes. METHODS: Multidisciplinary professionals from The Association of Children's Diabetes Clinicians (ACDC) and the National Type 2 Diabetes Working Group reviewed the evidence base and made recommendations using the Grading Of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. RESULTS AND DISCUSSION: Young people with type 2 diabetes should be managed within a paediatric diabetes team with close working with adult diabetes specialists, primary care and other paediatric specialties. Diagnosis of diabetes type can be challenging with many overlapping features. Diabetes antibodies may be needed to aid diagnosis. Co-morbidities and complications are frequently present at diagnosis and should be managed holistically. Lifestyle change and metformin are the mainstay of early treatment, with some needing additional basal insulin. GLP1 agonists should be used as second-line agents once early ketosis and symptoms are controlled. Glycaemic control improves microvascular but not cardiovascular risk. Reduction in excess adiposity, smoking prevention, increased physical activity and reduction of hypertension and dyslipidaemia are essential to reduce major adverse cardiovascular events. CONCLUSIONS: This evidence-based guideline aims to provide a practical approach in managing this condition in the UK.Item HormoneBayes: A novel Bayesian framework for the analysis of pulsatile hormone dynamics(Public Library of Science, 2024-12-29) Voliotis, M.; Abbara, A.; Prague, J. K.; Veldhuis, J. D.; Dhillo, W. S.; Tsaneva-Atanasova, K.The hypothalamus is the central regulator of reproductive hormone secretion. Pulsatile secretion of gonadotropin releasing hormone (GnRH) is fundamental to physiological stimulation of the pituitary gland to release luteinizing hormone (LH) and follicle stimulating hormone (FSH). Furthermore, GnRH pulsatility is altered in common reproductive disorders such as polycystic ovary syndrome (PCOS) and hypothalamic amenorrhea (HA). LH is measured routinely in clinical practice using an automated chemiluminescent immunoassay method and is the gold standard surrogate marker of GnRH. LH can be measured at frequent intervals (e.g., 10 minutely) to assess GnRH/LH pulsatility. However, this is rarely done in clinical practice because it is resource intensive, and there is no open-access, graphical interface software for computational analysis of the LH data available to clinicians. Here we present hormoneBayes, a novel open-access Bayesian framework that can be easily applied to reliably analyze serial LH measurements to assess LH pulsatility. The framework utilizes parsimonious models to simulate hypothalamic signals that drive LH dynamics, together with state-of-the-art (sequential) Monte-Carlo methods to infer key parameters and latent hypothalamic dynamics. We show that this method provides estimates for key pulse parameters including inter-pulse interval, secretion and clearance rates and identifies LH pulses in line with the widely used deconvolution method. We show that these parameters can distinguish LH pulsatility in different clinical contexts including in reproductive health and disease in men and women (e.g., healthy men, healthy women before and after menopause, women with HA or PCOS). A further advantage of hormoneBayes is that our mathematical approach provides a quantified estimation of uncertainty. Our framework will complement methods enabling real-time in-vivo hormone monitoring and therefore has the potential to assist translation of personalized, data-driven, clinical care of patients presenting with conditions of reproductive hormone dysfunction.Item Clinical features of type 1 diabetes in older adults and the impact of intermittently scanned continuous glucose monitoring: An Association of British Clinical Diabetologists (ABCD) study(Wiley, 2024-01-02) Deshmukh, H.; Adeleke, K.; Wilmot, E. G.; Folwell, A.; Barnes, D.; Walker, N.; Saunders, S.; Ssemmondo, E.; Walton, C.; Patmore, J.; Ryder, R. E. J.; Sathyapalan, T.AIMS: To evaluate the clinical features and impact of flash glucose monitoring in older adults with type 1 diabetes (T1D) across age groups defined as young-old, middle-old, and old-old. MATERIALS AND METHODS: Clinicians were invited to submit anonymized intermittently scanned continuous glucose monitoring (isCGM) user data to a secure web-based tool within the National Health Service secure network. We collected baseline data before isCGM initiation, such as demographics, glycated haemoglobin (HbA1c) values from the previous 12 months, Gold scores and Diabetes Distress Scale (DDS2) scores. For analysis, people with diabetes were classified as young-old (65-75 years), middle-old (>75-85 years) and old-old (>85 years). We compared baseline clinical characteristics across the age categories using a t test. All the analyses were performed in R 4.1.2. RESULTS: The study involved 1171 people with diabetes in the young-old group, 374 in the middle-old group, and 47 in the old-old group. There were no significant differences in baseline HbA1c and DDS2 scores among the young-old, middle-old, and old-old age groups. However, Gold score increased with age (3.20 [±1.91] in the young-old vs. 3.46 [±1.94] in the middle-old vs. 4.05 [±2.28] in the old-old group; p < 0.0001). This study showed reduced uptake of insulin pumps (p = 0.005) and structured education (Dose Adjustment For Normal Eating [DAFNE] course; p = 0.007) in the middle-old and old-old populations compared to the young-old population with T1D. With median isCGM use of 7 months, there was a significant improvement in HbA1c in the young-old (p < 0.001) and old-old groups, but not in the middle-old group. Diabetes-related distress score (measured by the DDS2) improved in all three age groups (p < 0.001) and Gold score improved (p < 0.001) in the young-old and old-old populations but not in the middle-old population. There was also a significant improvement in resource utilization across the three age categories following the use of is CGM. CONCLUSION: This study demonstrated significant differences in hypoglycaemia awareness and insulin pump use across the older age groups of adults with T1D. The implementation of isCGM demonstrated significant improvements in HbA1c, diabetes-related distress, hypoglycaemia unawareness, and resource utilization in older adults with T1D.