Aims. Polycystic ovarian syndrome (PCOS) is a common endocrine disorder characterised by ovarian morphological, systemic biochemical, and menstrual changes. Women with PCOS are at significantly increased risk of raised fasting glucose, impaired glucose tolerance, and diabetes. Recognition of these complications and early intervention are key to good health outcomes. We sought to identify DNA methylation patterns that may predict future diabetes onset in this high-risk PCOS population. Patients and Methods. Peripheral blood samples from women with PCOS and women with PCOS who later developed diabetes, were analysed by Illumina HumanMethylation450 BeadChip-arrays. Bisulphite-Pyrosequencing™ was used to validate and confirm array methylation data. Results. Array analyses identified 273 differentially methylated CpG loci (≥0.2 β-value change) at initial diagnosis of PCOS, between women who did or did not later develop diabetes. 19 of these sites demonstrated differential methylation in the same direction in ≥five sample pairs. Methylation in three of the candidates (cg11897887, cg02819655, and cg25542007) showed the best concordance with corresponding array β-values, and, most clearly differentiated ‘cases’ from ‘controls’. Conclusions. We have identified novel methylation biomarkers that could predict future onset of diabetes in this high-risk population. Use of methylation analyses to identify women who are likely to develop diabetes at diagnosis of PCOS may facilitate timely lifestyle interventions to reduce future morbidity.

Introduction: Women with gestational diabetes (GDM) are at greatly increased risk of type 2 diabetes (T2DM). The UK guidance recommends screening for T2DM at around 6 weeks post-partum and annually thereafter. We evaluated conformity to this guidance in two separate time periods. Methods: The proportion of tests performed within guidance was assessed using longitudinal plasma glucose and glycated haemoglobin data in two cohorts (1999-2007, n=251; 2015-2016, n=260) from hospital records on women previously diagnosed with GDM. Results: In the 1999-2007 and 2015-2016 cohorts, 59.8% and 35.0% of women had the recommended post-partum testing, respectively (p<0.001); just 13.5% and 14.2%, respectively, underwent the first annual test on time. During long-term follow-up of the 1999-2007 cohort (median follow-up: 12.3 years), the proportion of women tested in any given year averaged 34.2% over a 17-year period; there was a progressive decline in the proportion of women receiving a yearly test with time since delivery (p=0.002). Over the follow-up period, 85 women from the 1999-2007 cohort developed blood test results in the diabetic range with a median time to presumed DM diagnosis of 5.2 years (range 0.11-15.95 years). Kaplan-Meier analysis showed that 18.8% of women had blood test results in the diabetes range by 5 years and 37.8% by 10 years post-partum. Conclusions: Despite high profile guidelines and a clear clinical rationale to screen women with a past diagnosis of GDM, many women did not receive adequate screening for T2DM, both in the short- and long-term. This suggests alternative approaches are needed to ensure effective follow-up of this high-risk group. To have an impact, interventions need to be tailored to a young, generally healthy group in which traditional approaches to follow-up may not be best suited.

Our findings illustrate the widespread collateral impact of implementing measures to mitigate the impact of COVID-19 in people with, or being investigated for diabetes mellitus (DM). Ironically, failure to focus of the wider implications for people with DM and other groups with long-term conditions, may place them at increased risk of poor outcomes from SARS-CoV-2 infection itself, irrespective of the implications for their longer-term health prospects.

Introduction The rapid spread of the pandemic caused by the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)(COVID-19) virus resulted in governments around the world instigating a range of measures, including mandating the wearing of face coverings on public transport/in retail outlets. Methods We developed a sequential assessment of risk reduction provided by face coverings using a step-by-step approach. The United Kingdom Office of National Statistics(ONS) Population Survey data was utilised to determine the baseline total number of community-derived infections. These were linked to reported hospital admissions/hospital deaths to create case admission risk ratio/admission-related fatality rate. Results Overall, we show that only 7.3% of all community-based infection risk associates with public transport/retail outlets. The reported weekly community infection rate was 29,400 new cases at the start (24th July). The rate of growth in hospital admissions and deaths for England was around -15%/week, suggesting the infection rate, R, in the most vulnerable populations was just above 0.8. In this situation, average infections over the evaluated 13week follow-up period was 9,517/week. With face covering of 40% effectiveness, this reduced average infections by 844/week, hospital admissions by 8/week and deaths by 0.6/week; a fall of 9% over the period total. If, however, the R-value rises to 1.0, then average community infections would stay at 29,400/week and face coverings could reduce average weekly infections by 3,930, hospital admissions by 36 and deaths by 2.9/week; a 13% reduction. These reductions should be seen in the context of 102,000/week all-cause hospital emergency admissions in England and 8,900 reported deaths in the week ending 7thAugust 2020. Conclusion We have illustrated that the policy on mandation of face coverings in retail outlets/on public transport may have limited value in reducing hospital admissions/deaths. Impact appears small compared to all other sources of risk, thereby raising questions regarding effectiveness of the policy.