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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.

PurposeThe 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.ParticipantsWe 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 dateThere 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 plansThe 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.

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.

OBJECTIVE Studies using claims databases reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection >30 days earlier was associated with an increase in the incidence of type 1 diabetes. Using exact dates of diabetes diagnosis from the national register in Scotland linked to virology laboratory data, we sought to replicate this finding. RESEARCH DESIGN AND METHODS A cohort of 1,849,411 individuals aged RESULTS There were 365,080 individuals who had at least one detected SARS-CoV-2 infection during follow-up and 1,074 who developed type 1 diabetes. The rate ratio for incident type 1 diabetes associated with first positive test for SARS-CoV-2 (reference category: no previous infection) was 0.86 (95% CI 0.62, 1.21) for infection >30 days earlier and 2.62 (95% CI 1.81, 3.78) for infection in the previous 30 days. However, negative and positive SARS-CoV-2 tests were more frequent in the days surrounding diabetes presentation. In those aged 0–14 years, incidence of type 1 diabetes during 2020–2021 was 20% higher than the 7-year average. CONCLUSIONS Type 1 diabetes incidence in children increased during the pandemic. However, the cohort analysis suggests that SARS-CoV-2 infection itself was not the cause of this increase.

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. Graphical abstract

OBJECTIVES: To identify biomarkers of renal disease in adolescents with type 1 diabetes (T1D) and to compare findings in adults with T1D.METHODS: Twenty-five serum biomarkers were measured, using a Luminex platform, in 553 adolescents (median [interquartile range] age: 13.9 [12.6, 15.2] years), recruited to the Adolescent Type 1 Diabetes Cardio-Renal Intervention Trial. Associations with baseline and final estimated glomerular filtration rate (eGFR), rapid decliner and rapid increaser phenotypes (eGFR slopes 3 mL/min/1.73m2 /year, respectively), and albumin-creatinine ratio (ACR) were assessed. Results were also compared with those obtained in 859 adults (age: 55.5 [46.1, 64.4) years) from the Scottish Diabetes Research Network Type 1 Bioresource.RESULTS: In the adolescent cohort, baseline eGFR was negatively associated with trefoil factor-3, cystatin C, and beta-2 microglobulin (B2M) (B coefficient[95%CI]: -0.19 [-0.27, -0.12], P = 7.0 × 10-7 ; -0.18 [-0.26, -0.11], P = 5.1 × 10-6 ; -0.12 [-0.20, -0.05], P = 1.6 × 10-3 ), in addition to clinical covariates. Final eGFR was negatively associated with osteopontin (-0.21 [-0.28, -0.14], P = 2.3 × 10-8 ) and cystatin C (-0.16 [-0.22, -0.09], P = 1.6 × 10-6 ). Rapid decliner phenotype was associated with osteopontin (OR: 1.83 [1.42, 2.41], P = 7.3 × 10-6 ), whereas rapid increaser phenotype was associated with fibroblast growth factor-23 (FGF-23) (1.59 [1.23, 2.04], P = 2.6 × 10-4 ). ACR was not associated with any of the biomarkers. In the adult cohort similar associations with eGFR were found; however, several additional biomarkers were associated with eGFR and ACR.CONCLUSIONS: In this young population with T1D and high rates of hyperfiltration, osteopontin was the most consistent biomarker associated with prospective changes in eGFR. FGF-23 was associated with eGFR increases, whereas trefoil factor-3, cystatin C, and B2M were associated with baseline eGFR.

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.

At the outset of the COVID-19 epidemic in the UK, infectious disease modellers advised the government that unless a lockdown was imposed, most of the population would be infected within a few months and critical care capacity would be overwhelmed. This paper investigates the quantitative arguments underlying these predictions, and draws lessons for future policy.The modellers assumed that within age bands all individuals were equally susceptible and equally connected, leading to predictions that more than 80% of the population would be infected in the first wave of an unmitigated epidemic. Models that relax this unrealistic assumption to allow for selective removal of the most susceptible and connected individuals predict much smaller epidemic sizes. In most European countries no more than 10% of the population was infected in the first wave, irrespective of what restrictions were imposed. The modellers assumed that about 2% of those infected would require critical care, far higher than the proportion who entered critical care in the first wave, and failed to identify the key role of nosocomial transmission in overloading health systems. Model-based forecasts that only a lockdown could suppress the epidemic relied on a survey of contact rates in 2006, with no information on the types of contact most relevant to aerosol transmission or on heterogeneity of contact rates.In future epidemics, modellers should communicate the uncertainties associated with their assumptions and data, especially when these models are used to recommend policies that have high societal costs and are hard to reverse. Recognition of the gap between models and reality also implies a need to rebalance in favour of greater reliance on rapid studies of real-world transmission, robust model criticism, and acceptance that when measurements contradict model predictions it is the model that needs to be changed.

Objective Children are relatively protected from COVID-19, due to a range of potential mechanisms. We investigated if contact with children also affords adults a degree of protection from COVID-19. Design Cohort study based on linked administrative data. Setting Scotland. Study population All National Health Service Scotland healthcare workers and their household contacts as of March 2020. Main exposure Number of young children (0–11 years) living in the participant’s household. Main outcomes COVID-19 requiring hospitalisation, and any COVID-19 (any positive test for SARS-CoV-2) in adults aged ≥18 years between 1 March and 12 October 2020. Results 241 266, 41 198, 23 783 and 3850 adults shared a household with 0, 1, 2 and 3 or more young children, respectively. Over the study period, the risk of COVID-19 requiring hospitalisation was reduced progressively with increasing numbers of household children—fully adjusted HR (aHR) 0.93 per child (95% CI 0.79 to 1.10). The risk of any COVID-19 was similarly reduced, with the association being statistically significant (aHR per child 0.93; 95% CI 0.88 to 0.98). After schools reopened to all children in August 2020, no association was seen between exposure to young children and risk of any COVID-19 (aHR per child 1.03; 95% CI 0.92 to 1.14). Conclusion Between March and October 2020, living with young children was associated with an attenuated risk of any COVID-19 and COVID-19 requiring hospitalisation among adults living in healthcare worker households. There was no evidence that living with young children increased adults’ risk of COVID-19, including during the period after schools reopened., Many parents know that having young children in the house means they get frequent respiratory viral infections. Is this the same for COVID, which is less frequent and less severe in children? This cohort study finds that having young children in the house reduces the risk of COVID.

SummaryBackgroundThe objective of this study was to investigate how protection against COVID-19 conferred by previous infection is modified by vaccination.MethodsIn a cohort of all 152655 individuals in Scotland alive at 90 days after a positive test for SARS-CoV-2 (confirmed by cycle threshold < 30, or two tests) followed till 22 September 2021, rate ratios for reinfection were estimated with calendar time or tests as timescale.FindingsRates of detected and hospitalised reinfection with COVID-19 while unvaccinated were respectively 6.8 (95% CI 6.4 to 7.2) and 0.18 (95% CI 0.12 to 0.25) per 1000 person-months. These rates were respectively 68% and 74% lower than in a matched cohort of individuals who had not previously tested positive. Efficacy of two doses of vaccine in those with previous infection was estimated as as 84% (95 percent CI 81% to 86%) against detected reinfection and 71% (95 percent CI 29% to 88%) against hospitalised or fatal reinfection. The rate of detected reinfection after two doses of vaccine was 1.35 (95% CI 1.02 to 1.78) times higher in those vaccinated before first infection than in those unvaccinated at first infection.InterpretationThe combination of natural infection and vaccination provides maximal protection against new infection with SARS-CoV-2: prior vaccination does not impair this protection.FundingNo specific funding was received for this work.Research in contextEvidence before this studyIn a recent systematic review of cohort studies reported up to July 2021, the average reduction in COVID-19 infection rates in those with previous infection compared with those without evidence of previous infection was 90%. There is little information about the protective effect of previous infection against severe COVID-19, or about how the protective effects of previous infection against reinfection and severe disease are modified by vaccination.What this paper addsIn unvaccinated individuals the protection against hospitalised COVID-19 conferred by previous infection is similar to that induced by vaccination. In those with previous infection, vaccination reduces the rates of reinfection and hospitalised COVID-19 by about 70%.Implications of all the available evidenceThe combination of natural infection and vaccination provides maximal protection against COVID-19: prior vaccination does not seriously impair this protection.

Aims/hypothesis We assessed the real-world effect of flash monitor (FM) usage on HbA1c 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 HbA1c 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 HbA1c-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 ≥65 years; and 54.4% vs 36.2% in the least-deprived vs most-deprived quintile. Overall, the median (IQR) within-person change in HbA1c 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 HbA1c: −15.5 (−31.0, −4.0) mmol/mol (−1.4 [−2.8, −0.4]%) in those with HbA1c > 84 mmol/mol [9.8%] and 1.0 (−2.0, 5.5) mmol/mol (0.1 [−0.2, 0.5]%) in those with HbA1c 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 HbA1c at baseline was slight. No change in HbA1c 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 HbA1c 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. Graphical abstract

Aims/hypothesis The aim of this work was to examine whether glycaemic control has improved in those with type 1 diabetes in Scotland between 2004 and 2016, and whether any trends differed by sociodemographic factors. Methods We analysed records from 30,717 people with type 1 diabetes, registered anytime between 2004 and 2016 in the national diabetes database, which contained repeated measures of HbA1c. An additive mixed regression model was used to estimate calendar time and other effects on HbA1c. Results Overall, median (IQR) HbA1c decreased from 72 (21) mmol/mol [8.7 (4.1)%] in 2004 to 68 (21) mmol/mol (8.4 [4.1]%) in 2016. However, all of the improvement across the period occurred in the latter 4 years: the regression model showed that the only period of significant change in HbA1c was 2012–2016 where there was a fall of 3 (95% CI 1.82, 3.43) mmol/mol. The largest reductions in HbA1c in this period were seen in children, from 69 (16) mmol/mol (8.5 [3.6]%) to 63 (14) mmol/mol (7.9 [3.4]%), and adolescents, from 75 (25) mmol/mol (9.0 [4.4]%) to 70 (23) mmol/mol (8.6 [4.3]%). Socioeconomic status (according to Scottish Index of Multiple Deprivation) affected the HbA1c values: from the regression model, the 20% of people living in the most-deprived areas had HbA1c levels on average 8.0 (95% CI 7.4, 8.9) mmol/mol higher than those of the 20% of people living in the least-deprived areas. However this difference did not change significantly over time. From the regression model HbA1c was on average 1.7 (95% CI 1.6, 1.8) mmol/mol higher in women than in men. This sex difference did not narrow over time. Conclusions/interpretation In this high-income country, we identified a modest but important improvement in HbA1c since 2012 that was most marked in children and adolescents. These changes coincided with national initiatives to reduce HbA1c including an expansion of pump therapy. However, in most people, overall glycaemic control remains far from target levels and further improvement is badly needed, particularly in those from more-deprived areas. Electronic supplementary material The online version of this article (10.1007/s00125-019-4900-7) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

ObjectivesTo determine whether COVID-19 efficacy varies with clinical risk category and to investigate risk factors for severe COVID-19 in those who have received two doses of vaccine.DesignMatched case-control study (REACT-SCOT).SettingPopulation of Scotland from 1 December 2020 to 8 September 2021.Main outcome measureSevere COVID-19, defined as cases with entry to critical care or fatal outcome.ResultsEfficacy against severe COVID-19 of two doses of vaccine was 94% (95 percent CI 93% to 96%) in those without designated risk conditions, 89% (95 percent CI 86% to 91%) in those with moderate risk conditions, but only 73% (95 percent CI 64% to 79%) in those designated as clinically extremely vulnerable (CEV) and eligible for shielding. Of the 641 cases of severe COVID-19 in double-vaccinated individuals, 47% had moderate risk conditions and 38% were CEV. In the double-vaccinated CEV group, the rate ratio for severe disease (with no risk condition as reference category) was highest in solid organ transplants at 101 (95% CI 47 to 214) but even in this subgroup the absolute risk of severe COVID-19 was low (35 cases in 23678 person-months of follow-up).ConclusionsTwo doses of vaccine protect against severe COVID-19 in CEV individuals but the residual risk in double-vaccinated individuals remains far higher in those who are CEV than in those who are not. These results lay a basis for determining eligibility for additional measures including passive immunization to protect those at highest risk.

Objective To determine the risk of hospital admission with covid-19 and severe covid-19 among teachers and their household members, overall and compared with healthcare workers and adults of working age in the general population. Design Population based nested case-control study. Setting Scotland, March 2020 to July 2021, during defined periods of school closures and full openings in response to covid-19. Participants All cases of covid-19 in adults aged 21 to 65 (n=132 420) and a random sample of controls matched on age, sex, and general practice (n=1 306 566). Adults were identified as actively teaching in a Scottish school by the General Teaching Council for Scotland, and their household members were identified through the unique property reference number. The comparator groups were adults identified as healthcare workers in Scotland, their household members, and the remaining general population of working age. Main outcome measures The primary outcome was hospital admission with covid-19, defined as having a positive test result for SARS-CoV-2 during hospital admission, being admitted to hospital within 28 days of a positive test result, or receiving a diagnosis of covid-19 on discharge from hospital. Severe covid-19 was defined as being admitted to intensive care or dying within 28 days of a positive test result or assigned covid-19 as a cause of death. Results Most teachers were young (mean age 42), were women (80%), and had no comorbidities (84%). The risk (cumulative incidence) of hospital admission with covid-19 was Conclusion Compared with adults of working age who are otherwise similar, teachers and their household members were not found to be at increased risk of hospital admission with covid-19 and were found to be at lower risk of severe covid-19. These findings should reassure those who are engaged in face-to-face teaching.

Effect of Vaccination on Transmission of SARS-CoV-2 In this report from Scotland, vaccination of health care workers for SARS-CoV-2 was associated with a decrease in household transmission.

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.

BackgroundThe effect of vaccination for COVID-19 on onward transmission is unknown.MethodsA national record linkage study determined documented COVID-19 cases and hospitalisations in unvaccinated household members of vaccinated and unvaccinated healthcare workers from 8th December 2020 to 3rd March 2021. The primary endpoint was COVID-19 14 days following the first dose.ResultsThe cohort comprised of 194,362 household members (mean age 31·1 ± 20·9 years) and 144,525 healthcare workers (mean age 44·4 ± 11·4 years). 113,253 (78·3%) of healthcare workers received at least one dose of the BNT162b2 mRNA or ChAdOx1 nCoV-19 vaccine and 36,227 (25·1%) received a second dose. There were 3,123 and 4,343 documented COVID-19 cases and 175 and 177 COVID-19 hospitalisations in household members of healthcare workers and healthcare workers respectively. Household members of vaccinated healthcare workers had a lower risk of COVID-19 case compared to household members of unvaccinated healthcare worker (rate per 100 person-years 9·40 versus 5·93; HR 0·70, 95% confidence interval [CI] 0·63 to 0·78). The effect size for COVID-19 hospitalisation was similar, with the confidence interval crossing the null (HR 0·77 [95% CI 0·53 to 1·10]). The rate per 100 person years was lower in vaccinated compared to unvaccinated healthcare workers for documented (20·13 versus 8·51; HR 0·45 [95% CI 0·42 to 0·49]) and hospitalized COVID-19 (0·97 versus 0·14; HR 0·16 [95% CI 0·09 to 0·27]). Compared to the period before the first dose, the risk of documented COVID-19 case was lower at ≥ 14 days after the second dose for household members (HR 0·46 [95% CI 0·30to 0·70]) and healthcare workers (HR 0·08 [95% CI 0·04 to 0·17]).InterpretationVaccination of health care workers was associated with a substantial reduction in COVID-19 cases in household contacts consistent with an effect of vaccination on transmission.

SHu reports grants from Health Protection Scotland during the conduct of the study. TMC reports grants from Diabetes UK (18/0005786). CR reports grants from Public Health Scotland and UK Research and Innovation. 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

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.

ObjectiveTo assess the risk of hospital admission for coronavirus disease 2019 (covid-19) among patient facing and non-patient facing healthcare workers and their household members.DesignNationwide linkage cohort study.SettingScotland, UK, 1 March to 6 June 2020.ParticipantsHealthcare workers aged 18-65 years, their households, and other members of the general population.Main outcome measureAdmission to hospital with covid-19.ResultsThe cohort comprised 158 445 healthcare workers, most of them (90 733; 57.3%) being patient facing, and 229 905 household members. Of all hospital admissions for covid-19 in the working age population (18-65 year olds), 17.2% (360/2097) were in healthcare workers or their households. After adjustment for age, sex, ethnicity, socioeconomic deprivation, and comorbidity, the risk of admission due to covid-19 in non-patient facing healthcare workers and their households was similar to the risk in the general population (hazard ratio 0.81 (95% confidence interval 0.52 to 1.26) and 0.86 (0.49 to 1.51), respectively). In models adjusting for the same covariates, however, patient facing healthcare workers, compared with non-patient facing healthcare workers, were at higher risk (hazard ratio 3.30, 2.13 to 5.13), as were household members of patient facing healthcare workers (1.79, 1.10 to 2.91). After sub-division of patient facing healthcare workers into those who worked in “front door,” intensive care, and non-intensive care aerosol generating settings and other, those in front door roles were at higher risk (hazard ratio 2.09, 1.49 to 2.94). For most patient facing healthcare workers and their households, the estimated absolute risk of hospital admission with covid-19 was less than 0.5%, but it was 1% and above in older men with comorbidity.ConclusionsHealthcare workers and their households contributed a sixth of covid-19 cases admitted to hospital. Although the absolute risk of admission was low overall, patient facing healthcare workers and their household members had threefold and twofold increased risks of admission with covid-19.

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., Paul McKeigue and co-workers study comorbidities and medical treatments as risk factors for severe COVID-19 in Scotland., Author summary Why was this study done? Most people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) do not become seriously ill: risk of severe or fatal disease is associated with older age, male sex, and conditions designated by public health agencies, including asthma, diabetes, and heart disease. Studies reported so far have focused on these “listed conditions” but have not examined medical records systematically to identify possible risk factors for severe coronavirus disease 2019 (COVID-19). The objectives of this study were to identify risk factors for severe COVID-19 and to lay the basis for risk stratification based on electronic health records. What did the researchers do and find? Using Scotland’s capability for linking electronic health records, we report the first systematic study of the relationship of severe or fatal COVID-19 to preexisting health conditions and other risk factors. Residents in care homes were 21 times more likely to develop severe disease than people of the same age and sex not living in care homes. The conditions associated with increased risk include not only those already designated by public health agencies—asthma, diabetes, heart disease, disabling neurological disease, kidney disease—but other diagnoses that are associated with frailty and poor health such as strokes and a history of falls. In those without any listed conditions, use of prescribed drugs acting on the digestive system or nervous system is associated with increased risk of severe COVID-19. What do these findings mean? The risk to younger individuals without any recent history of hospital admission or use of prescription drugs is very low. This study lays a basis for calculating a risk score based on electronic health records for every individual in the population and using it to advise those at high risk of severe disease to shield themselves when there is a COVID-19 epidemic in their locality.

SummaryThe classic Susceptible-Infected-Recovered model formulated by Kermack and McKendrick [1] assumes that all individuals in the population are equally susceptible to infection. From fitting such a model to the trajectory of mortality from COVID-19 in 11 European countries up to 4 May 2020 Flaxman et al. concluded that “major non-pharmaceutical interventions – and lockdowns in particular – have had a large effect on reducing transmission” [2]. We show that relaxing the assumption of homogeneity to allow for individual variation in susceptibility or connectivity gives a model that has better fit to the data and more accurate 14-day forward prediction of mortality. Allowing for heterogeneity reduces the estimate of “counterfactual” deaths that would have occurred if there had been no interventions from 3.2 million to 262,000, implying that most of the slowing and reversal of COVID-19 mortality is explained by the build-up of herd immunity. The estimate of the herd immunity threshold depends on the value specified for the infection fatality ratio (IFR): a value of 0.3% for the IFR gives 15% for the average herd immunity threshold.

BackgroundChildren are relatively protected from novel coronavirus infection (COVID-19). The reasons for this protection are not well understood but differences in the immune response to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) have been implicated. If such differences are due to differential exposure to non-SARS-CoV-2 infectious agents, adults who are close contacts of children may partly share in this protection. Such a protective effect would have important implications for the lives of children, not least in terms of schooling.MethodsUsing a Scotland-wide record-linkage based occupational cohort comprising healthcare workers and members of their households, we examined whether sharing a household with young children (aged 0 to 11) attenuated the risk of hospitalisation with COVID-19, and/or testing positive for COVID-19 infection of any severity (any case of Covid-19). All healthcare workers directly employed by the National health Service (NHS) in Scotland, or contracted to provide general practice services, were included. Outcome and covariate data were obtained via linkage to Scotland-wide microbiology, drug prescribing, hospitalisation and death data.Results241,266 adults did not share a household with young children; 41,198, 23,783 and 3,850 shared a household with 1, 2 and 3 or more young children respectively. The risk of hospitalisation with COVID-19 was lower in those with one child and lower still in those with two or more children, adjusting for age the hazard ratio (HR) was 0.83 per child (95% CI 0.70-0.99). On additionally adjusting for sex, socioeconomic deprivation, occupation, professional role, staff/non-staff status, the number of adults and adolescents in each household, and comorbidity, the HR was 0.89 per child (95% CI 0.74-1.06). An association of the same magnitude, but more precisely estimated, was obtained for any case of COVID-19 (fully adjusted model, HR per child 0.89; 95% CI 0.84-0.95).ConclusionIncreased household exposure to young children was associated with an attenuated risk of testing positive for SARS-CoV-2 and appeared to also be associated with an attenuated risk of COVID-19 disease severe enough to require hospitalisation.

ObjectiveMany healthcare staff work in high-risk settings for contracting and transmitting Severe Acute Respiratory Syndrome Coronavirus 2. Their risk of hospitalisation for coronavirus disease 2019 (COVID-19), and that of their households, is poorly understood.Design and settings and participantsDuring the peak period for COVID-19 infection in Scotland (1st March 2020 to 6th June 2020) we conducted a national record linkage study to compare the risk of COVID-19 hospitalisation among healthcare workers (age: 18-65 years), their households and other members of the general population.Main outcomeHospitalisation with COVID-19ResultsThe cohort comprised 158,445 healthcare workers, the majority being patient facing (90,733 / 158,445; 57.3%), and 229,905 household members. Of all COVID-19 hospitalisations in the working age population (18-65-year-old), 17.2% (360 / 2,097) were in healthcare workers or their households. Adjusting for age, sex, ethnicity, socio-economic deprivation and comorbidity, the risk of COVID-19 hospitalisation in non-patient facing healthcare workers and their households was similar to the risk in the general population (hazards ratio [HR] 0.81; 95%CI 0.52-1.26 and 0.86; 95%CI 0.49-1.51 respectively). In models adjusting for the same covariates however, patient facing healthcare workers, compared to non-patient facing healthcare workers, were at higher risk (HR 3.30; 95%CI 2.13-5.13); so too were household members of patient facing healthcare workers (HR 1.79; 95%CI 1.10-2.91). On sub-dividing patient-facing healthcare workers into those who worked in front-door, intensive care and non-intensive care aerosol generating settings and other, those in ‘front door’ roles were at higher risk (HR 2.09; 95%CI 1.49-2.94). For most patient facing healthcare workers and their households, the estimated absolute risk of COVID-19 hospitalisation was less than 0.5% but was 1% and above in older men with comorbidity.ConclusionsHealthcare workers and their households contribute a sixth of hospitalised COVID-19 cases. Whilst the absolute risk of hospitalisation was low overall, patient facing healthcare workers and their households had 3- and 2-fold increased risks of COVID-19 hospitalisation.

ObjectivesIn this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response (change in erythrocyte sedimentation rate (ESR)) using 3158 European ancestry patients with rheumatoid arthritis.MethodsThe genetically regulated portion of gene expression was estimated in the full cohort of 3158 subjects (as well as within a subcohort consisting of 1575 UK patients) using the PrediXcan software package with three different reference panels. Estimated expression was tested for association with anti-TNF treatment response. As a replication/validation experiment, we also investigated the correlation between change in ESR with measured gene expression at the Interleukin 18 Receptor Accessory Protein (IL18RAP) gene in whole blood and synovial tissue, using an independent replication data set of patients receiving conventional synthetic disease modifying anti-rheumatic drugs, with directly measured (via RNA sequencing) gene expression.ResultsWe found that predicted expression of IL18RAP showed a consistent signal of association with treatment response across the reference panels. In our independent replication data set, IL18RAP expression in whole blood showed correlation with the change in ESR between baseline and follow-up (r=−0.35, p=0.0091). Change in ESR was also correlated with the expression of IL18RAP in synovial tissue (r=−0.28, p=0.02).ConclusionOur results suggest that IL18RAP expression is worthy of further investigation as a potential predictor of treatment response in rheumatoid arthritis that is not specific to a particular drug type.

ObjectivesTo investigate the relation of severe COVID-19 to prior drug prescribing.DesignMatched case-control study (REACT-SCOT) based on record linkage to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days.SettingScottish population.Main outcome measureSevere COVID-19, defined by entry to critical care or fatal outcome.ParticipantsAll 4272 cases of severe COVID-19 in Scotland since the start of the epidemic, with 36948 controls matched for age, sex and primary care practice.ResultsSevere COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in care homes, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.7, 13.2), and was not accounted for by treatment of conditions designated as conferring increased risk. 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. The largest effect was for antipsychotic agents: rate ratio 4.14 (3.39, 5.07). Other drug classes with large effects included proton pump inhibitors (rate rato 2.19 (1.70, 2.80) for >= 2 defined daily doses/day), opioids (3.62 (2.65, 4.94) 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.ConclusionsSevere 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. Although the evidence for causality is not conclusive, these results support existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy as a potential means of reducing COVID-19 risk.RegistrationENCEPP number EUPAS35558What is already known on this topicTwo previous studies have examined the relationship of severe COVID-19 to drugs for the cardiovascular system. This is the first systematic study of the relationship of severe COVID-19 to prior drug prescribing.What this study addsSevere 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. These results support earlier warnings that these drug classes that these drugs might increase susceptibility to COVID-19, and reinforce existing guidance on reducing overprescribing of these drug classes.

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 HbA1c levels, frequency of diabetic ketoacidosis (DKA) and severe hospitalised hypoglycaemia (SHH), overall and stratified by baseline HbA1c. 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 HbA1c 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 HbA1c 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 HbA1c, 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 HbA1c especially in those with high baseline HbA1c. 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. Graphical abstract

SummaryBackgroundStratifying the risk of colorectal cancer (CRC) based on polygenic risk scores (PRSs) within populations has the potential to optimize screening and develop targeted prevention strategies.MethodsA meta-analysis of eleven genome-wide association studies (GWAS), comprising 16 871 cases and 26 328 controls, was performed to capture CRC susceptibility variants. Genetic models with several candidate PRSs were generated from Scottish CRC case–control studies (6478 cases and 11 043 controls) for prediction of overall and site-specific CRC. Model performance was validated in UK Biobank (4800 cases and 20 287 controls). The 10-year absolute risk of CRC was estimated by modelling PRS with age and sex using the CRC incidence and mortality rates in the UK population.FindingsA weighted PRS including 116 CRC SNPs (wPRS116) showed the strongest performance. Deconstructing the PRS into multiple genetic risk regional scores or inclusion of additional SNPs that did not reach genome-wide significance did not provide any further improvement on predictive performance. The odds ratio (OR) for CRC risk per SD of wPRS116 in Scottish dataset was 1·46 (95%CI: 1·41-1·50, c-statistics: 0·603). Consistent estimates were observed in UK Biobank (OR=1·49, 95%CI: 1·44-1·54, c-statistics: 0·610) and showed no substantial heterogeneity among tumor sites. Compared to the middle quintile, those in the highest 1% of PRSs had 3·25-fold higher risk and those in the lowest 1% had 0·32-fold lower risk of developing CRC. Modelling PRS with age and sex in the general UK population allows the identification of a high-risk group with 10-year absolute risk ≥5%.InterpretationBy optimizing wPRS116, we show that genetic factors increase predictive performance but this increment is equivalent to the extraction of only one-tenth of the genetic susceptibility. When employing genetic risk profiling in population settings it provides a degree of risk discrimination that could, in principle, be integrated into population-based screening programs.

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.

SummaryAlthough population-wide lockdowns have been successful in slowing the COVID-19 epidemic, there is a consensus among disease modellers that keeping the load on critical care services within manageable limits will require an adaptive social distancing strategy, alternating cycles of relaxation and reimposition until a vaccine is available. An alternative strategy that has been tentatively proposed is to shield the elderly and others at high risk of severe disease, while allowing immunity to build up in those at low risk until the entire population is protected. We examine the performance required from a classifier that uses information from medical records to assign risk status for a such a stratify-and-shield policy to be effective in limiting mortality when social distancing is relaxed.We show that under plausible assumptions about the level of immunity required for population-level immunity, the proportion shielded is constrained to be no more than 15% of the population. Under varying assumptions about the infection fatality ratio (from 0.1% to 0.4%) and the performance of the classifier (3 to 4.5 bits of information for discrimination), we calculate the expected number of deaths in the unshielded group. We show that with likely values of the performance of a classifier that uses information from age, sex and medical records, at least 80% of those who would die if unshielded would be allocated to the high-risk shielded group comprising 15% of the population. Although the proportion of deaths that would be prevented by effective shielding does not vary much with the infection fatality ratio, the absolute number of deaths in the unshielded varies from less than 10,000 if the infection fatality rate is 0.1% to more than 50,000 if the infection fatality rate is as high as 0.4%.For projecting the effect of an optimally applied stratify-and-shield policy, studies now under way should help to resolve key uncertainties: the extent to which infection confers immunity, the prevalence of immunity, the infection fatality ratio, and the performance of a classifier constructed using information from medical records. It is time to give serious consideration to a stratify-and-shield policy that could bring the COVID-19 epidemic to an end in a matter of months while restoring economic activity, avoiding overload of critical care services and limiting mortality.

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10−7 for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.

Purpose To demonstrate a modelling approach that controls for time-invariant allocation bias in estimation of associations of outcome with drug exposure. Methods We show that in a model that includes terms for both ever-exposure versus never-exposure and cumulative exposure, the parameter for ever-exposure represents the effect of time-invariant allocation bias, and the parameter for cumulative exposure represents the effect of the drug after adjustment for this unmeasured confounding. This assumes no stepwise effect of the drug on the event rate, no reverse causation, and no unmeasured time-varying confounders. We demonstrated this by modelling the effect of statins on cardiovascular disease, for which the true effect has been well characterised in randomised trials, using time-updated Cox regression models in a national cohort of Type 2 diabetes patients. Results The crude hazard ratio associated with ever-use of statins was 1.13 in a standard cohort analysis comparing exposed with unexposed person-time intervals. When ever-never use and cumulative exposure are modelled jointly, the effect of statins can be estimated from the cumulative exposure parameter (hazard ratio 0.97 per year of exposure, 95% CI 0.97 to 0.98). The ever-exposed term (hazard ratio 1.20, 1.16 to 1.23) in this model can be interpreted as estimating the allocation bias. Conclusions Where stepwise effects on the risk of adverse events are unlikely, as for instance for effects on risk of cancer, joint modelling of ever-never and cumulative exposure can be used to study the effects of multiple drugs and to distinguish causal effects from confounding by allocation.

BackgroundExpression quantitative trait loci (eQTL) are genetic variants associated with gene expression. Using genome-wide genotype data, it is now possible to impute gene expression using eQTL mapping efforts. This approach can be used to analyse previously unexplored relationships between gene expression and heritable in vivo measures of human brain structural connectivity.MethodsUsing large-scale eQTL mapping studies, we computed 6,457 gene expression scores (eQTL scores) using genome-wide genotype data in UK Biobank, where each score represents a genetic proxy measure of gene expression. These scores were then tested for associations with two diffusion tensor imaging measures, fractional anisotropy (NFA=14,518) and mean diffusivity (NMD=14,485), representing white matter structural integrity.ResultsWe found FDR-corrected significant associations between 8 eQTL scores and structural connectivity phenotypes, including global and regional measures (βabsolute FA=0.0339-0.0453; MD=0.0308-0.0381) and individual tracts (βabsolute FA=0.0320-0.0561; MD=0.0295-0.0480). The loci within these eQTL scores have been reported to regulate expression of genes involved in various brain-related processes and disorders, such as neurite outgrowth and Parkinson’s disease (DCAKD, SLC35A4, SEC14L4, SRA1, NMT1, CPNE1, PLEKHM1, UBE3C).DiscussionOur findings indicate that eQTL scores are associated with measures of in vivo brain connectivity and provide novel information not previously found by conventional genome-wide association studies. Although the role of expression of these genes regarding white matter microstructural integrity is not yet clear, these findings suggest it may be possible, in future, to map potential trait- and disease-associated eQTL to in vivo brain connectivity and better understand the mechanisms of psychiatric disorders and brain traits, and their associated imaging findings.

Background: The risks of, and risk factors for, COVID-19 disease associated with diabetes are poorly quantified brbrMethods: We identified as cases all th

Background National guidelines in most countries set screening intervals for diabetic retinopathy (DR) that are insufficiently informed by contemporary incidence rates. This has unspecified implications for interval disease risks (IDs) of referable DR, disparities in ID between groups or individuals, time spent in referable state before screening (sojourn time), and workload. We explored the effect of various screening schedules on these outcomes and developed an open-access interactive policy tool informed by contemporary DR incidence rates. Methods and findings Scottish Diabetic Retinopathy Screening Programme data from 1 January 2007 to 31 December 2016 were linked to diabetes registry data. This yielded 128,606 screening examinations in people with type 1 diabetes (T1D) and 1,384,360 examinations in people with type 2 diabetes (T2D). Among those with T1D, 47% of those without and 44% of those with referable DR were female, mean diabetes duration was 21 and 23 years, respectively, and mean age was 26 and 24 years, respectively. Among those with T2D, 44% of those without and 42% of those with referable DR were female, mean diabetes duration was 9 and 14 years, respectively, and mean age was 58 and 52 years, respectively. Individual probability of developing referable DR was estimated using a generalised linear model and was used to calculate the intervals needed to achieve various IDs across prior grade strata, or at the individual level, and the resultant workload and sojourn time. The current policy in Scotland—screening people with no or mild disease annually and moderate disease every 6 months—yielded large differences in ID by prior grade (13.2%, 3.6%, and 0.6% annually for moderate, mild, and no prior DR strata, respectively, in T1D) and diabetes type (2.4% in T1D and 0.6% in T2D overall). Maintaining these overall risks but equalising risk across prior grade strata would require extremely short intervals in those with moderate DR (1–2 months) and very long intervals in those with no prior DR (35–47 months), with little change in workload or average sojourn time. Changing to intervals of 12, 9, and 3 months in T1D and to 24, 9, and 3 months in T2D for no, mild, and moderate DR strata, respectively, would substantially reduce disparity in ID across strata and between diabetes types whilst reducing workload by 26% and increasing sojourn time by 2.3 months. Including clinical risk factor data gave a small but significant increment in prediction of referable DR beyond grade (increase in C-statistic of 0.013 in T1D and 0.016 in T2D, both p < 0.001). However, using this model to derive personalised intervals did not have substantial workload or sojourn time benefits over stratum-specific intervals. The main limitation is that the results are pertinent only to countries that share broadly similar rates of retinal disease and risk factor distributions to Scotland. Conclusions Changing current policies could reduce disparities in ID and achieve substantial reductions in workload within the range of IDs likely to be deemed acceptable. Our tool should facilitate more rational policy setting for screening., Author summary Why was this study done? People with diabetes are advised to have their eyes photographed regularly to detect early retinal changes so that they can be referred for preventive treatments for diabetic retinopathy. In some countries, including Scotland, national health service programmes of regular screening have been set up. Deciding how often such screening should occur is a complex problem that depends on the individual risk of developing disease but also on system-level issues such as how much screening workload can be delivered, what level of disease progression between screens is deemed acceptable (interval disease risk), whether the system should be designed to ensure this risk is equal for all (equity), and the acceptable time for someone to be in a progressed state before detection (sojourn time). Our aim was to use contemporary data from the Scottish screening programme accrued over 10 years to enable policy makers and individuals to make better informed decisions about how often screening should occur and the design of screening programmes. Previous studies were much smaller, used data from earlier decades, when disease rates were higher, or only examined type 1 diabetes (T1D) or type 2 diabetes (T2D), not both. What did the researchers do and find? We took data on retinal photograph screenings from 128,606 screening examinations in people with T1D and 1,384,360 screenings in people with T2D with associated clinical risk factor data collected between 2007 and 2016. We quantified the current rates of disease occurrence and built a predictive model to allow those at more risk to be differentiated from those at lower risk. We used the current rate data and the risk model to quantify what the workload, sojourn time, and equity effects would be of different screening interval policies. We created an online tool to enable policy makers to evaluate the effects of different choices about screening intervals. The current system used in Scotland and elsewhere achieves low overall risks, but with large differences in risks between individuals. The prediction modelling showed that using lots of clinical risk factor data refines the prediction of progression of disease only slightly beyond using just diabetes type and the information contained in the last retinal photograph. What do these findings mean? We show how much the current system could reduce differences in risk by screening some people more often and others less often, and show that can be achieved at the same time as substantially reducing workload. The main limitation is that the results are pertinent only to countries that share broadly similar rates of retinal disease and risk factor distributions to Scotland, where the study was done.

Background The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. Methods and findings A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40–69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10−4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. Conclusion In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities., Evropi Theodoratou and colleagues investigate possible mechanisms linking urate with cardiovascular disease., Author summary Why was this study done? Serum urate level has been extensively studied in epidemiological studies in relation to various diseases, but the extent of any causal effect is still unclear, making it difficult to evaluate the clinical importance of urate. Mendelian randomization (MR) uses naturally occurring genetic variants as instruments to infer the causal role of a risk factor in a disease or outcome of interest. Previous MR studies were typically hypothesis driven, and few studies have comprehensively investigated how serum urate level might influence overall health. What did the researchers do and find? We implemented a phenome-wide association study (PheWAS) followed by a Bayesian analysis of tree-structured phenotypic model (TreeWAS) and MR analyses to explore the association between urate and a broad range of disease outcomes in the UK Biobank. We identified gout, hypertension, hypercholesterolemia, and a multitude of cardiovascular and cerebrovascular diseases (e.g., coronary atherosclerosis, myocardial infarction, angina pectoris, ischemic heart disease, heart failure, and cerebral infarction) that were associated with serum urate levels. MR analysis using various GWAS consortia data successfully replicated these associations but indicated the existence of genetic pleiotropy. Sensitivity analyses examining the pleiotropic effects of urate genetic risk loci on a set of metabolic traits support an inference that genetic pleiotropy contributes to observational association between urate and different types of cardiac events. What do these findings mean? There was little evidence of causality; instead, the pleiotropy of genetic variants on urate and metabolic traits is indicated for the observed associations with cardiovascular/metabolic diseases. The linked biological pathways between urate and metabolic traits indicated that the frequent coexistence of gout with hypertension, cardiovascular diseases (CVDs), and hyperlipidemia is a range of interrelated disease outcomes due to linked pathogenic components, rather than isolated events. These findings support the European League against Rheumatism (EULAR) recommendation of systematic screening and assessment of cardiovascular/metabolic comorbidities in gout patients. These findings may suggest new opportunities and challenges for developing drugs targeting a more distal mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular/metabolic comorbidities.

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

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 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.

ObjectivesWe sought to investigate whether genetic effects on response to TNF inhibitors (TNFi) in rheumatoid arthritis (RA) could be localised by considering known genetic susceptibility loci for relevant traits and to evaluate the usefulness of these genetic loci for stratifying drug response.MethodsWe studied the relation of TNFi response, quantified by change in swollen joint counts ( Δ SJC) and erythrocyte sedimentation rate ( Δ ESR) with locus-specific scores constructed from genome-wide assocation study summary statistics in 2938 genotyped individuals: 37 scores for RA; scores for 19 immune cell traits; scores for expression or methylation of 93 genes with previously reported associations between transcript level and drug response. Multivariate associations were evaluated in penalised regression models by cross-validation.ResultsWe detected a statistically significant association between Δ SJC and the RA score at the CD40 locus (p=0.0004) and an inverse association between Δ SJC and the score for expression of CD39 on CD4 T cells (p=0.00005). A previously reported association between CD39 expression on regulatory T cells and response to methotrexate was in the opposite direction. In stratified analysis by concomitant methotrexate treatment, the inverse association was stronger in the combination therapy group and dissipated in the TNFi monotherapy group. Overall, ability to predict TNFi response from genotypic scores was limited, with models explaining less than 1% of phenotypic variance.ConclusionsThe association with the CD39 trait is difficult to interpret because patients with RA are often prescribed TNFi after failing to respond to methotrexate. The CD39 and CD40 pathways could be relevant for targeting drug therapy.