A Longitudinal Clinical Trajectory Analysis Examining the Accumulation of Co-morbidity in People with Type 2 Diabetes (T2D) Compared with Non-T2D Individuals.

BACKGROUND: Type 2 diabetes mellitus (T2D) is commonly associated with an increasing complexity of multimorbidity. While some progress has been made in identifying genetic and non-genetic risk factors for T2D, understanding the longitudinal clinical history of individuals before/after T2D diagnosis may provide additional insights. METHODS: In this study, we utilised longitudinal data from the DARE (Diabetes Alliance for Research in England) study to examine the trajectory of clinical conditions in individuals with and without T2D. Data from 1932 individuals (T2D n = 1196 vs. matched non-T2D controls n = 736) were extracted and subjected to trajectory analysis over a period of up to 50 years (25 years pre-diagnosis/25 years post-diagnosis). We also analysed the cumulative proportion of people with diagnosed coronary artery disease (CAD) in their general practice (GP) record with an analysis of lower respiratory tract infection (RTI) as a comparator group. RESULTS: The mean age of diagnosis of T2D was 52.6 (95% confidence interval 52.0-53.4) years. In the years leading up to T2D diagnosis, individuals who eventually received a T2D diagnosis consistently exhibited a considerable increase in several clinical phenotypes. Additionally, immediately prior to T2D diagnosis, a significantly greater prevalence of hypertension (35%)/RTI (34%)/heart conditions (17%)/eye, nose, throat infection (19%) and asthma (12%) were observed. The corresponding trajectory of each of these conditions was much less dramatic in the matched controls. Post-T2D diagnosis, proportions of T2D individuals exhibiting hypertension/chronic kidney disease/retinopathy/infections climbed rapidly before plateauing. At the last follow-up by quintile of disadvantage, the proportion (%) of people with diagnosed CAD was 6.4% for quintile 1 (least disadvantaged) and 11% for quintile 5 (F = 3.4, p = 0.01 for the difference between quintiles). CONCLUSION: These findings provide novel insights into the onset/natural progression of T2D, suggesting an early phase of inflammation-related disease activity before any clinical diagnosis of T2D is made. Measures that reduce social inequality have the potential in the longer term to reduce the social gradient in health outcomes reported here.

Comparison of clinical features between patients with anti-synthetase syndrome and dermatomyositis: Results from the MYONET registry.

OBJECTIVES: To compare clinical characteristics, including the frequency of cutaneous, extramuscular manifestations, and malignancy, between adults with anti-synthetase syndrome (ASyS) and dermatomyositis (DM). METHODS: Using data regarding adults from the MYONET registry, a cohort of DM patients with anti-Mi2/-TIF1É£/-NXP2/-SAE/-MDA5 autoantibodies, and a cohort of ASyS patients with anti-tRNA synthetase autoantibodies (anti-Jo1/-PL7/-PL12/-OJ/-EJ/-Zo/-KS) were identified. Patients with DM sine dermatitis or with discordant dual autoantibody specificities were excluded. Sub-cohorts of patients with ASyS with or without skin involvement were defined based on presence of DM-type rashes (heliotrope rash, Gottron's papules/sign, violaceous rash, shawl sign, V sign, erythroderma, and/or periorbital rash). RESULTS: In total 1,054 patients were included (DM, n = 405; ASyS, n = 649). In ASyS cohort, 31% (n = 203) had DM-type skin involvement (ASyS-DMskin). A higher frequency of extramuscular manifestations, including Mechanic's hands, Raynaud's phenomenon, arthritis, interstitial lung disease, and cardiac involvement differentiated ASyS-DMskin from DM (all p< 0.001), whereas higher frequency of any of four DM-type rashes: heliotrope rash (n = 248, 61% vs n = 90, 44%), violaceous rash (n = 166, 41% vs n = 57, 9%), V sign (n = 124, 31% vs n = 28, 4%), and shawl sign (n = 133, 33% vs n = 18, 3%) differentiated DM from ASyS-DMskin (all p< 0.005). Cancer-associated myositis (CAM) was more frequent in DM (n = 67, 17%) compared with ASyS (n = 21, 3%) and ASyS-DMskin (n = 7, 3%) cohorts (both p< 0.001). CONCLUSION: DM-type rashes are frequent in patients with ASyS; however, distinct clinical manifestations differentiate these patients from classical DM. Skin involvement in ASyS does not necessitate increased malignancy surveillance. These findings will inform future ASyS classification criteria and patient management.

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England.

INTRODUCTION: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D. METHODS: This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England's Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression. RESULTS: Median age of the people living with T1D was 37 (interquartile range 25-52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m2, and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62-1.80]), age (OR 1.02, 95% CI 1.02-1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06-1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974-0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06-1.33/OR Asian 1.21, 95% CI 1.05-1.39) and having asthma (OR 1.27, 95% CI 1.19-1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89-2.32), severe mental illness (OR 1.83, 95% CI 1.57-2.12) or hypertension (OR 1.44, 95% CI 1.37-1.52). CONCLUSION: In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics.

A study to investigate genetic factors associated with weight gain in people with diabetes: analysis of polymorphisms in four relevant genes.

BACKGROUND: Weight change is often seen in people with diabetes. We investigated the effects of genes associated with weight change/glucose handling/insulin-signalling. MATERIALS/METHODS: DNA from diabetes individuals and non-diabetes individuals, plus clinical data, were available from the DARE study (n = 379 individuals: T1D n = 111; T2D n = 222; controls n = 46). Weight gain was assessed by temporal change of Body Mass Index (BMI). Genotyping was performed for CAV1rs926198, LEPRrs1137101, BDNFrs6265 and FTOrs9939609. RESULTS: No differences in genotype distributions were observed for the four SNPs in all groups un-stratified by weight gain. Following stratification differences in genotype distribution were observed. For those BMI relatively stable; controls showed a difference in genotype distributions versus T1D (CAV1rs926198, LEPRrs1137101). In T2D vs controls, significant differences were observed in genotype distribution for all four genes. For BMI increase, the only difference by category was LEPRrs1137101 (bothT1D/T2D vs controls). In BMI-stable groups, CAV1rs926198, T1D individuals showed lower T allele frequency (p=0.004) vs non-diabetes and for LEPRrs1137101 a higher G allele frequency versus controls (p=0.002). For T2D, CAV1rs926198, T allele frequency was lower in T2D than controls (p=0.005). For LEPR rs1137101, the G allele frequency was higher than in controls (p=0.004). In those with BMI increase, LEPRrs1137101 T1D individuals had higher G allele frequency versus controls (p=0.002) as did T2D vs controls (p=0.03). CONCLUSION: Differences in allele frequency were seen between diabetes individuals and non-diabetes diagnosed at baseline in relation to the likelihood of BMI increase of >10%. It is established that the G allele of LEPRrs1137101 is associated with weight gain/obesity. However, this is the first report of CAV1rs926198 polymorphism being associated with weight stability/gain in diabetes.

COVID-19 Vaccination and Diabetes Mellitus: How Much Has It Made a Difference to Outcomes Following Confirmed COVID-19 Infection?

INTRODUCTION: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus (COVID-19), its successive variants and the associated pandemic caused. We have previously shown that for people living with type 2 diabetes (T2DM), the risk of being admitted to hospital or dying following a COVID-19 infection progressively decreased through the first months of 2021. In this subsequent analysis we have examined how the UK COVID-19 vaccination programme impacted differentially on COVID-19 outcomes in people with T1DM or T2DM compared to appropriate controls. METHODS: T1DM and T2DM affected individuals were compared with their matched controls on 3:1 ratio basis. A 28-day hospital admission or mortality was used as the binary outcome variable with diabetes status and vaccination for COVID-19 as the main exposure variables. RESULTS: A higher proportion of T1DM individuals vs their controls was found to be vaccinated at the point of their first recorded positive COVID-19 test when compared to T2DM individuals vs their controls. Regarding the 28-day hospital admission rate, there was a greater and increasing protective effect of subsequent vaccination dosage (one, two or three) in mitigating the effects of COVID-19 infection versus no vaccination in T1DM than in T2DM individuals when compared with matched controls. Similar effects were observed in T2DM for death. Across both diabetes and non-diabetes individuals, those at greater socio-economic disadvantage were more likely to test positive for COVID-19 in the early phase of the pandemic. For T2DM individuals socio-economic disadvantage was associated with a greater likelihood of hospital admission and death, independent of vaccination status. Age and male sex were also independently associated with 28-day hospital admission in T2DM and to 28-day mortality, independent of vaccination status. African ethnicity was also an additional factor for hospital admission in people with T2DM. CONCLUSION: A beneficial effect of COVID-19 vaccination was seen in mitigating the harmful effects of COVID-19 infection; this was manifest in reduced hospital admission rate in T1DM individuals with a lesser effect in T2DM when compared with matched controls, regarding both hospital admission and mortality. Socio-economic disadvantage influenced likelihood of COVID-19 confirmed infection and the likelihood of hospital admission/death independent of the number of vaccinations given in T2DM.

Identification of Novel Associations and Localization of Signals in Idiopathic Inflammatory Myopathies Using Genome-Wide Imputation.

OBJECTIVE: The idiopathic inflammatory myopathies (IIMs) are heterogeneous diseases thought to be initiated by immune activation in genetically predisposed individuals. We imputed variants from the ImmunoChip array using a large reference panel to fine-map associations and identify novel associations in IIM. METHODS: We analyzed 2,565 Caucasian IIM patient samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically matched control samples. We imputed 1,648,116 variants from the ImmunoChip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM and clinical and serologic subgroups. RESULTS: The HLA locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, SDK2 and LINC00924 (both novel) and STAT4 in the whole IIM cohort, with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and for CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. CONCLUSION: We found novel and strong associations in IIM and PM and localized signals to single genes and immune cell types.

Low copy numbers of complement C4 and C4A deficiency are risk factors for myositis, its subgroups and autoantibodies.

BACKGROUND: Idiopathic inflammatory myopathies (IIM) are a group of autoimmune diseases characterised by myositis-related autoantibodies plus infiltration of leucocytes into muscles and/or the skin, leading to the destruction of blood vessels and muscle fibres, chronic weakness and fatigue. While complement-mediated destruction of capillary endothelia is implicated in paediatric and adult dermatomyositis, the complex diversity of complement C4 in IIM pathology was unknown. METHODS: We elucidated the gene copy number (GCN) variations of total C4, C4A and C4B, long and short genes in 1644 Caucasian patients with IIM, plus 3526 matched healthy controls using real-time PCR or Southern blot analyses. Plasma complement levels were determined by single radial immunodiffusion. RESULTS: The large study populations helped establish the distribution patterns of various C4 GCN groups. Low GCNs of C4T (C4T=2+3) and C4A deficiency (C4A=0+1) were strongly correlated with increased risk of IIM with OR equalled to 2.58 (2.28-2.91), p=5.0×10-53 for C4T, and 2.82 (2.48-3.21), p=7.0×10-57 for C4A deficiency. Contingency and regression analyses showed that among patients with C4A deficiency, the presence of HLA-DR3 became insignificant as a risk factor in IIM except for inclusion body myositis (IBM), by which 98.2% had HLA-DR3 with an OR of 11.02 (1.44-84.4). Intragroup analyses of patients with IIM for C4 protein levels and IIM-related autoantibodies showed that those with anti-Jo-1 or with anti-PM/Scl had significantly lower C4 plasma concentrations than those without these autoantibodies. CONCLUSIONS: C4A deficiency is relevant in dermatomyositis, HLA-DRB1*03 is important in IBM and both C4A deficiency and HLA-DRB1*03 contribute interactively to risk of polymyositis.

Mortality in People with Type 2 Diabetes Following SARS-CoV-2 Infection: A Population Level Analysis of Potential Risk Factors.

INTRODUCTION: Research is ongoing to increase our understanding of how much a previous diagnosis of type 2 diabetes mellitus (T2DM) affects someone's risk of becoming seriously unwell following a COVID-19 infection. In this study we set out to determine the relative likelihood of death following COVID-19 infection in people with T2DM when compared to those without T2DM. This was conducted as an urban population study and based in the UK. METHODS: Analysis of electronic health record data was performed relating to people living in the Greater Manchester conurbation (population 2.82 million) who had a recorded diagnosis of T2DM and subsequent COVID-19 confirmed infection. Each individual with T2DM (n = 13,807) was matched with three COVID-19-infected non-diabetes controls (n = 39,583). Data were extracted from the Greater Manchester Care Record (GMCR) database for the period 1 January 2020 to 30 June 2021. Social disadvantage was assessed through Townsend scores. Death rates were compared in people with T2DM to their respective non-diabetes controls; potential predictive factors influencing the relative likelihood of admission were ascertained using univariable and multivariable logistic regression. RESULTS: For individuals with T2DM, their mortality rate after a COVID-19 positive test was 7.7% vs 6.0% in matched controls; the relative risk (RR) of death was 1.28. From univariate analysis performed within the group of individuals with T2DM, the likelihood of death following a COVID-19 recorded infection was lower in people taking metformin, a sodium-glucose cotransporter 2 inhibitor (SGLT2i) or a glucagon-like peptide 1 (GLP-1) agonist. Estimated glomerular filtration rate (eGFR) and hypertension were associated with increased mortality and had odds ratios of 0.96 (95% confidence interval 0.96-0.97) and 1.92 (95% confidence interval 1.68-2.20), respectively. Likelihood of death following a COVID-19 infection was also higher in those people with a diagnosis of chronic obstructive pulmonary disease (COPD) or severe enduring mental illness but not with asthma, and in people taking aspirin/clopidogrel/insulin. Smoking in people with T2DM significantly increased mortality rate (odds ratio of 1.46; 95% confidence interval 1.29-1.65). In a combined analysis of patients with T2DM and controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of death (accounting for 26% of variance) were T2DM, age, male gender and social deprivation (higher Townsend score). CONCLUSION: Following confirmed infection with COVID-19 a number of factors are associated with mortality in individuals with T2DM. Prescription of metformin, SGLT2is or GLP-1 agonists and non-smoking status appeared to be associated with a reduced the risk of death for people with T2DM. Age, male sex and social disadvantage are associated with an increased risk of death.

The Risk Factors Potentially Influencing Hospital Admission in People with Diabetes, Following SARS-CoV-2 Infection: A Population-Level Analysis.

INTRODUCTION: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus and the associated global pandemic (Covid-19). People with diabetes are particularly at high risk of becoming seriously unwell after contracting this virus. METHODS: This population-based study included people living in the Greater Manchester conurbation who had a recorded diagnosis of type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and subsequent Covid-19 infection. Each individual with T1DM (n = 862) or T2DM (n = 13,225) was matched with three Covid-19-infected non-diabetes controls. RESULTS: For individuals with T1DM, hospital admission rate in the first 28 days after a positive Covid-19 test was 10% vs 4.7% in age/gender-matched controls [relative risk (RR) 2.1]. For individuals with T2DM, hospital admission rate after a positive Covid-19 test was 16.3% vs 11.6% in age/gender-matched controls (RR 1.4). The average Townsend score was higher in T2DM (1.8) vs matched controls (0.4), with a higher proportion of people with T2DM observed in the top two quintiles of greatest disadvantage (p < 0.001). For Covid-19-infected individuals with T1DM, factors influencing admission likelihood included age, body mass index (BMI), hypertension, HbA1c, low HDL-cholesterol, lower estimated glomerular filtration rate (eGFR), chronic obstructive pulmonary disease (COPD) and being of African/mixed ethnicity. In Covid-19-infected individuals with T2DM, factors related to a higher admission rate included age, Townsend index, comorbidity with COPD/asthma and severe mental illness (SMI), lower eGFR. Metformin prescription lowered the likelihood. For multivariate analysis in combined individuals with T2DM/controls, factors relating to higher likelihood of admission were having T2DM/age/male gender/diagnosed COPD/diagnosed hypertension/social deprivation (higher Townsend index) and non-white ethnicity (all groups). CONCLUSION: In a UK population we have confirmed a significantly higher likelihood of admission in people with diabetes following Covid-19 infection. A number of factors mediate that increased likelihood of hospital admission. For T2DM, the majority of factors related to increased admission rate are common to the general population but more prevalent in T2DM. There was a protective effect of metformin in people with T2DM.

Longitudinal clinical trajectory analysis of individuals before and after diagnosis of Type 2 Diabetes Mellitus (T2DM) indicates that vascular problems start early.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi-morbidity/treatment complexity. Some headway has been made to identify genetic and non-genetic risk factors for T2DM. However, longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi-morbidity. METHODS: This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre- and post-T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n = 237 vs matched non-T2DM controls n = 44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. RESULTS: For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis, a greater indication of ischaemic heart disease proportions was observed. Post-T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. CONCLUSION: These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores.

Analysis of continuous glucose tracking data in people with type 1 diabetes after COVID-19 vaccination reveals unexpected link between immune and metabolic response, augmented by adjunctive oral medication.

INTRODUCTION: The COVID-19 vaccination programme is under way worldwide. Anecdotal evidence is increasing that some people with type 1 diabetes mellitus (T1DM) experience temporary instability of blood glucose (BG) levels post-vaccination which normally settles within 2-3 days. We report an analysis of BG profiles of 20 individuals before/after vaccination. METHODS: We examined the BG profile of 20 consecutive adults (18 years of age or more) with T1DM using the FreeStyle Libre flash glucose monitor in the period immediately before and after COVID-19 vaccination. The primary outcome measure was percentage (%) BG readings in the designated target range 3.9-10 mmmol/L as reported on the LibreView portal for 7 days prior to the vaccination (week -1) and the 7 days after the vaccination (week +1). RESULTS: There was a significant decrease in the %BG on target following the COVID-vaccination for the 7 days following vaccination (mean 45.2% ± SE 4.2%) vs pre-COVID-19 vaccination (mean 52.6% ± SE 4.5%). This was mirrored by an increase in the proportion of readings in other BG categories 10.1%-13.9%/≥14%. There was no significant change in BG variability in the 7days post-COVID-19 vaccination. This change in BG proportion on target in the week following vaccination was most pronounced for people taking Metformin/Dapagliflozin+basal-bolus insulin (-23%) vs no oral hypoglycaemic agents (-4%), and median age <53 vs ≥53 years (greater reduction in %BG in target for older individuals (-18% vs -9%)). CONCLUSION: In T1DM, we have shown that COVID-19 vaccination can cause temporary perturbation of BG, with this effect more pronounced in patients talking oral hypoglycaemic medication plus insulin, and in older individuals. This may also have consequences for patients with T2DM who are currently not supported by flash glucose monitoring.

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Analysis of human total antibody repertoires in TIF1γ autoantibody positive dermatomyositis.

We investigate the accumulated microbial and autoantigen antibody repertoire in adult-onset dermatomyositis patients sero-positive for TIF1γ (TRIM33) autoantibodies. We use an untargeted high-throughput approach which combines immunoglobulin disease-specific epitope-enrichment and identification of microbial and human antigens. We observe antibodies recognizing a wider repertoire of microbial antigens in dermatomyositis. Antibodies recognizing viruses and Poxviridae family species are significantly enriched. The identified autoantibodies recognise a large portion of the human proteome, including interferon regulated proteins; these proteins cluster in specific biological processes. In addition to TRIM33, we identify autoantibodies against eleven further TRIM proteins, including TRIM21. Some of these TRIM proteins share epitope homology with specific viral species including poxviruses. Our data suggest antibody accumulation in dermatomyositis against an expanded diversity of microbial and human proteins and evidence of non-random targeting of specific signalling pathways. Our findings indicate that molecular mimicry and epitope spreading events may play a role in dermatomyositis pathogenesis.

Associations between human leukocyte antigens and renal function.

Human leukocyte antigens (HLA) have been associated with renal function, but previous studies report contradictory findings with little consensus on the exact nature or impact of this observation. This study included 401,307 white British subjects aged 39-73 when they were recruited by UK Biobank. Subjects' HLA types were imputed using HLA*IMP:02 software. Regression analysis was used to compare 362 imputed HLA types with estimated glomerular filtration rate (eGFR) as a primary outcome and clinical indications as secondary outcome measures. 22 imputed HLA types were associated with increased eGFR (and therefore increased renal function). Decreased eGFR (decreased renal function) was associated with 11 imputed HLA types, seven of which were also associated with increased risk of end-stage renal disease and/or chronic kidney disease. Many of these HLA types are commonly inherited together in established haplotypes, for example: HLA-A*01:01, B*08:01, C*07:01, DRB1*03:01, DQB1*02:01. This haplotype has a population frequency of 9.5% in England and each allele was associated with decreased renal function. 33 imputed HLA types were associated with kidney function in white British subjects. Linkage disequilibrium in HLA heritance suggests that this is not random and particularly affects carriers of established haplotypes. This could have important applications for the diagnosis and treatment of renal disease and global population health.

Genetically defined favourable adiposity is not associated with a clinically meaningful difference in clinical course in people with type 2 diabetes but does associate with a favourable metabolic profile.

AIMS: Change in weight, HbA1c , lipids, blood pressure and cardiometabolic events over time is variable in individuals with type 2 diabetes. We hypothesised that people with a genetic predisposition to a more favourable adiposity distribution could have a less severe clinical course/progression. METHODS: We involved people with type 2 diabetes from two UK-based cohorts: 11,914 individuals with GP follow-up data from the UK Biobank and 723 from Salford. We generated a 'favourable adiposity' genetic score and conducted cross-sectional and longitudinal studies to test its association with weight, BMI, lipids, blood pressure, medication use and risk of myocardial infarction and stroke using 15 follow-up time points with 1-year intervals. RESULTS: The 'favourable adiposity' genetic score was cross-sectionally associated with higher weight (effect size per 1 standard deviation higher genetic score: 0.91 kg [0.59,1.23]) and BMI (0.30 kg/m2 [0.19,0.40]), but higher high-density lipoprotein (0.02 mmol/L [0.01,0.02]) and lower triglycerides (-0.04 mmol/L [-0.07, -0.02]) in the UK Biobank at baseline, and this pattern of association was consistent across follow-up. There was a trend for participants with higher 'favourable adiposity' genetic score to have lower risk of myocardial infarction and/or stroke (odds ratio 0.79 [0.62, 1.00]) compared to those with lower score. A one standard deviation higher score was associated with lower odds of using lipid-lowering (0.91 [0.86, 0.97]) and anti-hypertensive medication (0.95 [0.91, 0.99]). CONCLUSIONS: In individuals with type 2 diabetes, having more 'favourable adiposity' alleles is associated with a marginally better lipid profile long-term and having lower odds of requiring lipid-lowering or anti-hypertensive medication in spite of relatively higher adiposity.