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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.

Aims/hypothesisThe aim of this study was to provide data from a contemporary population-representative cohort on rates and predictors of renal decline in type 1 diabetes.MethodsWe used data from a cohort of 5777 people with type 1 diabetes aged 16 and older, diagnosed before the age of 50, and representative of the adult population with type 1 diabetes in Scotland (Scottish Diabetes Research Network Type 1 Bioresource; SDRNT1BIO). We measured serum creatinine and urinary albumin/creatinine ratio (ACR) at recruitment and linked the data to the national electronic healthcare records.ResultsMedian age was 44.1 years and diabetes duration 20.9 years. The prevalence of CKD stages G1, G2, G3 and G4 and end-stage renal disease (ESRD) was 64.0%, 29.3%, 5.4%, 0.6%, 0.7%, respectively. Micro/macroalbuminuria prevalence was 8.6% and 3.0%, respectively. The incidence rate of ESRD was 2.5 (95% CI 1.9, 3.2) per 1000 person-years. The majority (59%) of those with chronic kidney disease stages G3–G5 did not have albuminuria on the day of recruitment or previously. Over 11.6 years of observation, the median annual decline in eGFR was modest at −1.3 ml min−1[1.73 m]−2 year−1(interquartile range [IQR]: −2.2, −0.4). However, 14% experienced a more significant loss of at least 3 ml min−1[1.73 m]−2. These decliners had more cardiovascular disease (OR 1.9,p = 5 × 10−5) and retinopathy (OR 1.3p = 0.02). Adding HbA1c, prior cardiovascular disease, recent mean eGFR and prior trajectory of eGFR to a model with age, sex, diabetes duration, current eGFR and ACR maximised the prediction of final eGFR (r2increment from 0.698 to 0.745,p −16). Attempting to model nonlinearity in eGFR decline or to detect latent classes of decliners did not improve prediction.ConclusionsThese data show much lower levels of kidney disease than historical estimates. However, early identification of those destined to experience significant decline in eGFR remains challenging.

Background and Aims: We considered if deep learning (DL) models trained to predict diabetic retinopathy (DR) are also useful for prediction of renal disease in people with type 1 diabetes. Materials and Methods: We used 35000 retinal images and associated ground truth DR grades from the online Kaggle dataset. These were used to train a Resnet convolutional neural network (CNN) to predict the ground truth DR grades. The CNN was then applied to retinal images for each patient in the SDRNT1BIO available from the national screening program generating 10 DL outputs corresponding to DR gradings for both eyes. Longitudinal eGFR readings and risk factor data were available for the SDRNT1BIO through linkage to clinical records. Univariate associations of DL outputs with final eGFR at follow-up was tested via linear regression models. A penalised Bayesian approach using 10-fold cross-validation was used to select the most predictive DL features and assess prediction performance. Results: We found 7 DL outputs had a significant univariate association with follow-up eGFR adjusting for age, sex, duration of diabetes, baseline eGFR, and length of follow-up at p Conclusion: This small study demonstrates that retinal images contain information for predicting eGFR decline and that deep learning can extract this information. Future work on a larger dataset using DL to predict renal disease progression directly is therefore warranted. Disclosure J.C. Mellor: None. A.J. Storkey: None. H. Colhoun: Advisory Panel; Self; Eli Lilly and Company, Novartis Pharmaceuticals Corporation, Sanofi-Aventis. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk A/S, Pfizer Inc., Regeneron Pharmaceuticals, Roche Pharma, Sanofi-Aventis. Speaker's Bureau; Self; Eli Lilly and Company, Regeneron Pharmaceuticals, Sanofi. Stock/Shareholder; Self; Bayer AG, Roche Pharma. P.M. McKeigue: Advisory Panel; Spouse/Partner; Eli Lilly and Company, Novartis Pharmaceuticals Corporation, Sanofi. Research Support; Spouse/Partner; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk A/S, Pfizer Inc., Regeneron Pharmaceuticals, Roche Pharma, Sanofi-Aventis. Stock/Shareholder; Spouse/Partner; Bayer AG, Roche Pharma.

IntroductionThe role of serum urate level has been extensively investigated in observational studies. However, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance.ObjectivesTo explore any causal or pleiotropic association between serum urate level and a broad spectrum of disease outcomes.MethodsPhenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic models (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 UK Biobank participants. Mendelian Randomisation (MR) analyses were performed to replicate significant findings using various GWAS consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for serum urate.ResultsPheWAS analysis, examining the association with 1,431 disease outcomes, identified a multitude of disease outcomes including gout, hypertension, hypercholesterolemia, and heart diseases (e.g., coronary atherosclerosis, myocardial infarction, and ischaemic heart disease) that were associated (pConclusionsHigh serum urate levels are associated with 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 more distal mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

Aims/hypothesis:\ud \ud Dapagliflozin, a sodium–glucose cotransporter 2 (SGLT2) inhibitor, is indicated for improving glycaemic control in type 2 diabetes mellitus. Whether its effects on HbA1c and other variables, including safety outcomes, in clinical trials are obtained in real-world practice needs to be established.\ud Methods:\ud \ud We used data from the comprehensive national diabetes register, the Scottish Care Information-Diabetes (SCI-Diabetes) collaboration database, available from 2004 to mid-2016. Data within this database were linked to mortality data from the General Registrar, available from the Information Services Division (ISD) of the National Health Service in Scotland. We calculated crude within-person differences between pre- and post-drug-initiation values of HbA1c, BMI, body weight, systolic blood pressure (SBP) and eGFR. We used mixed-effects regression models to adjust for within-person time trajectories in these measures. For completeness, we evaluated safety outcomes, cardiovascular disease events, lower-limb amputation and diabetic ketoacidosis, focusing on cumulative exposure effects, using Cox proportional hazard models, though power to detect such effects was limited.\ud Results:\ud \ud Among 8566 people exposed to dapagliflozin over a median of 210 days the crude within-person change in HbA1c was −10.41 mmol/mol (−0.95%) after 3 months’ exposure. The crude change after 12 months was −12.99 mmol/mol (−1.19%) but considering the expected rise over time in HbA1c gave a dapagliflozin-exposure-effect estimate of −15.14 mmol/mol (95% CI −15.87, −14.41) (−1.39% [95% CI −1.45, −1.32]) at 12 months that was maintained thereafter. A drop in SBP of −4.32 mmHg (95% CI −4.84, −3.79) on exposure within the first 3 months was also maintained thereafter. Reductions in BMI and body weight stabilised by 6 months at −0.82 kg/m2 (95% CI −0.87, −0.77) and −2.20 kg (95% CI −2.34, −2.06) and were maintained thereafter. eGFR declined initially by −1.81 ml min−1 [1.73 m]−2 (95% CI −2.10, −1.52) at 3 months but varied thereafter. There were no significant effects of cumulative drug exposure on safety outcomes.\ud Conclusions/interpretation:\ud \ud Dapagliflozin exposure was associated with reductions in HbA1c, SBP, body weight and BMI that were at least as large as in clinical trials. Dapagliflozin also prevented the expected rise in HbA1c and SBP over the period of study.

Background: Although COVID-19 vaccines have been shown to have high efficacy in the general population, it has not been established whether this applies to vulnerable groups. The objective of this study was to estimate the efficacy of vaccination in reducing the risk of severe COVID-19 among those designated as clinically extremely vulnerable in Scotland. Methods: In a matched case-control design (REACT-SCOT), all 111295 cases of COVID-19 in Scotland diagnosed from 1 December 2020 to 16 March 2021 were matched for age, sex and primary care practice to 1093449 controls from the general population. This was linked to national data on vaccinations and those designated as clinically extremely vulnerable and thus eligible for shielding support. Severe COVID-19 was defined as cases with entry to critical care or fatal outcome. Rate ratios associated with vaccination within risk groups were estimated by conditional logistic regression. Results: The rate ratio for severe COVID-19 associated with vaccination at least 14 days before was 0.29 (95% CI 0.22 to 0.37) in those eligible for shielding, compared with 0.29 (95% CI 0.25 to 0.34) in those ineligible for shielding. The rate ratio for hospitalized or fatal COVID-19 was 0.39 (95% CI 0.33 to 0.46) in those eligible and 0.37 (95% CI 0.33 to 0.41) in those not eligible for shielding. Examined by specific shielding conditions, the rate ratio for hospitalized or fatal COVID-19 ranged from 0.33 (95% CI 0.21 to 0.51) in those with specific cancers to 0.74 (95% CI 0.36 to 1.51) in solid organ transplant recipients, and 0.53 (95% CI 0.33 to 0.84) in others on immunosuppressants (excluding solid organ transplant recipients). Conclusions: These results are reassuring with respect to efficacy in clinically vulnerable individuals including immunocompromised individuals, but studies in larger populations are needed to estimate efficacy in solid organ transplant recipients.

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Immunoglobulin G (IgG) is the most abundant serum antibody and is a key determinant of the humoral immune response. Its structural characteristics and effector functions are modulated through the attachment of various sugar moieties called glycans. IgG N-glycome patterns change with the age of individual and in different diseases. Variability of IgG glycosylation within a population is well studied and is affected by a combination of genetic and environmental factors. However, global inter-population differences in IgG glycosylation have never been properly addressed. Here we present population-specific N-glycosylation patterns of whole IgG, analysed in 5 different populations totalling 10,482 IgG glycomes, and of IgG’s fragment crystallisable region (Fc), analysed in 2,530 samples from 27 populations sampled across the world. We observed that country of residence associates with many N-glycan features and is a strong predictor of monogalactosylation variability. IgG galactosylation also strongly correlated with the development level of a country, defined by United Nations health and socioeconomic development indicators. We found that subjects from developing countries had low IgG galactosylation levels, characteristic for inflammation and ageing. Our results suggest that citizens of developing countries may be exposed to country-specific environmental factors that can cause low-grade chronic inflammation and the apparent increase in biological age.

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown.METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function.RESULTS: Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1).CONCLUSIONS: The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

AIMS/HYPOTHESIS: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. METHODS: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30-75 ml min-1 [1.73 m]-2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. RESULTS: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p

Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions basedon albuminuria and renal function. We identified 16 genome-wide significant loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain(COL4A3)gene, which encodes a major structural component of the glomerular basement membrane (GBM) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD(BMP7)or renal biology (COLEC11andDDR1). The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.

Although the C-statistic is widely used for evaluating the performance of diagnostic tests, its limitations for evaluating the predictive performance of biomarker panels have been widely discussed. The increment in C obtained by adding a new biomarker to a predictive model has no direct interpretation, and the relevance of the C-statistic to risk stratification is not obvious. This paper proposes that the C-statistic should be replaced by the expected information for discriminating between cases and non-cases (expected weight of evidence, denoted as Λ), and that the strength of evidence favouring one model over another should be evaluated by cross-validation as the difference in test log-likelihoods. Contributions of independent variables to predictive performance are additive on the scale of Λ. Where the effective number of independent predictors is large, the value of Λ is sufficient to characterize fully how the predictor will stratify risk in a population with given prior probability of disease, and the C-statistic can be interpreted as a mapping of Λ to the interval from 0.5 to 1. Even where this asymptotic relationship does not hold, there is a one-to-one mapping between the distributions in cases and non-cases of the weight of evidence favouring case over non-case status, and the quantiles of these distributions can be used to calculate how the predictor will stratify risk. This proposed approach to reporting predictive performance is demonstrated by analysis of a dataset on the contribution of microbiome profile to diagnosis of colorectal cancer.

There is substantial variation in C-peptide levels in those with clinically diagnosed type 1 diabetes (T1DM). We examined the association of C-peptide levels with genotypic risk scores (GRS) for T1DM and T2DM in 5856 genotyped people diagnosed with T1DM and requiring insulin within 1 year of diagnosis (the SDRNT1BIO). Region-specific GRS and residual genome wide scores for T1DM and T2DM were computed based on publicly available summary GWAS data. Total genome wide scores were the sum of the residual and region specific scores. C-peptide was measured using a Roche ultrasensitive immunoassay on untimed serum. Overall 39% had detectable C-peptide (≥5 pmol/L) with prevalence being highest with older age of onset (p=6.9x10-05) and shorter duration (p=1.8x10-10). Heritability was 0.31 for age at onset and 0.26 for C-peptide adjusted for sex and age at onset. Lower T1DM genome-wide score and higher T2DM score were positively associated with C-peptide level, associations that were stronger with older age at onset (p=4.9x10- for T1DM score, p=0.009 for T2DM score interactions with age at onset). The T1DM and T2DM GRS were uncorrelated except at the HLA region. Most T1DM region specific scores were lower with increasing age at onset in particular at the HLA (p=3.2x10-34), INS (1.9x10-05) CTSH (9.1x10-06) and IKZF1 (p=0.0001) regions. Adjusted for age at onset, higher C-peptide level was also associated with lower T1DM GRS at the HLA (p=9.8x10-17) and INS (p=2.7x10-06) regions. Most T2DM region specific scores were higher with older age at onset and adjusted C-peptide except a T2DM associated HLA region score which was lower at higher age of onset (p=1.7x10-17) and higher C-peptide (p=1.1x10-09). These results show that in people diagnosed with T1DM, especially at older ages, the variation in persistence of C-peptide secretion in part reflects a continuous spectrum of genetic risk for T1DM and T2DM. The lack of correlation in T1DM and T2DM GRS is in keeping with this continuum rather than “misdiagnosis” of T1DM as T2DM. Disclosure P. McKeigue: Stock/Shareholder; Self; Bayer AG, Roche Pharma. A. Spiliopoulou: None. S. McGurnaghan: None. M. Colombo: None. T.J. McDonald: None. H. Colhoun: Research Support; Self; AstraZeneca, Boehringer Ingelheim GmbH. Stock/Shareholder; Self; Bayer AG. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Other Relationship; Self; Eli Lilly and Company. Advisory Panel; Self; Novartis Pharmaceuticals Corporation. Research Support; Self; Regeneron Pharmaceuticals, Inc.. Advisory Panel; Self; Regeneron Pharmaceuticals, Inc.. Speaker's Bureau; Self; Regeneron Pharmaceuticals, Inc.. Other Relationship; Self; Regeneron Pharmaceuticals, Inc.. Research Support; Self; Pfizer Inc., Roche Pharma. Stock/Shareholder; Self; Roche Pharma. Research Support; Self; Sanofi-Aventis. Advisory Panel; Self; Sanofi-Aventis. Speaker's Bureau; Self; Sanofi. Other Relationship; Self; Sanofi. Research Support; Self; Novo Nordisk Inc..

Background and aimsDeveloping sparse panels of biomarkers for cardiovascular disease in type 2 diabetes would enable risk stratification for clinical decision making and selection into clinical trials. We examined the individual and joint performance of five candidate biomarkers for incident cardiovascular disease (CVD) in type 2 diabetes that an earlier discovery study had yielded.MethodsApolipoprotein CIII (apoCIII), N-terminal prohormone B-type natriuretic peptide (NT-proBNP), high sensitivity Troponin T (hsTnT), Interleukin-6, and Interleukin-15 were measured in baseline serum samples from the Collaborative Atorvastatin Diabetes trial (CARDS) of atorvastatin versus placebo. Among 2105 persons with type 2 diabetes and median age of 62.9 years (range 39.2–77.3), there were 144 incident CVD (acute coronary heart disease or stroke) cases during the maximum 5-year follow up. We used Cox Proportional Hazards models to identify biomarkers associated with incident CVD and the area under the receiver operating characteristic curves (AUROC) to assess overall model prediction.ResultsThree of the biomarkers were singly associated with incident CVD independently of other risk factors; NT-proBNP (Hazard Ratio per standardised unit 2.02, 95% Confidence Interval [CI] 1.63, 2.50), apoCIII (1.34, 95% CI 1.12, 1.60) and hsTnT (1.40, 95% CI 1.16, 1.69). When combined in a single model, only NT-proBNP and apoCIII were independent predictors of CVD, together increasing the AUROC using Framingham risk variables from 0.661 to 0.745.ConclusionsThe biomarkers NT-proBNP and apoCIII substantially increment the prediction of CVD in type 2 diabetes beyond that obtained with the variables used in the Framingham risk score.

Aims/hypothesis As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. Methods We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case–control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30–75 ml min−1 [1.73 m]−2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. Results Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p

OBJECTIVE Poorer glycemic control in type 1 diabetes may alter N-glycosylation patterns on circulating glycoproteins, and these alterations may be linked with diabetic kidney disease (DKD). We investigated associations between N-glycans and glycemic control and renal function in type 1 diabetes. RESEARCH DESIGN AND METHODS Using serum samples from 818 adults who were considered to have extreme annual loss in estimated glomerular filtration rate (eGFR; i.e., slope) based on retrospective clinical records, from among 6,127 adults in the Scottish Diabetes Research Network Type 1 Bioresource Study, we measured total and IgG-specific N-glycan profiles. This yielded a relative abundance of 39 total (GP) and 24 IgG (IGP) N-glycans. Linear regression models were used to investigate associations between N-glycan structures and HbA1c, albumin-to-creatinine ratio (ACR), and eGFR slope. Models were adjusted for age, sex, duration of type 1 diabetes, and total serum IgG. RESULTS Higher HbA1c was associated with a lower relative abundance of simple biantennary N-glycans and a higher relative abundance of more complex structures with more branching, galactosylation, and sialylation (GP12, 26, 31, 32, and 34, and IGP19 and 23; all P < 3.79 × 10−4). Similar patterns were seen for ACR and greater mean annual loss of eGFR, which were also associated with fewer of the simpler N-glycans (all P < 3.79 × 10−4). CONCLUSIONS Higher HbA1c in type 1 diabetes is associated with changes in the serum N-glycome that have elsewhere been shown to regulate the epidermal growth factor receptor and transforming growth factor-β pathways that are implicated in DKD. Furthermore, N-glycans are associated with ACR and eGFR slope. These data suggest that the role of altered N-glycans in DKD warrants further investigation.

We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes.In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC).Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA1c.We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.

A common problem in biomedical research is to calculate the sample size required to learn a classifier using a (possibly high-dimensional) panel of biomarkers. This paper describes a simple method based on a Gaussian approximation for calculating the predictive performance of the learned classifier given the size of the biomarker panel, the size of the training sample, and the optimal predictive performance (expressed as C-statistic [Formula: see text]) of the biomarker panel that could be obtained if a training sample of unlimited size were available. Under the assumption that the biomarker effect sizes have the same correlation structure as the biomarkers, the required sample size does not depend upon these correlations, but only upon [Formula: see text] and upon the sparsity of the distribution of effect sizes, defined by the proportion of biomarkers that have nonzero effects. To learn a classifier that extracts 80% of the predictive information, the required case sample size varies from about 0.1 cases per variable for a panel with [Formula: see text] and a sparse distribution of effect sizes (such that 1% of biomarkers have nonzero effect sizes) to nine cases per variable for a panel with [Formula: see text] and a diffuse distribution of effect sizes.

Background: Whether metformin precipitates lactic acidosis in patients with chronic kidney disease (CKD) remains\ud under debate. We examined whether metformin use was associated with an increased risk of acute kidney injury\ud (AKI) as a proxy for lactic acidosis and whether survival among those with AKI varied by metformin exposure.\ud Methods: All individuals with type 2 diabetes and available prescribing data between 2004 and 2013 in Tayside,\ud Scotland were included. The electronic health record for diabetes which includes issued prescriptions was linked to\ud laboratory biochemistry, hospital admission, death register and Scottish Renal Registry data. AKI events were defined\ud using the Kidney Disease Improving Global Outcomes criteria with a rise in serum creatinine of at least 26.5 μmol/l or\ud a rise of greater than 150% from baseline for all hospital admissions. Cox Regression Analyses were used to examine\ud whether person-time periods in which current metformin exposure occurred were associated with an increased rate of\ud first AKI compared to unexposed periods. Cox regression was also used to compare 28 day survival rates following first\ud AKI events in those exposed to metformin versus those not exposed.\ud Results: Twenty-five thousand one-hundred fourty-eight patients were included with a total person-time of\ud 126,904 person years. 4944 (19.7%) people had at least one episode of AKI during the study period. There\ud were 32.4 cases of first AKI/1000pyrs in current metformin exposed person-time periods compared to 44.9\ud cases/1000pyrs in unexposed periods. After adjustment for age, sex, diabetes duration, calendar time, number\ud of diabetes drugs and baseline renal function, current metformin use was not associated with AKI incidence,\ud HR 0.94 (95% CI 0.87, 1.02, p = 0.15). Among those with incident AKI, being on metformin at admission was\ud associated with a higher rate of survival at 28 days (HR 0.81, 95% CI 0.69, 0.94, p = 0.006) even after\ud adjustment for age, sex, pre-admission eGFR, HbA1c and diabetes duration.\ud Conclusions: Contrary to common perceptions, we found no evidence that metformin increases incidence of\ud AKI and was associated with higher 28 day survival following incident AKI.

BACKGROUND Few genetic risk factors have been uncovered that contribute specifically to the racial disparity in prostate cancer (CaP) observed in African Americans (AA). With the advent of ancestry informative marker (AIM) single nucleotide polymorphism (SNP) panels and powerful genetic strategies such as mapping by admixture linkage disequilibrium (MALD) it is possible to discover genes that underlie ethnic variation in disease risk. METHODS One thousand one hundred thirty AA CaP cases enrolled in the North Carolina-Louisiana Prostate Cancer Project (PCaP) were genotyped using a 1,509 AIM SNP panel. MALD was performed using ADMIXMAP to test for linkage between CaP risk and ancestry estimates at each AIM SNP. RESULTS The largest increase of African ancestry was observed at marker rs12543473 (P = 0.0011), located on chromosome 8q24.21, and the greatest excess of European ancestry was observed at marker rs10768140 (P = 0.0004) at chromosome 11p13. CONCLUSIONS The study confirmed the 8q24 risk loci and identified a novel genomic region on 11p13 that is associated with CaP risk. These findings should be replicated in larger AA populations and combined with fine mapping data to further refine the novel 11p13 CaP risk loci. Prostate 74:1–9, 2014. © 2013 Wiley Periodicals, Inc.

Aims/hypothesis The aim of our study was to identify subgroups of patients attending the Scottish Diabetic Retinopathy Screening (DRS) programme who might safely move from annual to two yearly retinopathy screening. Methods This was a retrospective cohort study of screening data from the DRS programme collected between 2005 and 2011 for people aged ≥12 years with type 1 or type 2 diabetes in Scotland. We used hidden Markov models to calculate the probabilities of transitions to referable diabetic retinopathy (referable background or proliferative retinopathy) or referable maculopathy. Results The study included 155,114 individuals with no referable diabetic retinopathy or maculopathy at their first DRS examination and with one or more further DRS examinations. There were 11,275 incident cases of referable diabetic eye disease (9,204 referable maculopathy, 2,071 referable background or proliferative retinopathy). The observed transitions to referable background or proliferative retinopathy were lower for people with no visible retinopathy vs mild background retinopathy at their prior examination (respectively, 1.2% vs 8.1% for type 1 diabetes and 0.6% vs 5.1% for type 2 diabetes). The lowest probability for transitioning to referable background or proliferative retinopathy was among people with two consecutive screens showing no visible retinopathy, where the probability was

Objectives This study sought to determine whether ethnic differences in diabetes, dyslipidemia, and ectopic fat deposition account for ethnic differences in incident cardiovascular disease. Background Coronary heart disease risks are elevated in South Asians and are lower in African Caribbeans compared with Europeans. These ethnic differences map to lipid patterns and ectopic fat deposition. Methods Cardiovascular risk factors were assessed in 2,049 Europeans, 1,517 South Asians, and 630 African Caribbeans from 1988 through 1991 (mean age: 52.4 ± 6.9 years). Fatal and nonfatal events were captured over a median 20.5-year follow-up. Subhazard ratios (SHR) were calculated using competing risks regression. Results Baseline diabetes prevalence was more than 3 times greater in South Asians and African Caribbeans than in Europeans. South Asians were more and African Caribbeans were less centrally obese and dyslipidemic than Europeans. Compared with Europeans, coronary heart disease incidence was greater in South Asians and less in African Caribbeans. The age- and sex-adjusted South Asian versus European SHR was 1.70 (95% confidence interval [CI]: 1.52 to 1.91, p Conclusions Ethnic differences in measured metabolic risk factors did not explain differences in coronary heart disease incidence. The apparently greater association between diabetes and stroke risk in South Asians and African Caribbeans compared with Europeans merits further study.

Diabetes is the leading cause of end stage renal disease. Despite evidence for a substantial heritability of diabetic kidney disease, efforts to identify genetic susceptibility variants have had limited success. We extended previous efforts in three dimensions, examining a more comprehensive set of genetic variants in larger numbers of subjects with type 1 diabetes characterized for a wider range of cross-sectional diabetic kidney disease phenotypes. In 2,843 subjects, we estimated that the heritability of diabetic kidney disease was 35% (p=6x10-3 7 ). Genome-wide association analysis and replication in 12,540 individuals identified no single variants reaching stringent levels of significance and, despite excellent power, provided little independent confirmation of previously published associated variants. Whole exome sequencing in 997 subjects failed to identify any large-effect coding alleles of lower frequency influencing the risk of diabetic kidney disease. However, sets of alleles increasing body mass index (p=2.2×10-5) and the risk of type 2 diabetes (p=6.1x10-4 11 ) were associated with the risk of diabetic kidney disease. We also found genome-wide genetic correlation between diabetic kidney disease and failure at smoking cessation (p=1.1×10-4 13 ). Pathway analysis implicated ascorbate and aldarate metabolism (p=9×10-6), and pentose and glucuronate interconversions (p=3×10-6 14 ) in pathogenesis of diabetic kidney disease. These data provide further evidence for the role of genetic factors influencing diabetic kidney disease in those with type 1 diabetes and highlight some key pathways that may be responsible. Altogether these results reveal important biology behind the major cause of kidney disease.

We address the task of genotype imputation to a dense reference panel given genotype likelihoods computed from ultralow coverage sequencing as inputs. In this setting, the data have a high-level of missingness or uncertainty, and are thus more amenable to a probabilistic representation. Most existing imputation algorithms are not well suited for this situation, as they rely on prephasing for computational efficiency, and, without definite genotype calls, the prephasing task becomes computationally expensive. We describe GeneImp, a program for genotype imputation that does not require prephasing and is computationally tractable for whole-genome imputation. GeneImp does not explicitly model recombination, instead it capitalizes on the existence of large reference panels—comprising thousands of reference haplotypes—and assumes that the reference haplotypes can adequately represent the target haplotypes over short regions unaltered. We validate GeneImp based on data from ultralow coverage sequencing (0.5×), and compare its performance to the most recent version of BEAGLE that can perform this task. We show that GeneImp achieves imputation quality very close to that of BEAGLE, using one to two orders of magnitude less time, without an increase in memory complexity. Therefore, GeneImp is the first practical choice for whole-genome imputation to a dense reference panel when prephasing cannot be applied, for instance, in datasets produced via ultralow coverage sequencing. A related future application for GeneImp is whole-genome imputation based on the off-target reads from deep whole-exome sequencing.

In an isolated population, individuals are likely to share large genetic regions inherited from common ancestors. Identity by descent (IBD) can be inferred from SNP genotypes, which is useful in a number of applications, including identifying genetic variants influencing complex disease risk, and planning efficient cohort-sequencing strategies. We present ANCHAP – a method for detecting IBD in isolated populations. We compare accuracy of the method against other long-range and local phasing methods, using parent–offspring trios. In our experiments, we show that ANCHAP performs similarly as the other long-range method, but requires an order-of-magnitude less computational resources. A local phasing model is able to achieve similar sensitivity, but only at the cost of higher false discovery rates. In some regions of the genome, the studied individuals share haplotypes particularly often, which hints at the history of the populations studied. We demonstrate the method using SNP genotypes from three isolated island populations, as well as in a cohort of unrelated individuals. In samples from three isolated populations of around 1000 individual each, an average individual shares a haplotype at a genetic locus with 9–12 other individuals, compared with only 1 individual within the non-isolated population. We describe an application of ANCHAP to optimally choose samples in resequencing studies. We find that with sample sizes of 1000 individuals from an isolated population genotyped using a dense SNP array, and with 20% of these individuals sequenced, 65% of sequences of the unsequenced subjects can be partially inferred.

OBJECTIVE To determine the extent of, and reasons for, ethnic differences in type 2 diabetes incidence in the U.K. RESEARCH DESIGN AND METHODS Population-based triethnic cohort. Participants were without diabetes, aged 40–69 at baseline (1989–1991), and followed-up for 20 years. Baseline measurements included fasting and postglucose bloods, anthropometry, and lifestyle questionnaire. Incident diabetes was identified from medical records and participant recall. Ethnic differences in diabetes incidence were examined using competing risks regression. RESULTS Incident diabetes was identified in 196 of 1,354 (14%) Europeans, 282 of 839 (34%) Indian Asians, and 100 of 335 (30%) African Caribbeans. All Indian Asians and African Caribbeans were first-generation migrants. Compared with Europeans, age-adjusted subhazard ratios (SHRs [95% CI]) for men and women, respectively, were 2.88 (95%, 2.36–3.53; P < 0.001) and 1.91 (1.18–3.10; P = 0.008) in Indian Asians, and 2.23 (1.64–3.03; P < 0.001) and 2.51 (1.63–3.87; P < 0.001) in African Caribbeans. Differences in baseline insulin resistance and truncal obesity largely attenuated the ethnic minority excess in women (adjusted SHRs: Indian Asians 0.77 [0.49–1.42]; P = 0.3; African Caribbeans 1.48 [0.89–2.45]; P = 0.13), but not in men (adjusted SHRs: Indian Asians 1.98 [1.52–2.58]; P < 0.001 and African Caribbeans, 2.05 [1.46–2.89; P < 0.001]). CONCLUSIONS Insulin resistance and truncal obesity account for the twofold excess incidence of diabetes in Indian Asian and African Caribbean women, but not men. Explanations for the excess diabetes risk in ethnic minority men remains unclear. Further study requires more precise measures of conventional risk factors and identification of novel risk factors.

Objective To determine any ethnic differences in circulating interleukin (IL)-6 concentrations among SAs and Europeans, and to assess their relationship with body composition and insulin resistance measures. Methods Body composition was assessed among 80 SA and European men and women using anthropometry, dual-energy X-ray absorptiometry and abdominal CT scan. Oral glucose tolerance tests with insulin response were performed to assess insulin resistance measures. IL-6 levels were measured by high sensitivity ELISA. Results Median IL-6 values were higher in SA compared with European women: 1.94 mg/l versus 1.51 mg/l, p = 0.041, but not so in men (1.56 mg/l versus 1.57 mg/l). Only measures of obesity, in particular percentage fat area (r = 0.6, p = 0.003), were positively correlated with IL-6 in SAs. Differences in body fat percentage (visceral and total) could explain up to 30% of the IL-6 difference between Asian and European women. Conclusion SA women have elevated circulating IL-6 levels, in part due to greater visceral and percent fat levels compared with European women. This observation may in part explain why Asians are at elevated cardiovascular disease risk. Future studies should address the effects of lifestyle factors (physical activity, diet) on plasma IL-6 concentrations in SA women.

BACKGROUND: In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation.METHODS AND RESULTS: We performed a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with pCONCLUSIONS: Based on results from this study that included a relatively large sample size, we suggest that CETP may be the only detectable locus with common genetic variants that influence HDL-C response to statins substantially in individuals of European descent. Although CETP is known to be associated with HDL-C, we provide evidence that this pharmacogenetic effect is independent of its association with baseline HDL-C levels.

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites.

We report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets.We analysed 967 cases and 343 normoglycaemic controls. The samples were genotyped with the Affymetrix Genome-wide Human SNP array 5.0. Associations of genotyped and imputed markers with type 2 diabetes were tested using a missing data likelihood score test. A fixed-effects meta-analysis including 1,804 cases and 780 normoglycaemic controls was carried out by weighting the effect estimates by their inverse variances.In the meta-analysis of the two Hispanic studies, markers showing suggestive associations (p 10(-5)) were identified in two known diabetes genes, HNF1A and KCNQ1, as well as in several additional regions. Meta-analysis of the two Hispanic studies and the recent DIAGRAM+ dataset identified genome-wide significant signals (p 5 × 10(-8)) within or near the genes HNF1A and CDKN2A/CDKN2B, as well as suggestive associations in three additional regions, IGF2BP2, KCNQ1 and the previously unreported C14orf70.We observed numerous regions with suggestive associations with type 2 diabetes. Some of these signals correspond to regions described in previous studies. However, many of these regions could not be replicated in the DIAGRAM datasets. It is critical to carry out additional studies in Hispanic and American Indian populations, which have a high prevalence of type 2 diabetes.

Genome-wide linkage and association studies have uncovered variants associated with sarcoidosis, a multiorgan granulomatous inflammatory disease. African ancestry may influence disease pathogenesis, as African-Americans are more commonly affected by sarcoidosis. Therefore, we conducted the first sarcoidosis genome-wide ancestry scan using a map of 1384 highly ancestry-informative single-nucleotide polymorphisms genotyped on 1357 sarcoidosis cases and 703 unaffected controls self-identified as African-American. The most significant ancestry association was at marker rs11966463 on chromosome 6p22.3 (ancestry association risk ratio (aRR) = 1.90; P = 0.0002). When we restricted the analysis to biopsy-confirmed cases, the aRR for this marker increased to 2.01; P = 0.00007. Among the eight other markers that demonstrated suggestive ancestry associations with sarcoidosis were rs1462906 on chromosome 8p12, which had the most significant association with European ancestry (aRR = 0.65; P = 0.002), and markers on chromosomes 5p13 (aRR = 1.46; P = 0.005) and 5q31 (aRR = 0.67; P = 0.005), which correspond to regions we previously identified through sib-pair linkage analyses. Overall, the most significant ancestry association for Scadding stage IV cases was to marker rs7919137 on chromosome 10p11.22 (aRR = 0.27; P = 2 x 10(-5)), a region not associated with disease susceptibility. In summary, through admixture mapping of sarcoidosis we have confirmed previous genetic linkages and identified several novel putative candidate loci for sarcoidosis. Genes and Immunity (2011) 12, 67-77; doi:10.1038/gene.2010.56; published online 23 December 2010

Childhood absence epilepsy (CAE) is an idiopathic generalised epilepsy (IGE) characterised by typical absence seizures manifested by transitory loss of awareness with 2.5-4 Hz spike-wave complexes on ictal EEG. A genetic component to the aetiology is wet[ recognised but the mechanism of inheritance and the genes involved are yet to be fully established.A genome wide single nucleotide polymorphism (SNP)-based high density linkage scan was carried out using 41 nuclear pedigrees with at least two affected members. Multipoint parametric and non-parametric linkage analyses were performed using MERLIN 1.1.1 and a susceptibility locus was identified on chromosome 3p23-p14 (Z(mean) = 3.9, p < 0.0001; HLOD = 3.3, alpha = 0.7). The (inked region harbours the functional candidate genes TRAK1 and CACNA2D2. Fine-mapping using a tagSNP approach demonstrated disease association with variants in TRAK1. (C) 2009 Elsevier B.V. All rights reserved.

People of African origin have increased risk of stroke and retinal microvascular disease compared with populations of European origin. We compared quantitative measures of retinal microvasculature in British white Europeans and African Caribbeans.Population-based study of 215 (45% male) British African-Caribbean migrants and 323 (48% male) white Europeans aged 40-69 years. Digitised retinal images were analysed using a validated semi-automated system.Arteriolar optimality deviation, an indicator of endothelial dysfunction, was greater in African Caribbeans (age- and sex-adjusted means [95% CIs]: 0.06 [0.05-0.06] vs 0.04 [0.04-0.05], p = 0.004); this was unexplained by conventional risk factors. Arteriolar diameters were narrower in African Caribbeans (age- and sex-adjusted means [95% CIs]: 18.4 [18.1-18.6] vs 17.9 [17.6-18.2], p = 0.011). These ethnic differences in diameters were attenuated on adjustment for systolic BP (SBP) (adjusted means: 18.2 vs 18.1, p = 0.31). However, there was a significant interaction (p = 0.011) between diabetes and SBP, such that SBP was strongly associated with arteriolar diameter in people without diabetes, but not in those with diabetes (adjusted beta-coefficients for SBP: Europeans: -0.42, p = 0.002 vs 0.17, p = 0.69, African Caribbeans: -0.35, p = 0.023 vs 0.01, p = 0.96). Other measures of retinal vasculature did not differ by ethnicity.British African Caribbeans appear to have poorer retinal arteriolar endothelial function than white Europeans. Higher BPs explained the narrower arterioles in African Caribbeans; however, patterns of association between arteriolar narrowing and BP suggest the possibility that cerebral autoregulation and/or remodelling might be adversely affected by diabetes in both ethnic groups.

Uric acid is the end product of purine metabolism in humans and great apes, which have lost hepatic uricase activity, leading to uniquely high serum uric acid concentrations (200-500 microM) compared with other mammals (3-120 microM). About 70% of daily urate disposal occurs via the kidneys, and in 5-25% of the human population, impaired renal excretion leads to hyperuricemia. About 10% of people with hyperuricemia develop gout, an inflammatory arthritis that results from deposition of monosodium urate crystals in the joint. We have identified genetic variants within a transporter gene, SLC2A9, that explain 1.7-5.3% of the variance in serum uric acid concentrations, following a genome-wide association scan in a Croatian population sample. SLC2A9 variants were also associated with low fractional excretion of uric acid and/or gout in UK, Croatian and German population samples. SLC2A9 is a known fructose transporter, and we now show that it has strong uric acid transport activity in Xenopus laevis oocytes.

Background: We aimed to estimate the prevalence, correlates and impact of dementia in Havana and Matanzas, Cuba. Methods: A 1-phase catchment area survey of all over 65-year-old residents of 7 catchment areas in Havana and 1 in Matanzas was conducted. Dementia diagnosis was established according to DSM-IV and our own, pre-validated10/66 criteria. The impact of dementia was assessed through associations with needs for care, cutting back on work to care and caregiver psychological morbidity. Results: We interviewed 2,944 older people, a response proportion of 96.4%. The prevalence of DSM-IV dementia was 6.4% and that of 10/66 dementia 10.8%. Both dementia outcomes were associated with older age, less education, a family history of dementia, shorter leg length and smaller skull circumference. Dementia, rather than physical health problems or depression, was the main contributor to needs for care (population-attributable prevalence fraction = 64.6%) and caregiver cutting back on work (population-attributable prevalence fraction = 57.3%). Conclusion: The prevalence of dementia in Cuba is similar to Europe. Among health conditions, dementia is the major contributor to dependency and caregiver economic and psychological strain. More attention needs to be given to it and other chronic diseases associated more with disability than premature mortality.

To assess the cost-effectiveness of adopting risk-stratified approaches to extended screening intervals in the national diabetic retinopathy screening programme in Scotland.A continuous-time hidden Markov model was fitted to national longitudinal screening data to derive transition probabilities between observed non-referable and referable retinopathy states. These were incorporated in a decision model simulating progression, costs and visual acuity outcomes for a synthetic cohort with a covariate distribution matching that of the Scottish diabetic screening population. The cost-effectiveness of adopting extended (2-year) screening for groups with no observed retinopathy was then assessed over a 30-year time horizon.Individuals with a current grade of no retinopathy on two consecutive screening episodes face the lowest risk of progressing to referable disease. For the cohort as a whole, the incremental cost per quality-adjusted life year gained for annual vs. biennial screening ranged from approximately £74 000 (for those with no retinopathy and a prior observed grade of mild or observable background retinopathy) to approximately £232 000 per quality-adjusted life year gained (for those with no retinopathy on two consecutive screening episodes). The corresponding incremental cost-effectiveness ratios in the subgroup with Type 1 diabetes were substantially lower; approximately £22 000 to £85 000 per quality-adjusted life year gained, respectively.Biennial screening for individuals with diabetes who have no retinopathy is likely to deliver significant savings for a very small increase in the risk of adverse visual acuity and quality of life outcomes. There is greater uncertainty regarding the long-term cost-effectiveness of adopting biennial screening in younger people with Type 1 diabetes.

Admixture mapping (AM) is a promising method for the identification of genetic risk factors for complex traits and diseases showing prevalence differences among populations. Efficient application of this method requires the use of a genomewide panel of ancestry-informative markers (AIMs) to infer the population of origin of chromosomal regions in admixed individuals. Genomewide AM panels with markers showing high frequency differences between West African and European populations are already available for disease-gene discovery in African Americans. However, no such a map is yet available for Hispanic/Latino populations, which are the result of two-way admixture between Native American and European populations or of three-way admixture of Native American, European, and West African populations. Here, we report a genomewide AM panel with 2,120 AIMs showing high frequency differences between Native American and European populations. The average intermarker genetic distance is ∼1.7 cM. The panel was identified by genotyping, with the Affymetrix GeneChip Human Mapping 500K array, a population sample with European ancestry, a Mesoamerican sample comprising Maya and Nahua from Mexico, and a South American sample comprising Aymara/Quechua from Bolivia and Quechua from Peru. The main criteria for marker selection were both high information content for Native American/European ancestry (measured as the standardized variance of the allele frequencies, also known as “f value”) and small frequency differences between the Mesoamerican and South American samples. This genomewide AM panel will make it possible to apply AM approaches in many admixed populations throughout the Americas.

Udgivelsesdato: April Childhood absence epilepsy (CAE) is an idiopathic generalised epilepsy characterised by absence seizures manifested by transitory loss of awareness with 2.5-4 Hz spike-wave complexes on ictal EEG. A genetic component to aetiology is established but the mechanism of inheritance and the genes involved are not fully defined. Available evidence suggests that genes encoding brain expressed voltage-gated calcium channels, including CACNG3 on chromosome 16p12-p13.1, may represent susceptibility loci for CAE. The aim of this work was to further evaluate CACNG3 as a susceptibility locus by linkage and association analysis. Assuming locus heterogeneity, a significant HLOD score (HLOD = 3.54, alpha = 0.62) was obtained for markers encompassing CACNG3 in 65 nuclear families with a proband with CAE. The maximum non-parametric linkage score was 2.87 (P < 0.002). Re-sequencing of the coding exons in 59 patients did not identify any putative causal variants. A linkage disequilibrium (LD) map of CACNG3 was constructed using 23 single nucleotide polymorphisms (SNPs). Transmission disequilibrium was sought using individual SNPs and SNP-based haplotypes with the pedigree disequilibrium test in 217 CAE trios and the 65 nuclear pedigrees. Evidence for transmission disequilibrium (P < or = 0.01) was found for SNPs within a approximately 35 kb region of high LD encompassing the 5'UTR, exon 1 and part of intron 1 of CACNG3. Re-sequencing of this interval was undertaken in 24 affected individuals. Seventy-two variants were identified: 45 upstream; two 5'UTR; and 25 intronic SNPs. No coding sequence variants were identified, although four variants are predicted to affect exonic splicing. This evidence supports CACNG3 as a susceptibility locus in a subset of CAE patients.

A family-based study has recently reported that a variant located in intron 10 of the gene MGEA5 increases susceptibility to Type 2 Diabetes (T2D). We evaluated the distribution of this SNP in a sample of T2D patients (N = 271) and controls (N = 244) from Mexico City. The frequency of the T allele was higher in the cases (2.6%) than in the controls (1.8%). After adjusting for age, sex, BMI, education, and individual ancestry the odds ratio was 1.60 but the 95% confidence interval was wide and overlapped 1 (0.52–4.86, P-value : 0.404). In order to characterize the distribution of the MGEA5-14 polymorphism in the relevant parental populations, we genotyped this variant in European (and European Americans), West African, and Native American samples. The T-allele was present at a frequency of 2.3% in Spain, 4.2% in European Americans, and 13% in Western Africans, but was absent in two Native American samples from Mexico and Peru. Given the low frequency of the T-allele, further studies using large sample sizes will be required to confirm the role of this variant in T2D. Am. J. Hum. Biol. 19:593–596, 2007. © 2007Wiley-Liss, Inc.

Human skin pigmentation shows a strong positive correlation with ultraviolet radiation intensity, suggesting that variation in skin color is, at least partially, due to adaptation via natural selection. We investigated the evolution of pigmentation variation by testing for the presence of positive directional selection in 6 pigmentation genes using an empirical F(ST) approach, through an examination of global diversity patterns of these genes in the Centre d'Etude du Polymorphisme Humain (CEPH)-Diversity Panel, and by exploring signatures of selection in data from the International HapMap project. Additionally, we demonstrated a role for MATP in determining normal skin pigmentation variation using admixture mapping methods. Taken together (with the results of previous admixture mapping studies), these results point to the importance of several genes in shaping the pigmentation phenotype and a complex evolutionary history involving strong selection. Polymorphisms in 2 genes, ASIP and OCA2, may play a shared role in shaping light and dark pigmentation across the globe, whereas SLC24A5, MATP, and TYR have a predominant role in the evolution of light skin in Europeans but not in East Asians. These findings support a case for the recent convergent evolution of a lighter pigmentation phenotype in Europeans and East Asians.

Admixture between populations originating on different continents can be exploited to detect disease susceptibility loci at which risk alleles are distributed differentially between these populations. We first examine the statistical power and mapping resolution of this approach in the limiting situation in which gamete admixture and locus ancestry are measured without uncertainty. We show that, for a rare disease, the most efficient design is to study affected individuals only. In a typical African American population (two-way admixture proportions 0.8/0.2, ancestry crossover rate 2 per 100 cM), a study of 800 affected individuals has 90% power to detect at P values −5 a locus that generates a risk ratio of 2 between populations, with an expected mapping resolution (size of 95% confidence region for the position of the locus) of 4 cM. In practice, to infer locus ancestry from marker data requires Bayesian computationally intensive methods, as implemented in the program ADMIXMAP. Affected-only study designs require strong prior information on the frequencies of each allele given locus ancestry. We show how data from unadmixed and admixed populations can be combined to estimate these ancestry-specific allele frequencies within the admixed population under study, allowing for variation between allele frequencies in unadmixed and admixed populations. Using simulated data based on the genetic structure of the African American population, we show that 60% of information can be extracted in a test for linkage using markers with an ancestry information content of 36% at 3-cM spacing. As in classic linkage studies, the most efficient strategy is to use markers at a moderate density for an initial genome search and then to saturate regions of putative linkage with additional markers, to extract nearly all information about locus ancestry.

Objective: To investigate the role of genetic admixture in explaining phenotypic variation in obesity-related traits in a sample of African-American women (n = 145) and to determine significant associations between obesity traits and admixture genetic markers. Research Methods and Procedures: Associations between genetic admixture and BMI, resting metabolic rate, fat mass, fat-free mass, and bone mineral density were tested using linear regression considering the estimation of admixture by 1) a maximum-likelihood approach (MLA) and 2) a Bayesian analysis. Results: Both the conservative MLA and the Bayesian approach support an association between African genetic admixture and BMI. Evidence for the associations of African genetic admixture with fat mass and fat-free mass was supported by the Bayesian analysis; the MLA supported an association with bone mineral density. When the individual ancestry informative markers that were used to estimate admixture were tested for associations with BMI, significant associations were identified in chromosomes 1, 11, and 12. Discussion: These results provide evidence supporting the application of admixture mapping methods to the identification of genes that result in higher levels of obesity among African-American women. Further research is needed to replicate and further explore these findings.

To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations--African American, African Caribbean, and Hispanic American--in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is 70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case–control design nested within a prospective cohort of patients with baseline eGFR 30–60 ml/min per 1.73 m 2 . Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

N‐glycans attached to the Fc portion of IgG are important modulators of IgG effector functions 1, 2 (see Supplementary Figure 1, available on the Arthritis & Rheumatology web site at http://onlinelibrary.wiley.com/doi/10.1002/art.39273/abstract). Glycans that lack terminal galactose activate complement and make IgG proinflammatory, while the addition of galactose decreases the inflammatory potential of IgG 1, 3. Further extension of IgG glycans by the addition of sialic acid dramatically changes the physiologic role of IgG, converting it from a proinflammatory agent into an antiinflammatory agent. This relatively small fraction of sialylated IgG is believed to be responsible for the immunosuppressive activity of intravenous immunoglobulins (IVIGs) 4. Another feature of the IgG glycan, fucose attached to the glycan core (core fucose), interferes with binding of IgG to Fcγ receptor IIIa (FcγRIIIa) and greatly diminishes its capacity for the activation of antibody‐dependent cell‐mediated cytotoxicity (ADCC) 5. The removal of core fucose from IgG glycans increases clinical efficacy of monoclonal antibodies, enhancing their therapeutic effect through ADCC‐mediated killing 6, 7. Our recent genome‐wide association study (GWAS) of the IgG glycome in 2,247 individuals identified 16 genetic loci that are associated with variations in composition of the IgG glycome 8. Three of these 16 genes (IKZF1, BACH2, and HLA–DQA2) have previously also been identified as GWAS hits in systemic lupus erythematosus (SLE) 9, 10, 11. SLE is a chronic multiorgan autoimmune disease that predominantly affects women and certain ethnic groups including African/African Caribbean, American Indian, Asian Indian, Polynesian, and Chinese populations. Development of SLE includes multiple genetic and environmental risk factors that result in loss of tolerance and development of an autoreactive immune response, including autoimmune cells producing pathogenic autoantibodies, mainly of the IgG1 and IgG3 subclasses. The molecular mechanisms leading to SLE are unknown, but mice lacking α‐mannosidase II (αM‐II) develop an SLE‐like syndrome 12. Absence of αM‐II leads to the absence of complex branched N‐glycans 13. Studies by Green et al ruled out the notion that an ontogenic defect of the kidneys is involved in SLE pathogenesis and, by bone marrow reconstitution experiments, also excluded a role of bone marrow–derived cells. However, mice that are deficient in recombination‐activating genes and that therefore cannot generate functional B and T cells showed a more severe disease, indicating the importance of the immunosuppressive role of IgG in SLE. This idea was confirmed by the finding that IgG administration dampened SLE‐like symptoms 12. Aiming to investigate the potential role of IgG glycosylation in SLE, we analyzed IgG glycome composition in 3 cohorts of SLE patients and matching controls.

The evidence on the association between pioglitazone use and bladder cancer is contradictory, with many studies subject to allocation bias. The aim of our study was to examine the effect of exposure to pioglitazone on bladder cancer risk internationally across several cohorts. The potential for allocation bias was minimised by focusing on the cumulative effect of pioglitazone as the primary endpoint using a time-dependent approach. Prescription, cancer and mortality data from people with type 2 diabetes were obtained from six populations across the world (British Columbia, Finland, Manchester, Rotterdam, Scotland and the UK Clinical Practice Research Datalink). A discrete time failure analysis using Poisson regression was applied separately to data from each centre to model the effect of cumulative drug exposure on bladder cancer incidence, with time-dependent adjustment for ever use of pioglitazone. These were then pooled using fixed and random effects meta-regression. Data were collated on 1.01 million persons over 5.9 million person-years. There were 3,248 cases of incident bladder cancer, with 117 exposed cases and a median follow-up duration of 4.0 to 7.4 years. Overall, there was no evidence for any association between cumulative exposure to pioglitazone and bladder cancer in men (rate ratio [RR] per 100 days of cumulative exposure, 1.01; 95% CI 0.97, 1.06) or women (RR 1.04; 95% CI 0.97, 1.11) after adjustment for age, calendar year, diabetes duration, smoking and any ever use of pioglitazone. No association was observed between rosiglitazone and bladder cancer in men (RR 1.01; 95% CI 0.98, 1.03) or women (RR 1.00; 95% CI 0.94, 1.07). The cumulative use of pioglitazone or rosiglitazone was not associated with the incidence of bladder cancer in this large, pooled multipopulation analysis.

Aims/hypothesis South Asian individuals have an increased risk of diabetes compared with Europeans that is unexplained by obesity and traditional or established metabolic measures. Circulating amino acids (AAs) may provide additional explanatory insights. In a unique cohort of European and South Asian men, we compared cross-sectional associations between AAs, metabolic and obesity traits, and longitudinal associations with incident diabetes. Methods Nuclear magnetic spectroscopy was used to measure the baseline (1988–1991) levels of nine AAs in serum samples from a British population-based cohort of 1,279 European and 1,007 South Asian non-diabetic men aged 40–69 years. Follow-up was complete for 19 years in 801 European and 643 South Asian participants. Results The serum concentrations of isoleucine, phenylalanine, tyrosine and alanine were significantly higher in South Asian men, while cross-sectional correlations of AAs with glycaemia and insulin resistance were similar in the two ethnic groups. However, most AAs were less strongly correlated with measures of obesity in the South Asian participants. Diabetes developed in 227 (35%) South Asian and 113 (14%) European men. Stronger adverse associations were observed between branched chain and aromatic AAs and incident diabetes in South Asian men. Tyrosine was a particularly strong predictor of incident diabetes in South Asian individuals, even after adjustment for metabolic risk factors, including obesity and insulin resistance (adjusted OR for a 1 SD increment, 1.47, 95% CI 1.17,1.85, p = 0.001) compared with Europeans (OR 1.10, 0.87, 1.39, p = 0.4; p = 0.045 for ethnicity × tyrosine interaction). Conclusions/interpretation Branched chain and aromatic AAs, particularly tyrosine, may be a focus for identifying novel aetiological mechanisms and potential treatment targets for diabetes in South Asian populations and may contribute to their excess risk of diabetes. Electronic supplementary material The online version of this article (doi:10.1007/s00125-015-3517-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Genome-wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed-sample data, but few methods have been proposed for the analysis of genotype-by-environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family-based designs. We propose a novel method—generalized least squares (GLX)—to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran-Armitage trend test, and the W QLS and M QLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome-wide interaction with insecticide exposure—rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.

Exploration of the human genome presents new challenges and opportunities for epidemiological research. Although the case-control design is quicker and cheaper for study of associations between genotype and risk of disease than the cohort design, cohort studies have been recommended because they can be used to study gene-environment interactions. Although the scientific relevance of statistical interaction is pertinent, the main disadvantage of the case-control design-susceptibility to bias when estimating effects of exposures that are measured retrospectively-does not necessarily apply when studying statistical interaction between genotype and environmental exposure. Because correctly designed genetic association studies are equivalent to randomised comparisons between genotypes, conclusions about cause can be drawn from genetic associations even when the risk ratio is modest. For adequate statistical power to detect such modest risk ratios, the case-control design is more feasible than the cohort design.