What to do with diabetes therapies when HbA1c lowering is inadequate:add, switch, or continue? A MASTERMIND study

This is the author accepted manuscript. The final version is available from BioMed Central via the DOI in this record.Background: It is unclear what to do when people with type 2 diabetes have had no or a limited glycemic response to a recently introduced medication. Intra-individual HbA1c variability can obscure true response. Some guidelines suggest stopping apparently ineffective therapy, but no studies have addressed this issue. Methods: In a retrospective cohort analysis using the UK Clinical Practice Research Datalink (CPRD), we assessed the outcome of 55,530 patients with type 2 diabetes starting their second or third non-insulin glucose lowering medication, with a baseline HbA1c >58mmol/mol (7.5%). For those with no HbA1c improvement or a limited response at 6 months (HbA1c fall <5.5mmol/mol [0.5%]) we compared HbA1c 12 months later in those who continued their treatment unchanged, switched to new treatment, or added new treatment. Results: An increase or a limited reduction in HbA1c was common, occurring in 21.9% (12,168/55,230), who had a mean HbA1c increase of 2.5mmol/mol (0.2%). After this limited response, continuing therapy was more frequent (n=9,308; 74%) than switching (n=1,177; 9%) or adding (n=2,163; 17%). Twelve months later, in those who switched medication HbA1c fell (-6.8mmol/mol [-0.6%], 95%CI -7.7, -6.0) only slightly more than those who continued unchanged (-5.1 mmol/mol [-0.5%], 95%CI -5.5, -4.8). Adding another new therapy was associated with a substantially better reduction (-12.4mmol/mol [-1.1%], 95%CI -13.1, -11.7). Propensity score matched subgroups demonstrated similar results. Conclusions: Where glucose lowering therapy does not appear effective on initial HbA1c testing, changing agents does not improve glycemic control. The initial agent should be continued with another therapy added.Medical Research Council (MRC)National Institute for Health Research (NIHR

Fetal and infant growth and the risk of obesity during early childhood: the Generation R Study.

OBJECTIVE: To examine whether infant growth rates are influenced by fetal growth characteristics and are associated with the risks of overweight and obesity in early childhood. DESIGN: This study was embedded in the Generation R Study, a population-based prospective cohort study from fetal life onward. METHODS: Fetal growth characteristics (femur length (FL) and estimated fetal weight (EFW)) were assessed in the second and third trimesters and at birth (length and weight). Infant peak weight velocity (PWV), peak height velocity (PHV), and body mass index at adiposity peak (BMIAP) were derived for 6267 infants with multiple height and weight measurements. RESULTS: EFW measured during the second trimester was positively associated with PWV and BMIAP during infancy. Subjects with a smaller weight gain between the third trimester and birth had a higher PWV. FL measured during the second trimester was positively associated with PHV. Gradual length gain between the second and third trimesters and between the third trimester and birth were associated with higher PHV. Compared with infants in the lowest quintile, the infants in the highest quintile of PWV had strongly increased risks of overweight/obesity at the age of 4 years (odds ratio (95% confidence interval): 15.01 (9.63, 23.38)). CONCLUSION: Fetal growth characteristics strongly influence infant growth rates. A higher PWV, which generally occurs in the first month after birth, was associated with an increased risk of overweight and obesity at 4 years of age. Longer follow-up studies are necessary to determine how fetal and infant growth patterns affect the risk of disease in later life

The disproportionate excess mortality risk of COVID-19 in younger people with diabetes warrants vaccination prioritisation

This is the final version. Available on open access from Springer via the DOI in this recordData availability: CHESS data cannot be shared publicly as it was collected by Public Health England as part of their statutory responsibilities, which allows them to process patient confidential data without explicit patient consent. Data utilised in this study were made available through an agreement between the University of Warwick and Public Health England. Individual requests for access to CHESS data are considered directly by Public Health England (contact via [email protected]).Diabetes U

Simple integrative preprocessing preserves what is shared in data sources

<p>Abstract</p> <p>Background</p> <p>Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting of measurements of the same entity but on different variables, and on tasks where source-specific variation is considered noisy or not interesting. Principal components analysis of all sources combined together is an obvious choice if it is not important to distinguish between data source-specific and shared variation. Canonical Correlation Analysis (CCA) focuses on mutual dependencies and discards source-specific "noise" but it produces a separate set of components for each source.</p> <p>Results</p> <p>It turns out that components given by CCA can be combined easily to produce a linear and hence fast and easily interpretable feature extraction method. The method fuses together several sources, such that the properties they share are preserved. Source-specific variation is discarded as uninteresting. We give the details and implement them in a software tool. The method is demonstrated on gene expression measurements in three case studies: classification of cell cycle regulated genes in yeast, identification of differentially expressed genes in leukemia, and defining stress response in yeast. The software package is available at <url>http://www.cis.hut.fi/projects/mi/software/drCCA/</url>.</p> <p>Conclusion</p> <p>We introduced a method for the task of data fusion for exploratory data analysis, when statistical dependencies between the sources and not within a source are interesting. The method uses canonical correlation analysis in a new way for dimensionality reduction, and inherits its good properties of being simple, fast, and easily interpretable as a linear projection.</p

The impact of population-level HbA1c screening on reducing diabetes diagnostic delay in middle-aged adults: a UK Biobank analysis

This is the final version. Available on open access from Springer via the DOI in this recordData availability: UK Biobank data are available through a procedure described at http://www.ukbiobank.ac.uk/using-the-resource/Aims/hypothesis Screening programmes can detect cases of undiagnosed diabetes earlier than symptomatic or incidental diagnosis. However, the improvement in time to diagnosis achieved by screening programmes compared with routine clinical care is unclear. We aimed to use the UK Biobank population-based study to provide the first population-based estimate of the reduction in time to diabetes diagnosis that could be achieved by HbA1c-based screening in middle-aged adults. Methods We studied UK Biobank participants aged 40–70 years with HbA1c measured at enrolment (but not fed back to participants/clinicians) and linked primary and secondary healthcare data (n=179,923) and identified those with a pre-existing diabetes diagnosis (n=13,077, 7.3%). Among the remaining participants (n=166,846) without a diabetes diagnosis, we used an elevated enrolment HbA1c level (≥48 mmol/mol [≥6.5%]) to identify those with undiagnosed diabetes. For this group, we used Kaplan–Meier analysis to assess the time between enrolment HbA1c measurement and subsequent clinical diabetes diagnosis up to 10 years, and Cox regression to identify clinical factors associated with delayed diabetes diagnosis. Results In total, 1.0% (1703/166,846) of participants without a diabetes diagnosis had undiagnosed diabetes based on calibrated HbA1c levels at UK Biobank enrolment, with a median HbA1c level of 51.3 mmol/mol (IQR 49.1–57.2) (6.8% [6.6–7.4]). These participants represented an additional 13.0% of diabetes cases in the study population relative to the 13,077 participants with a diabetes diagnosis. The median time to clinical diagnosis for those with undiagnosed diabetes was 2.2 years, with a median HbA1c at clinical diagnosis of 58.2 mmol/mol (IQR 51.0–80.0) (7.5% [6.8–9.5]). Female participants with lower HbA1c and BMI measurements at enrolment experienced the longest delay to clinical diagnosis. Conclusions/interpretation Our population-based study shows that HbA1c screening in adults aged 40–70 years can reduce the time to diabetes diagnosis by a median of 2.2 years compared with routine clinical care. The findings support the use of HbA1c screening to reduce the time for which individuals are living with undiagnosed diabetes.Research EnglandNational Institute for Health Research (NIHR)Wellcome Trus

A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available. © 2013 Capra et al

Type 2 Diabetes and COVID-19-Related Mortality in the Critical Care Setting: A National Cohort Study in England, March-July 2020

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this recordOBJECTIVE: To describe the relationship between type 2 diabetes and all-cause mortality among adults with coronavirus disease 2019 (COVID-19) in the critical care setting. RESEARCH DESIGN AND METHODS: This was a nationwide retrospective cohort study in people admitted to hospital in England with COVID-19 requiring admission to a high dependency unit (HDU) or intensive care unit (ICU) between 1 March 2020 and 27 July 2020. Cox proportional hazards models were used to estimate 30-day in-hospital all-cause mortality associated with type 2 diabetes, with adjustment for age, sex, ethnicity, obesity, and other major comorbidities (chronic respiratory disease, asthma, chronic heart disease, hypertension, immunosuppression, chronic neurological disease, chronic renal disease, and chronic liver disease). RESULTS: A total of 19,256 COVID-19-related HDU and ICU admissions were included in the primary analysis, including 13,809 HDU (mean age 70 years) and 5,447 ICU (mean age 58 years) admissions. Of those admitted, 3,524 (18.3%) had type 2 diabetes and 5,077 (26.4%) died during the study period. Patients with type 2 diabetes were at increased risk of death (adjusted hazard ratio [aHR] 1.23 [95% CI 1.14, 1.32]), and this result was consistent in HDU and ICU subsets. The relative mortality risk associated with type 2 diabetes decreased with higher age (age 18-49 years aHR 1.50 [95% CI 1.05, 2.15], age 50-64 years 1.29 [1.10, 1.51], and age ≥65 years 1.18 [1.09, 1.29], P value for age-type 2 diabetes interaction = 0.002). CONCLUSIONS: Type 2 diabetes may be an independent prognostic factor for survival in people with severe COVID-19 requiring critical care treatment, and in this setting the risk increase associated with type 2 diabetes is greatest in younger people.Research EnglandEngineering and Physical Sciences Research Council (EPSRC)University of WarwickNational Institute for Health Research (NIHR)Wellcome Trus

Using detergent to enhance detection sensitivity of African trypanosomes in human CSF and blood by Loop-Mediated Isothermal Amplification (LAMP)

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; The loop-mediated isothermal amplification (LAMP) assay, with its advantages of simplicity, rapidity and cost effectiveness, has evolved as one of the most sensitive and specific methods for the detection of a broad range of pathogenic microorganisms including African trypanosomes. While many LAMP-based assays are sufficiently sensitive to detect DNA well below the amount present in a single parasite, the detection limit of the assay is restricted by the number of parasites present in the volume of sample assayed; i.e. 1 per µL or 103 per mL. We hypothesized that clinical sensitivities that mimic analytical limits based on parasite DNA could be approached or even obtained by simply adding detergent to the samples prior to LAMP assay.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methodology/Principal Findings:&lt;/b&gt; For proof of principle we used two different LAMP assays capable of detecting 0.1 fg genomic DNA (0.001 parasite). The assay was tested on dilution series of intact bloodstream form Trypanosoma brucei rhodesiense in human cerebrospinal fluid (CSF) or blood with or without the addition of the detergent Triton X-100 and 60 min incubation at ambient temperature. With human CSF and in the absence of detergent, the LAMP detection limit for live intact parasites using 1 µL of CSF as the source of template was at best 103 parasites/mL. Remarkably, detergent enhanced LAMP assay reaches sensitivity about 100 to 1000-fold lower; i.e. 10 to 1 parasite/mL. Similar detergent-mediated increases in LAMP assay analytical sensitivity were also found using DNA extracted from filter paper cards containing blood pretreated with detergent before card spotting or blood samples spotted on detergent pretreated cards.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions/Significance:&lt;/b&gt; This simple procedure for the enhanced detection of live African trypanosomes in biological fluids by LAMP paves the way for the adaptation of LAMP for the economical and sensitive diagnosis of other protozoan parasites and microorganisms that cause diseases that plague the developing world.&lt;/p&gt

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P &lt; 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

attract: A Method for Identifying Core Pathways That Define Cellular Phenotypes

attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system