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OBJECTIVE: In this study we examine whether hospitalized coronavirus disease 2019 (COVID-19) pneumonia increases long-term cardiovascular mortality more than other hospitalized pneumonias in people with type 2 diabetes and aim to quantify the relative cardiovascular disease (CVD) mortality risks associated with COVID-19 versus non-COVID-19 pneumonia. RESEARCH DESIGN AND METHODS: With use of the SCI-Diabetes register, two cohorts were identified: individuals with type 2 diabetes in 2016 and at the 2020 pandemic onset. Hospital and death records were linked for determination of pneumonia exposure and CVD deaths. Poisson regression estimated rate ratios (RRs) for CVD death associated with both pneumonia types, with adjustment for confounders. Median follow-up durations were 1,461 days (2016 cohort) and 700 days (2020 cohort). RESULTS: The adjusted RR for CVD death following non-COVID-19 pneumonia was 5.51 (95% CI 5.31–5.71) prepandemic and 7.3 (6.86–7.76) during the pandemic. For COVID-19 pneumonia, the RR was 9.13 (8.55–9.75). Beyond 30 days post pneumonia, the RRs converged, to 4.24 (3.90–4.60) for non-COVID-19 and 3.35 (3.00–3.74) for COVID-19 pneumonia, consistent even with exclusion of prior CVD cases. CONCLUSIONS: Hospitalized pneumonia, irrespective of causal agent, marks an increased risk for CVD death immediately and over the long-term. COVID-19 pneumonia poses a higher CVD death risk than other pneumonias in the short-term, but this distinction diminishes over time. These insights underscore the need for including pneumonia in CVD risk assessments, with particular attention to the acute impact of COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]

During growth under selection, mutant types appear that are rare in unselected populations. Stress-induced mechanisms may cause these structures or selection may favor a series of standard events that modify common preexisting structures. One such mutation is the short junction (SJ) duplication with long repeats separated by short sequence elements: AB*(CD)*(CD)*E (* = a few bases). Another mutation type, described here, is the tandem inversion duplication (TID), where two copies of a parent sequence flank an inverse-order segment: AB(CD)(E'D'C'B')(CD)E. Both duplication types can amplify by unequal exchanges between direct repeats (CD), and both are rare in unselected cultures but common after prolonged selection for amplification. The observed TID junctions are asymmetric (aTIDs) and may arise from a symmetrical precursor (sTID)-ABCDE(E'D'C'B'A')ABCDE-when sequential deletions remove each palindromic junction. Alternatively, one deletion can remove both sTID junctions to generate an SJ duplication. It is proposed that sTID structures form frequently under all growth conditions, but are usually lost due to their instability and fitness cost. Selection for increased copy number helps retain the sTID and favors deletions that remodel junctions, improve fitness, and allow higher amplification. Growth improves with each step in formation of an SJ or aTID amplification, allowing selection to favor completion of the mutation process.

Objective: Whether advances in the management of type 1 diabetes are reducing rates of diabetic ketoacidosis (DKA) is unclear. We investigated time trends in DKA rates in a national cohort of individuals with type 1 diabetes monitored for 14 years, overall and by socioeconomic characteristics.Research Design and Methods: All individuals in Scotland with type 1 diabetes who were alive and at least 1 year old between 1 January 2004 and 31 December 2018 were identified using the national register (N = 37,939). DKA deaths and hospital admissions were obtained through linkage to Scottish national death and morbidity records. Bayesian regression was used to test for DKA time trends and association with risk markers, including socioeconomic deprivation.Results: There were 30,427 DKA admissions and 472 DKA deaths observed over 393,223 person-years at risk. DKA event rates increased over the study period (incidence rate ratio [IRR] per year 1.058 [95% credibility interval 1.054-1.061]). Males had lower rates than females (IRR male-to-female 0.814 [0.776-0.855]). DKA incidence rose in all age-groups other than 10- to 19-year-olds, in whom rates were the highest, but fell over the study. There was a large socioeconomic differential (IRR least-to-most deprived quintile 0.446 [0.406-0.490]), which increased during follow-up. Insulin pump use or completion of structured education were associated with lower DKA rates, and antidepressant and methadone prescription were associated with higher DKA rates.Conclusions: DKA incidence has risen since 2004, except in 10- to 19-year-olds. Of particular concern are the strong and widening socioeconomic disparities in DKA outcomes. Efforts to prevent DKA, especially in vulnerable groups, require strengthening. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: We aimed to report current rates of CVD in type 1 diabetes and to develop a CVD risk prediction tool for type 1 diabetes. Methods: A cohort of 27,527 people with type 1 diabetes without prior CVD was derived from the national register in Scotland. Incident CVD events during 199,552 person-years of follow-up were ascertained using hospital admissions and death registers. A Poisson regression model of CVD was developed and then validated in the Swedish National Diabetes Register (n = 33,183). We compared the percentage with a high 10 year CVD risk (i.e., ≥10%) using the model with the percentage eligible for statins using current guidelines by age. Results: The age-standardised rate of CVD per 100,000 person-years was 4070 and 3429 in men and women, respectively, with type 1 diabetes in Scotland, and 4014 and 3956 in men and women in Sweden. The final model was well calibrated (Hosmer–Lemeshow test p > 0.05) and included a further 22 terms over a base model of age, sex and diabetes duration (C statistic 0.82; 95% CI 0.81, 0.83). The model increased the base model C statistic from 0.66 to 0.80, from 0.60 to 0.75 and from 0.62 to 0.68 in those aged <40, 40–59 and ≥ 60 years, respectively (all p values <0.005). The model required minimal calibration in Sweden and had a C statistic of 0.85. Under current guidelines, >90% of those aged 20–39 years and 100% of those ≥40 years with type 1 diabetes were eligible for statins, but it was not until age 65 upwards that 100% had a modelled risk of CVD ≥10% in 10 years. Conclusions/interpretation: A prediction tool such as that developed here can provide individualised risk predictions. This 10 year CVD risk prediction tool could facilitate patient discussions regarding appropriate statin prescribing. Apart from 10 year risk, such discussions may also consider longer-term CVD risk, the potential for greater benefits from early vs later statin intervention, the potential impact on quality of life of an early CVD event and evidence on safety, all of which could influence treatment decisions, particularly in younger people with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Abstract Background The rapidly increasing speed with which genome sequence data can be generated will be accompanied by an exponential increase in the number of sequenced eukaryotes. With the increasing number of sequenced eukaryotic genomes comes a need for bioinformatic techniques to aid in functional annotation. Ideally, genome context based techniques such as proximity, fusion, and phylogenetic profiling, which have been so successful in prokaryotes, could be utilized in eukaryotes. Here we explore the application of phylogenetic profiling, a method that exploits the evolutionary co-occurrence of genes in the assignment of functional linkages, to eukaryotic genomes. Results In order to evaluate the performance of phylogenetic profiling in eukaryotes, we assessed the relative performance of commonly used profile construction techniques and genome compositions in predicting functional linkages in both prokaryotic and eukaryotic organisms. When predicting linkages in E. coli with a prokaryotic profile, the use of continuous values constructed from transformed BLAST bit-scores performed better than profiles composed of discretized E-values; the use of discretized E-values resulted in more accurate linkages when using S. cerevisiae as the query organism. Extending this analysis by incorporating several eukaryotic genomes in profiles containing a majority of prokaryotes resulted in similar overall accuracy, but with a surprising reduction in pathway diversity among the most significant linkages. Furthermore, the application of phylogenetic profiling using profiles composed of only eukaryotes resulted in the loss of the strong correlation between common KEGG pathway membership and profile similarity score. Profile construction methods, orthology definitions, ontology and domain complexity were explored as possible sources of the poor performance of eukaryotic profiles, but with no improvement in results. Conclusion Given the current set of completely sequenced eukaryotic organisms, phylogenetic profiling using profiles generated from any of the commonly used techniques was found to yield extremely poor results. These findings imply genome-specific requirements for constructing functionally relevant phylogenetic profiles, and suggest that differences in the evolutionary history between different kingdoms might generally limit the usefulness of phylogenetic profiling in eukaryotes.

Abstract Background New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. Results We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology. Conclusion VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at http://visant.bu.edu.

The article explores the advancements and benefits of insulin pump therapy, highlighting its role in enhancing diabetes management through precise insulin delivery and improved user experience. Topics discussed include innovations in insulin pump technology, challenges in managing therapy, and the future potential of integrated closed-loop systems.

Background: New techniques for determining relationships between biomolecules of all types -- genes, proteins, noncoding DNA, metabolites and small molecules -- are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. Results: We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology. Conclusion: VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at http://visant.bu.edu. [ABSTRACT FROM AUTHOR]

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<10-4. 4 DL outputs remained significant on adjusting for the screening program manual DR grading of the image (p ranging from 1.62x10-8 to 9.9x10-5). For predicting achieved eGFR we found that DL outputs improved test log-likelihood by 22.2 natural log units (equivalent to a p-value of < 10-9) increasing the R2, from 0.64 to 0.65. 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. [ABSTRACT FROM AUTHOR]

Phylogenetic profiling is now an effective computational method to detect functional associations between proteins. The method links two proteins in accordance with the similarity of their phyletic distributions across a set of genomes. While pair-wise linkage is useful, it misses correlations in higher order groups: triplets, quadruplets, and so on. Here we assess the probability of observing co-occurrence patterns of 3 binary profiles by chance and show that this probability is asymptotically the same as the mutual information in three profiles. We demonstrate the utility of the probability and the mutual information metrics in detecting overly represented triplets of orthologous proteins which could not be detected using pairwise profiles. These triplets serve as small building blocks, i.e. motifs in protein networks; they allow us to infer the function of uncharacterized members, and facilitate analysis of the local structure and global organization of the protein network. Our method is extendable to N-component clusters, and therefore serves as a general tool for high order protein function annotation.

A question of fundamental importance is the definition and identification of modules from microarray experiments. A wide variety of techniques have been used to gain insight into the elucidation of such modules. One problem, however, is the inability to directly compare results between the different data sets produced due to the inherent parameterizations of their approaches. We first aim to provide a mechanism by which different approaches to module finding can be directly compared. Moreover, the same approach can be used to internally compare the modules predicted by the same technique, but at different parameterizations. We apply this approach to analyze the flow of genes through modules at different module thresholds of the Barkai Signature method, thereby further resolving the modules into sets of co-expressed genes.

Problems of inference in systems biology are ideally reduced to formulations which can efficiently represent the features of interest. In the case of predicting gene regulation and pathway networks, an important feature which describes connected genes and proteins is the relationship between active and inactive forms, i.e. between the "on" and "off" states of the components. While not optimal at the limits of resolution, these logical relationships between discrete states can often yield good approximations of the behavior in larger complex systems, where exact representation of measurement relationships may be intractable. We explore techniques for extracting binary state variables from measurement of gene expression, and go on to describe robust measures for statistical significance and information that can be applied to many such types of data. We show how statistical strength and information are equivalent criteria in limiting cases, and demonstrate the application of these measures to simple systems of gene regulation.