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Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as “at risk” for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.

MicroRNAs are a class of small RNAs involved in post-transcriptional gene silencing with roles in disease and development. Many computational tools have been developed to identify novel microRNAs. However, there have been no attempts to predict cleavage sites for Drosha from primary sequence, or to identify cleavage sites using deep neural networks. Here, we present DeepMirCut, a recurrent neural network-based software that predicts both Dicer and Drosha cleavage sites. We built a microRNA primary sequence database including flanking genomic sequences for 34,713 microRNA annotations. We compare models trained on sequence data, sequence and secondary structure data, as well as input data with annotated structures. Our best model is able to predict cuts within closer average proximity than results reported for other methods. We show that a guanine nucleotide before and a uracil nucleotide after Dicer cleavage sites on the 3′ arm of the microRNA precursor had a positive effect on predictions while the opposite order (U before, G after) had a negative effect. Our analysis was also able to predict several positions where bulges had either positive or negative effects on the score. We expect that our approach and the data we have curated will enable several future studies.

Summary The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments., Graphical abstract, Highlights • Soft clustering based on 32 phenotypes identified 4 quantitative archetypes • These reflect different patterns of dysfunction across T2D etiological processes • The four archetypes are different in disease progression, GRSs, and omics signals • Some patients are dominated by one archetype, but many have etiological combinations, Wesolowska-Andersen et al. represent the clinical heterogeneity of newly diagnosed T2D as four quantitative archetype profiles reflecting patterns of dysfunction in disease etiological processes, rather than clustering individuals into categorical subgroups as attempted by others. The archetype profiles differ in genetic risk scores, disease progression, and circulating omics biomarkers.

Genetic studies have identified “favourable adiposity” variants - where the allele associated with higher adiposity is associated with lower risk of type 2 diabetes. We took a novel approach to find more of these alleles and find the underlying mechanisms. We first performed a genome wide association study (GWAS) of body fat percentage using 451000 individuals from UK Biobank. Second, we used published genetic data in a multivariate test to find alleles associated with higher adiposity but a “favourable” metabolic phenotype: higher HDL-C, adiponectin, sex hormone binding globulin, but lower triglycerides, fasting insulin and alanine transaminase. Third, we used abdominal imaging data from 4 studies to define the adiposity phenotype in more detail. We identified 620 variants associated with body fat percentage (p “Favourable adiposity” alleles associated with higher BMI but lower risk of type 2 diabetes are characterized by higher subcutaneous but lower liver fat. Disclosure H. Yaghootkar: None. Y. Ji: None. A.M. Yiorkas: None. F. Frau: None. D. Mook-Kanamori: None. R. de Mutsert: None. J. Tyrrell: None. S.E. Jones: None. R. Beaumont: None. A.R. Wood: None. L. Thomas: None. K.V. Allebrandt: None. N. Stefan: Consultant; Self; Merck Sharp & Dohme Corp.. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; AstraZeneca. Speaker's Bureau; Self; AstraZeneca, OmniaMed Ltd.. H. Staiger: None. E. Pearson: Speaker's Bureau; Self; Eli Lilly and Company. A.I. Blakemore: None. J.D. Bell: None. T.M. Frayling: None.

MicroRNAs are conserved, endogenous small RNAs with critical post-transcriptional regulatory functions throughout eukaryota, including prominent roles in development and disease. Despite much effort, microRNA annotations still contain errors and are incomplete due especially to challenges related to identifying valid miRs that have small numbers of reads, to properly locating hairpin precursors and to balancing precision and recall. Here, we present miRWoods, which solves these challenges using a duplex-focused precursor detection method and stacked random forests with specialized layers to detect mature and precursor microRNAs, and has been tuned to optimize the harmonic mean of precision and recall. We trained and tuned our discovery pipeline on data sets from the well-annotated human genome, and evaluated its performance on data from mouse. Compared to existing approaches, miRWoods better identifies precursor spans, and can balance sensitivity and specificity for an overall greater prediction accuracy, recalling an average of 10% more annotated microRNAs, and correctly predicts substantially more microRNAs with only one read. We apply this method to the under-annotated genomes of Felis catus (domestic cat) and Bos taurus (cow). We identified hundreds of novel microRNAs in small RNA sequencing data sets from muscle and skin from cat, from 10 tissues from cow and also from human and mouse cells. Our novel predictions include a microRNA in an intron of tyrosine kinase 2 (TYK2) that is present in both cat and cow, as well as a family of mirtrons with two instances in the human genome. Our predictions support a more expanded miR-2284 family in the bovine genome, a larger mir-548 family in the human genome, and a larger let-7 family in the feline genome., Author summary While the computational prediction of microRNA loci from high-throughput sequence data is well-studied, challenges persist in defining the minimum number of reads required for a locus to be evaluated, as well as in defining the precursor span. We present a new method, “miRWoods”, which has greater recall of known microRNAs, while also achieving as good or better overall performance. Our approach uses improved duplex-based methods of precursor detection and a pair of random forest layers that sensitively detect mature products and precursors. We trained our model on data from human, and confirmed that it can successfully be applied cross-species by evaluating predictions for the mouse genome. We then applied our approach to new sequencing data mapped to the under-annotated genomes of cow and cat. We were able to use miRWoods to improve annotations for cat and cow microRNAs, and found novel microRNAs in human and mouse, and identified errors in current annotations.

During the uploading of data for submission to the EudraCT results database, a discrepancy was identified. It was noted that the number of deaths per group was not consistent with the number in the final report and trial publication. This discrepancy was found to relate to two randomisation numbers. During the trial, the randomisation database had been held separately from the trial database, with manual transcription of randomisation numbers from the randomisation database to the trial database. Two randomisation numbers had been entered incorrectly into the trial database and, although this was documented at the time, the correction had not been made in the analysis data set. The two infants in question received the correct treatment in accordance with their allocation, but were analysed according to the wrong treatment group. Following the identification of this error, all analyses were repeated. It was confirmed that this error had a negligible impact on the study results. Furthermore, the two infants in question had not been included in the primary and secondary outcome analyses, as one had died and the other had withdrawn prior to the primary end-point assessment, so the key study outcomes remain unchanged. The only changes to the results are in the number of serious adverse events and minor changes to the data in demographics tables mostly affecting decimal points and the CONSORT diagram. Our interpretation of the study results remains unchanged.

Summary Background The function of local renin-angiotensin systems in skeletal muscle and adipose tissue remains largely unknown. A polymorphism of the human angiotensin converting enzyme (ACE) gene has been identified in which the insertion ( I ) rather than deletion ( D ) allele is associated with lower ACE activity in body tissues and increased response to some aspects of physical training. We studied the association between the ACE gene insertion or deletion polymorphism and changes in body composition related to an intensive exercise programme, to investigate the metabolic effects of local human renin-angiotensin systems. Methods We used three independent methods (bioimpedance, multiple skinfold-thickness assessment of whole-body composition, magnetic resonance imaging of the mid-thigh) to study changes in body composition in young male army recruits over 10 weeks of intensive physical training. Findings Participants with the II genotype had a greater anabolic response than those with one or more D alleles for fat mass (0·55 vs −0·20 kg, p=0·04 by bioimpedance) and non-fat mass (1·31 vs −0·15 kg, p=0·01 by bioimpedance). Changes in body morphology with training measured by the other methods were also dependent on genotype. Interpretation II genotype, as a marker of low ACE activity in body tissues, may conserve a positive energy balance during rigorous training, which suggests enhanced metabolic efficiency. This finding may explain some of the survival and functional benefits of therapy with ACE inhibitors.