Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers

AbstractAs weight-loss surgery is an effective treatment for the glycaemic control of type 2 diabetes in obese patients, yet not all patients benefit, it is valuable to find predictive factors for this diabetic remission. This will help elucidating possible mechanistic insights and form the basis for prioritising obese patients with dysregulated diabetes for surgery where diabetes remission is of interest. In this study, we combine both clinical and genomic factors using heuristic methods, informed by prior biological knowledge in order to rank factors that would have a role in predicting diabetes remission, and indeed in identifying patients who may have low likelihood in responding to bariatric surgery for improved glycaemic control. Genetic variants from the Illumina CardioMetaboChip were prioritised through single-association tests and then seeded a larger selection from protein–protein interaction networks. Artificial neural networks allowing nonlinear correlations were trained to discriminate patients with and without surgery-induced diabetes remission, and the importance of each clinical and genetic parameter was evaluated. The approach highlighted insulin treatment, baseline HbA1c levels, use of insulin-sensitising agents and baseline serum insulin levels, as the most informative variables with a decent internal validation performance (74% accuracy, area under the curve (AUC) 0.81). Adding information for the eight top-ranked single nucleotide polymorphisms (SNPs) significantly boosted classification performance to 84% accuracy (AUC 0.92). The eight SNPs mapped to eight genes — ABCA1, ARHGEF12, CTNNBL1, GLI3, PROK2, RYBP, SMUG1 and STXBP5 — three of which are known to have a role in insulin secretion, insulin sensitivity or obesity, but have not been indicated for diabetes remission after bariatric surgery before.</jats:p

Antihypertensive medication uses and serum ACE2 levels

The current coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with major respiratory failure where the old and those with an underlying chronic disease are at highest risk of mortality1. Several factors have been proposed that may be underlying the higher mortality rates in these high-risk groups. The most frequent comorbidities associated with COVID-19 related mortality are clinical hypertension and type 2 diabetes (T2D)2,3. Although lower survival can simply be attributed to the frailty of this population, it has been suggested that administration of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may affect the susceptibility to COVID-19 related outcomes by upregulating ACE24. It is well known that ACE2 is the cellular receptor that COVID-19 and other SARS coronaviruses bind to for entering the host cell5

Coding and regulatory variants are associated with serum protein levels and disease.

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases

Meals, Microbiota and Mental Health in Children and Adolescents (MMM-Study) : A protocol for an observational longitudinal case-control study

Funding Information: This manuscript was partially funded by grant European Union's Horizon 2020 research and innovation program, grant GEMMA 825033 as well as by the University of Iceland Research Fund and The Landspitali University Hospital Scientific Fund. Publisher Copyright: © 2022 Asbjornsdottir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Recent studies indicate that the interplay between diet, intestinal microbiota composition, and intestinal permeability can impact mental health. More than 10% of children and adolescents in Iceland suffer from mental disorders, and rates of psychotropics use are very high. The aim of this novel observational longitudinal case-control study, “Meals, Microbiota and Mental Health in Children and Adolescents (MMM-Study)” is to contribute to the promotion of treatment options for children and adolescents diagnosed with mental disorders through identification of patterns that may affect the symptoms. All children and adolescents, 5-15 years referred to the outpatient clinic of the Child and Adolescent Psychiatry Department at The National University Hospital in Reykjavik, Iceland, for one year (n≈150) will be invited to participate. There are two control groups, i.e., sex-matched children from the same postal area (n≈150) and same parent siblings (full siblings) in the same household close in age +/- 3 years (n<150). A three-day food diary, rating scales for mental health, and multiple questionnaires will be completed. Biosamples (fecal-, urine-, saliva-, blood samples, and buccal swab) will be collected and used for 16S rRNA gene amplicon sequencing of the oral and gut microbiome, measurements of serum factors, quantification of urine metabolites and host genotype, respectively. For longitudinal follow-up, data collection will be repeated after three years in the same groups. Integrative analysis of diet, gut microbiota, intestinal permeability, serum metabolites, and mental health will be conducted applying bioinformatics and systems biology approaches. Extensive population-based data of this quality has not been collected before, with collection repeated in three years' time, contributing to the high scientific value. The MMM-study follows the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) guidelines. Approval has been obtained from the Icelandic National Bioethics Committee, and the study is registered with Clinicaltrials.gov. The study will contribute to an improved understanding of the links between diet, gut microbiota and mental health in children through good quality study design by collecting information on multiple components, and a longitudinal approach. Furthermore, the study creates knowledge on possibilities for targeted and more personalized dietary and lifestyle interventions in subgroups.Peer reviewe

Coding and regulatory variants are associated with serum protein levels and disease.

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases

Author Correction:Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

In the version of this article initially published, Cristina Leal Rodríguez (Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark) was omitted from the author list. The error has been corrected in the HTML and PDF versions of the article</p

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes : an IMI-DIRECT study

Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.Peer reviewe