Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study

Objective We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). Research Design and Methods 732 recently diagnosed T2D patients from the IMI-DIRECT study were extensively phenotyped over three years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS) and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. Results Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS, and increasing CLIm; visceral or liver fat, HDL-cholesterol and triglycerides had further independent, though weaker, roles (R2=0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from AUROC=0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS and CLIm was relatively stable (odds ratios 0.07 to 0.09). T2D polygenic risk score and baseline pancreatic fat, GLP-1, glucagon, diet, and physical activity did not show an independent role. Conclusions Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of T2D patients in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression

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

Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura

Publisher Copyright: © 2023, The Author(s).Migraine is a complex neurovascular disease with a range of severity and symptoms, yet mostly studied as one phenotype in genome-wide association studies (GWAS). Here we combine large GWAS datasets from six European populations to study the main migraine subtypes, migraine with aura (MA) and migraine without aura (MO). We identified four new MA-associated variants (in PRRT2, PALMD, ABO and LRRK2) and classified 13 MO-associated variants. Rare variants with large effects highlight three genes. A rare frameshift variant in brain-expressed PRRT2 confers large risk of MA and epilepsy, but not MO. A burden test of rare loss-of-function variants in SCN11A, encoding a neuron-expressed sodium channel with a key role in pain sensation, shows strong protection against migraine. Finally, a rare variant with cis-regulatory effects on KCNK5 confers large protection against migraine and brain aneurysms. Our findings offer new insights with therapeutic potential into the complex biology of migraine and its subtypes.Peer reviewe

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus.

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk

Showcase targets from Slieker et al, 2021 [11] and Abbasi et al, 2016 [16].

Showcase targets from Slieker et al, 2021 [11] and Abbasi et al, 2016 [16].</p

Database overview.

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.</div

Schematic representation of the BALDR pipeline.

The BALDR pipeline consists of three steps: Data integration, Query, and User report. In the Data integration step, data from multiple data sources is captured and consolidated into the BALDR database accessible to the BALDR source code. In the query step, the user selects up to 20 candidates using UniProt IDs or gene names. The targets are linked by UniProt IDs to the BALDR database and the BALDR source code runs using Rmarkdown. Lastly, a User report is produced containing five main sections: Functional information, Experimental evidence, Disease association, Mechanistic evidence, and Biomarker potential. Image credit: Openclipart.org.</p

Example of a BALDR report using nine biomarker candidates from Slieker <i>et al</i> [11] and Abbasi <i>et al</i>. [16].

Example of a BALDR report using nine biomarker candidates from Slieker et al [11] and Abbasi et al. [16].</p

Functional gene set terms from eight databases used in overrepresentation analysis.

Functional gene set terms from eight databases used in overrepresentation analysis.</p