Publications by authors named "L C Groop"
Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals.
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- This study examines the relationship between resting heart rate and cardiovascular diseases, identifying 493 genetic variants linked to this trait through a large-scale analysis of 835,465 individuals.
- It highlights the significance of higher genetically predicted resting heart rates, which are associated with an increased risk of dilated cardiomyopathy but lower risk for conditions like atrial fibrillation and ischemic strokes.
- The study also challenges previous findings on resting heart rate and all-cause mortality, suggesting earlier results may have been influenced by biases, ultimately enhancing our understanding of the biological implications of resting heart rate in cardiovascular health.
Article Synopsis
- Researchers examined genetic factors influencing insulin levels after a glucose challenge in over 55,000 people from different ancestry groups, identifying ten new genetic locations linked to postprandial insulin resistance.
- * They found that many of these loci share genetics with type 2 diabetes, suggesting a common underlying mechanism.
- * The study also highlighted nine candidate genes affecting GLUT4, a key glucose transporter, which plays an important role in glucose uptake during the post-meal state.
Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome.
View Article and Find Full Text PDFWe identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates.
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