Publications by authors named "M Bruinenberg"
Article Synopsis
- - The study analyzed data from 703,901 individuals and identified 99 genetic loci related to physical activity levels and sedentary behavior, particularly focusing on leisure time activities and screen use.
- - Certain genes linked to sedentary behavior show heightened expression in skeletal muscle when influenced by resistance training, highlighting a connection between genetics and exercise.
- - The findings suggest that lower screen time and increased physical activity can positively impact health, but these effects may be influenced by factors like body mass index (BMI).
Blood eosinophil count is associated with a variety of common complex outcomes in epidemiological observation. The aim of this study was to explore the causal association between determined blood eosinophil count and 20 common complex outcomes (10 metabolic, 6 cardiac, and 4 pulmonary). Through Mendelian randomization, we investigated genetic evidence for the genetically determined eosinophil in association with each outcomes using individual-level LifeLines cohort data (n = 13,301), where a weighted eosinophil genetic risk score comprising five eosinophil associated variants was created.
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- Many genome-wide association studies (GWAS) often overlook environmental factors like smoking, which might affect the genetic analysis of obesity traits.
- This study analyzed GWAS data from over 240,000 participants, including smokers and nonsmokers, to find genetic links to body mass index (BMI) and body fat distribution.
- The researchers identified 23 new genetic loci related to obesity and 9 loci that interact with smoking, suggesting that smoking can influence genetic predispositions to body fat.
Despite the recent explosive rise in number of genetic markers for complex disease traits identified in genome-wide association studies, there is still a large gap between the known heritability of these traits and the part explained by these markers. To gauge whether this 'heritability gap' is closing, we first identified genome-wide significant SNPs from the literature and performed replication analyses for 32 highly relevant traits from five broad disease areas in 13 436 subjects of the Lifelines Cohort. Next, we calculated the variance explained by multi-SNP genetic risk scores (GRSs) for each trait, and compared it to their broad- and narrow-sense heritabilities captured by all common SNPs.
View Article and Find Full Text PDFThere is ongoing debate on the association between eosinophil count and diseases, as previous studies were inconsistent. We studied the relationship of eosinophil count with 22 complex metabolic, cardiac, and pulmonary traits and diseases. From the population-based LifeLines Cohort Study (N = 167,729), 13,301 individuals were included.
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