Publications by authors named "M Pietzner"

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants.

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Understanding the genetic basis of routinely-acquired blood tests can provide insights into several aspects of human physiology. We report a genome-wide association study of 42 quantitative blood test traits defined using Electronic Healthcare Records (EHRs) of ~50,000 British Bangladeshi and British Pakistani adults. We demonstrate a causal variant within the PIEZO1 locus which was associated with alterations in red cell traits and glycated haemoglobin.

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Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors.

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Liver X receptor-α (LXRα) regulates cellular cholesterol abundance and potently activates hepatic lipogenesis. Here we show that at least 1 in 450 people in the UK Biobank carry functionally impaired mutations in LXRα, which is associated with biochemical evidence of hepatic dysfunction. On a western diet, male and female mice homozygous for a dominant negative mutation in LXRα have elevated liver cholesterol, diffuse cholesterol crystal accumulation and develop severe hepatitis and fibrosis, despite reduced liver triglyceride and no steatosis.

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Background: Variation in thyroid function parameters within the normal range has been observationally associated with adverse health outcomes. Whether those associations reflect causal effects is largely unknown.

Methods: We systematically tested associations between genetic differences in thyrotropin (TSH) and free thyroxine (FT4) within the normal range and more than 1100 diseases and more than 6000 molecular traits (metabolites and proteins) in three large population-based cohorts.

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