rs11679492 - PLEKHM3
Magnitude 2.2 · 1 study on file
Reported associations
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Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning - Unknown journal (n.d.) · Unknown authors · PubMed 34128465
ABSTRACT: Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits
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Lifestyle context
Concrete actions anchored to the cited research. We do not prescribe, we describe.
Diet
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Refined carbohydrates and added sugars Moderate
Variant increases pancreatic fat; refined carbohydrates promote hepatic and pancreatic fat accumulation
Prioritize whole grains and legumes; limit added sugars to less than 25g daily
Exercise
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Aerobic exercise Moderate
Variant increases pancreatic fat accumulation; exercise reduces pancreatic fat and improves insulin sensitivity
150 minutes moderate-intensity aerobic activity per week
Screening
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Pancreatic fat and metabolic parameters Moderate
Variant is associated with increased pancreatic fat accumulation, a risk factor for metabolic disease
Annual fasting glucose, HbA1c, lipid panel; consider pancreatic imaging