Publications by authors named "Henrik Strangalies"
Article Synopsis
- Risk stratification is essential for identifying high-risk individuals and preventing diseases, and this study investigated using NMR spectroscopy-derived metabolomic profiles to assess risk for 24 common diseases, potentially improving beyond traditional clinical predictors.
- A neural network was trained on data from nearly 118,000 participants, successfully linking metabolomic states to incident disease rates for most conditions studied, except breast cancer.
- The findings indicate that combining metabolomic states with demographic factors like age and sex can enhance predictions for several common diseases, proving clinically useful across different decision thresholds.
Background: In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, towards clinical application, the added value over clinical predictors later in life is crucial.
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