Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores.

In most of the environment–disease relationships, the models were well calibrated, with the calibration coefficients being nominally significant in 8 and 7 out of 8 relationships in the UKB and BBJ, respectively ( P < 0.05).

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