1 paper2 findings
Random forest model
upin silico1
random forest model predicts cardiovascular-kidney-metabolic syndrome progression in silico (AUC 0.971, 95% CI 0.969-0.974).
“RF model demonstrated superior performance compared to alternative ML algorithms, achieving an accuracy of 91.1%, a sensitivity of 93.7%, a specificity of 87.4%, an F1 score of 92.6%, and an AUC of 0.971[95%CI(0.969-0.974)]”
In silicon = 12,376
AHA Scientific Sessions 2025 abstract3 Nov 2025
Dietary nutrient mixture
downin humans1OR 0.68
Dietary nutrient mixture lowers the risk of cardiovascular-kidney-metabolic syndrome progression in humans (OR 0.68, 95% CI 0.62-0.74).
“negative Weighted Quantile Sum regression index demonstrated a statistically significant inverse association between the dietary nutrient mixture and CKM syndrome progression (OR = 0.68, 95% CI: 0.62-0.74, P < 0.01)”
Humansn = 12,376
AHA Scientific Sessions 2025 abstract3 Nov 2025
Over a median 13.48 years, 2,567 UK Biobank participants progressed from stage 0 or 1 to stage 2 or 3 of the syndrome.Over a median 13.48 years, 2,567 UK Biobank participants progressed from stage 0 or 1 to stage 2 or 3 of the syndrome.
Each 100-unit increase in cumulative exposure was associated with an adjusted hazard ratio of 1.07 for cardiovascular disease over nine years, driven by earlier stages.Each 100-unit increase in cumulative exposure was associated with an adjusted hazard ratio of 1.07 for cardiovascular disease over nine years, driven by earlier stages.