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This study investigated whether genetic factors linked to cardiovascular-kidney-metabolic disease predict how strongly molecular features respond to weight-loss interventions. Analyzing four molecular layers (proteins, gene expression, DNA methylation, metabolites) across 13 different intervention studies including diet, bariatric surgery, and medication, researchers found that genetically-identified risk factors did not show consistently larger responses to treatment than other molecular features. The findings suggest genetic risk markers should not be interpreted as indicators of treatment responsiveness or disease reversibility.
Why it matters
This challenges the assumption that genetically-supported disease targets will be more responsive to interventions, which has implications for precision medicine approaches and drug development strategies. The results suggest that genetic risk factors may contribute to disease through mechanisms that are not easily reversed by current weight-loss treatments, potentially explaining why cardiovascular risk persists even after successful weight reduction.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background Substantial cardiovascular and kidney risk persists after successful weight loss. Whether features that genetics implicates in cardiovascular-kidney-metabolic (CKM) disease show larger short-term responses to weight-loss and cardiometabolic interventions than other features is unknown. Methods Across four molecular layers (proteome, transcriptome, methylome, metabolome), we applied layer-specific cis-Mendelian randomization with colocalization or summary-data shared-signal filtering against eight CKM genome-wide association studies. Within 13 omics-by-intervention analyses (diet, bariatric surgery, empagliflozin, behavioral weight loss), we compared response magnitude between nominated and adequately-instrumented non-nominated features using rank-based Cliff’s {delta} ; methylation was baseline-variance-matched and directional analyses exploratory. Results Here we show genetic nomination is not consistently associated with larger observed response magnitude: estimates are small (|{delta}| < 0.08 in all 13; median |{delta}| = 0.03), near zero (descriptive pooled {delta} = -0.003, 95% CI -0.016 to +0.010; I2 = 0%), none significant by label-permutation, though smaller proteomic and transcriptomic analyses remain compatible with modest differences. Nominated proteins and metabolites frequently change (48–71% respond, like comparison features), not universal non-response. Nominated CpG sites have lower baseline inter-individual variance (Mann–Whitney P = 1.7 x 10-6 to 4 x 10-3), and apparent methylation persistence attenuates after variance-matching. Conclusions Under the definitions and datasets studied, genetic target support does not consistently predict pharmacodynamic responsiveness and should not be read as a treatment-response or reversibility biomarker; baseline dynamic range should be assessed and controlled when comparing molecular change across selected features. Nominated features are not shown unchanged or to explain residual risk; same-subject longitudinal studies are needed to test prognostic or therapeutic value.