Background/Objectives: Prospective studies have reported associations between systemic inflammatory biomarkers and colorectal cancer (CRC) risk, but whether these reflect a causal role of inflammation or responses to preclinical disease remains uncertain. Methods: Using data from 383,850 UK Biobank participants (aged 40–69 years; recruited 2006–2010), we investigated potential reverse causality by assessing these associations according to time since biomarker assessment. Baseline systemic inflammatory markers, including C-reactive protein (CRP), albumin, and blood cell-derived ratios, were assessed. Multivariable Cox models estimated associations with CRC risk across predefined follow-up intervals. Results: Over a median follow-up of 11.8 years, 4996 participants developed CRC. Inflammatory biomarkers were associated with increased CRC risk, particularly during the early follow-up. Associations weakened substantially or disappeared when early follow-up was excluded. For example, CRP was associated with a 3.03-fold higher CRC risk (highest vs. lowest quartile; 95% CI: 2.06–4.45) within the 0–1-year interval, while no association was observed during the 3–5, 6–8, or >8-year intervals. Across complete follow-up, the association remained statistically significant but much weaker (HR: 1.17; 95% CI: 1.07–1.28). Similar patterns were observed for most other biomarkers, except the platelet-to-neutrophil ratio. Conclusions: Our results suggest that the associations between inflammatory biomarkers and CRC risk may partly reflect reverse causality arising from preclinical disease.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.