Lifestyle related serum metabolites associated with late onset psoriasis

[Fred]

This study investigated lifestyle related serum metabolites associated with late onset psoriasis risk and evaluate their predictive potential.

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Background:
Although healthy lifestyle behaviours are associated with a lower risk of psoriasis, the underlying metabolic mechanisms remain unclear.

Objectives:
To investigate lifestyle-related serum metabolites associated with late-onset psoriasis risk and evaluate their predictive potential.

Methods:
We analysed 190,692 participants (aged 38–73) from UK Biobank with complete data on lifestyle and serum metabolites. Healthy lifestyle was assessed based on diet, exercise, smoking and BMI. The association between lifestyle-related metabolites and late-onset psoriasis risk was identified by a sequential analytic strategy that combined the Cox regression and elastic net regression model. A machine learning model was developed to predict psoriasis risk using clinical features, polygenic risk scores (PRS) and critical metabolites.

Results:
During a median of 14.6 years of follow-up, 2114 incident late-onset psoriasis cases were documented among 186,812 participants. Ideal lifestyle factors were significantly associated with reduced disease burden, with BMI showing the highest population attributable fractions (PAF) of 24.1%. We identified 11 of 134 lifestyle-related metabolites that were significantly associated with the risk of late-onset psoriasis. These predominantly mapped to lipid and glucose metabolism pathways, comprising seven lipoprotein subclasses, two ketones, unsaturation degree and phenylalanine. The addition of these metabolites into clinical characteristics and PRS could significantly improve the performance of predicting the risk of late-onset psoriasis (AUC 0.860, 95% CI 0.857–0.863).

Conclusion:
Multiple lifestyle-related serum metabolites are associated with the incidence of late-onset psoriasis, and their integration with traditional clinical features and genetic susceptibility shows promise in enhancing the predictive accuracy of late-onset psoriasis using a machine learning–based model.

Source: onlinelibrary.wiley.com

*Funding: Various Chinese grants

[Caroline]

Absolutely very interesting and matching with my vision that our metabolic system is strongly involved in our disease.