The circadian syndrome is a better predictor for psoriasis than the metabolic syndrome via an explainable machine learning method - the NHANES survey during 2005-2006 and 2009-2014.

ABSTRACT

Objective: To explore the association between circadian syndrome (CircS) and Metabolic Syndrome (MetS) with psoriasis. Compare the performance of MetS and CircS in predicting psoriasis. Methods: An observational study used data from the NHANES surveys conducted in 2005-2006 and 2009-2014. We constructed three multiple logistic regression models to investigate the relationship between MetS, CircS, and their components with psoriasis. The performance of MetS and CircS in predicting psoriasis was compared using five machine-learning algorithms, and the best-performing model was explained via SHAP. Then, bidirectional Mendelian randomization analyses with the inverse variance weighted (IVW) as the primary method were employed to determine the causal effects of each component. Result: A total of 9,531 participants were eligible for the study. Both the MetS (OR = 1.53, 95%CI 1.07-2.17, P = 0.02) and CircS (OR = 1.40, 95%CI 1.02-1.91, P = 0.039) positively correlated with psoriasis. Each CircS algorithmic model performs better than MetS, with Categorical Features+Gradient Boosting for CircS (the area under the precision-recall curve = 0.969) having the best prediction effect on psoriasis. Among the components of CircS, elevated blood pressure, depression symptoms, elevated waist circumference (WC), and short sleep contributed more to predicting psoriasis. Under the IVW methods, there were significant causal relationships between WC (OR = 1.52, 95%CI 1.34-1.73, P = 1.35e-10), hypertension (OR = 1.68, 95%CI 1.19-2.37, P = 0.003), depression symptoms (OR = 1.39, 95%CI 1.17-1.65, P = 1.51e-4), and short sleep (OR = 2.03, 95%CI 1.21-3.39, p = 0.007) with psoriasis risk. Conclusion: CircS demonstrated superior predictive ability for prevalent psoriasis compared to MetS, with elevated blood pressure, depression symptoms, and elevated WC contributing more to the prediction.