Chronic plaque psoriasis, a chronic inflammatory disease, stems from a complicated interaction of genes and environmental influences. This condition has two subtypes, distinguished by genetic and immunological characteristics. The first subtype has an early onset, which appears before age 40, accounting for 75% of cases (early-onset psoriasis EOP). The second subtype has a late onset. It appears after age 40 (late-onset psoriasis LOP).1 LOP, in contrast to the EOP, that is, heavily influenced by genetics, is more affected by environmental factors, lifestyle behaviours and often occurs alongside various metabolic-related comorbidities.2 Current evidence suggests that lifestyle factors, including obesity, smoking, physical activity and diet, significantly influence the likelihood of developing psoriasis. Shen et al.3 found that these lifestyle factors, rather than individual genetic predispositions, are more critical in psoriasis development. Despite the known association between healthy lifestyles and reduced psoriasis risk, the underlying metabolic mechanisms and their predictive value remain unclear. To specifically address this question, Zhou et al.,2 within this JEADV issue, are analysing serum metabolites connected to lifestyle and investigating their relationship with the likelihood of developing LOP. They also assess the metabolites' predictive ability. In brief, researchers investigated the connection between lifestyle-related serum metabolites and LOP, using data from a Biobank cohort. The study had two phases. Phase 1 employed linear regression and matched case–control analysis to identify metabolites linked to a healthy lifestyle. Then, Phase 2 explored the association between psoriasis and the metabolites identified in Phase 1, focusing on individuals initially without psoriasis who later developed the condition during the study period while accounting for the matching variables from the case–control analysis. Finally, they calculate population attributable fractions (PAF) for lifestyle and genetic risk factors. This article, employing advanced epidemiological, statistical and machine learning techniques, delves into the intricate relationships between lifestyle factors, metabolites and disease. In summary, by utilizing a large, well-characterized prospective cohort with standardized metabolomic profiling, this study identifies, for the first time, the metabolic mechanisms that explain how a healthy lifestyle lowers LOP risk. This study's outcomes highlight the necessity of constant metabolic monitoring and imply that early lifestyle interventions could potentially prevent LOP development. While this study offers a valuable and novel perspective, it is important to acknowledge its limitations. Although the UK Biobank data are robust, the voluntary nature of participation introduces selection bias. Individuals who choose to participate often demonstrate greater health and lifestyle awareness, potentially skewing the results. It has been noted that UK Biobank participants tend to be older, more often female, and live in less deprived areas compared to non-participants. They also report lower rates of obesity, smoking, alcohol consumption and self-reported health issues.4 This bias towards healthier individuals raises concerns about the representativeness of the cohort.5 Therefore, calculating PAF from this data may produce inaccurate results. Furthermore, the 14-year timeframe means that metabolites and lifestyle factors likely changed significantly, potentially impacting the study's findings. Interim assessments might have been more suitable. Finally, because the study did not involve an intervention, concluding the importance of early lifestyle intervention could be misleading. In summary, despite its innovative approach to predicting LOP risk by integrating lifestyle, metabolites and genetics, the study's conclusions should be interpreted with caution due to these limitations. In conclusion, predicting the risk of LOP by combining lifestyle factors, metabolites and genetics within a large cohort over a substantial period is a worthwhile goal. In this sense, this approach is innovative and offers a new perspective to the field. EA: received honoraria for lectures/presentations from UCB, AbbVie, Johnson & Johnson, Lilly and royalty from Springer Nature. ME: no conflict of interest. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.
Alpsoy et al. (Tue,) studied this question.