Chen Zeru, Chen Haiwei, Chen Xiaotong, Chen Yuling, Wang Jintong, Ou Yuhua
The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510200, China.
Department of Clinical Medicine, The Second School of Clinical Medicine, Guangzhou Medical University, Guangzhou, 511436, China.
Lipids Health Dis. 2025 Jun 9;24(1):210. doi: 10.1186/s12944-025-02615-5.
Patients' quality of life is greatly impacted by psoriasis, a prevalent chronic inflammatory skin condition that is frequently linked to a number of systemic disorders. Recent research shows that obesity is a major risk factor for psoriasis. Since Relative Fat Mass (RFM), an innovative way to measure obesity, offers a more precise estimate of body fat percentage, this study aims to investigate the connection between RFM and psoriasis and its potential as a disease predictor.
The analysis included 6,006 people the National Health and Nutrition Examination Survey (NHANES) conducted between 2003 and 2006, 151 of whom had psoriasis. Weighted multivariable logistic regression, restricted cubic splines (RCS), subgroup analysis, and interaction tests were employed to assess the link between RFM and psoriasis. ROC curves were used to compare RFM with conventional measures of obesity (WWI, BRI). Furthermore, LASSO regression and multivariable regression based on AIC were used to create a psoriasis risk prediction model that included RFM and additional clinical factors.
RFM and psoriasis risk were revealed to be significantly positively correlated. The chance of developing psoriasis increased by 7% for every unit rise in RFM (95% CI: 1.03 to 1.12). RFM showed better predictive ability than conventional markers including BMI, WWI, and BRI (AUC = 0.573). The RFM-psoriasis relationship and diabetes status significantly interacted, with the association being weaker in diabetic individuals, according to subgroup analysis and interaction tests. Promising results were obtained from the created psoriasis risk prediction model that included RFM, age, total dietary sugar, education level, history of heart disease, and hypertension.
This research demonstrates that RFM outperforms traditional anthropometric methods in predicting risk. It also presents the initial evidence establishing a positive link between RFM and the likelihood of developing psoriasis.The psoriasis risk prediction model underscores RFM's effectiveness as a valuable approach in both clinical and public health domains, aiming to alleviate the impact of psoriasis-related issues by offering a practical instrument for early risk assessment and personalized clinical strategies.
银屑病是一种常见的慢性炎症性皮肤病，常与多种系统性疾病相关，极大地影响了患者的生活质量。最近的研究表明，肥胖是银屑病的主要危险因素。由于相对脂肪量（RFM）作为一种测量肥胖的创新方法，能更精确地估计体脂百分比，本研究旨在探讨RFM与银屑病之间的联系及其作为疾病预测指标的潜力。
分析纳入了2003年至2006年美国国家健康与营养检查调查（NHANES）中的6006人，其中151人患有银屑病。采用加权多变量逻辑回归、受限立方样条（RCS）、亚组分析和交互作用检验来评估RFM与银屑病之间的联系。使用ROC曲线将RFM与传统肥胖测量指标（体重指数、身体肥胖指数）进行比较。此外，使用基于AIC的LASSO回归和多变量回归来创建一个包含RFM和其他临床因素的银屑病风险预测模型。
RFM与银屑病风险呈显著正相关。RFM每增加一个单位，患银屑病的几率增加7%（95%置信区间：1.03至1.12）。RFM显示出比包括体重指数、体重指数、身体肥胖指数在内的传统指标更好的预测能力（AUC = 0.573）。亚组分析和交互作用检验表明，RFM与银屑病的关系和糖尿病状态存在显著交互作用，在糖尿病个体中这种关联较弱。从包含RFM、年龄、总膳食糖、教育水平、心脏病史和高血压的银屑病风险预测模型中获得了有前景的结果。
本研究表明，RFM在预测风险方面优于传统人体测量方法。它还提供了初步证据，证实RFM与患银屑病的可能性之间存在正相关关系。银屑病风险预测模型强调了RFM在临床和公共卫生领域作为一种有价值方法的有效性，旨在通过提供早期风险评估和个性化临床策略的实用工具来减轻银屑病相关问题的影响。
