人口统计学、生活方式和临床因素与Dupuytren病存在的关联：来自生命线队列研究的结果

Q3 Medicine

Journal of Hand Surgery Global Online Pub Date : 2025-07-24 DOI:10.1016/j.jhsg.2025.100786

Michel F.N. Noordman BSc , Sophie A. Riesmeijer MD, PhD , Paul M.N. Werker MD, PhD , Ilja M. Nolte PhD

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引用次数: 0

摘要

许多危险因素与Dupuytren病（DD）有关，但其作用尚不清楚。因此，我们的目的是在生命线中调查多种危险因素与DD存在的关系，生命线是一项正在进行的前瞻性人群队列研究，于2006年启动，共有165,000名参与者。方法采用问卷调查的方式，采用医生自述的诊断方法，确定患者是否存在DD。人口统计学、生活方式和临床因素与DD之间的关系通过调整年龄、年龄和性别的逻辑回归进行分析。如果P <；.25，选取该变量纳入多变量logistic回归模型。将相关危险因素分组以克服多重共线性。采用逐步分层建模。采用对数似然比检验比较嵌套模型。采用55年对照进行敏感性分析，以评估研究结果的稳健性。结果总体而言，1320（2.1%）名生命线参与者报告患有DD。年龄、年龄和性别占观察到的DD风险变异性的7.8%。DD的其他危险因素包括（骨）关节炎、抗炎或抗风湿产品、高密度脂蛋白水平、甘油三酯水平、酒精使用、糖尿病和糖尿病药物，而肥胖的人体测量测量与DD呈负相关。它们的贡献相对较小，解释方差仅增加到8.76%。结论年龄和男性是增加DD风险的主要因素，但肥胖、（骨）关节炎、抗炎和抗风湿药物、高密度脂蛋白水平、甘油三酯水平、饮酒、糖尿病和糖尿病药物等人体测量指标也对DD的最终风险模型有重要影响。特别是关节相关因素值得关注，因为之前对这些风险因素的证据尚无定论。临床相关性我们的风险模型为DD的预防提供了机会。未来的研究应该阐明类风湿关节炎在DD中的作用。风险模型可能能够创建准确的DD个体风险概况，从而优化护理。

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Associations of Demographic, Lifestyle, and Clinical Factors With the Presence of Dupuytren Disease: Results from the Lifelines Cohort Study

Purpose

Many risk factors have been associated with Dupuytren disease (DD), but their contribution is still unclear. Therefore, we aimed to investigate the associations of a wide range of risk factors with the presence of DD in Lifelines, an ongoing prospective population-based cohort study with >165,000 participants initiated in 2006.

Methods

The presence of DD was determined through questionnaires by self-reported doctor’s diagnosis. The association between demographic, lifestyle, and clinical factors and DD was analyzed using logistic regression adjusted for age, age², and sex. If P < .25, the variable was selected for inclusion in multivariable logistic regression models. Related risk factors were grouped into blocks to overcome multicollinearity. Stepwise hierarchical modeling was applied. Nested models were compared using log-likelihood ratio tests. Sensitivity analysis using controls >55 years was performed to assess the robustness of the findings.

Results

Overall, 1,320 (2.1%) Lifelines participants reported to have DD. Age, age², and sex accounted for 7.8% of the variability observed in DD risk. Other risk factors for DD were (osteo)arthritis, anti-inflammatory or antirheumatic products, high-density lipoprotein levels, triglyceride levels, alcohol use, and diabetes and diabetes medication, while anthropometric measures of adiposity were negatively associated with DD. Their contribution was relatively small, with the explained variance increasing only to 8.76%.

Conclusions

Older age and male sex were the predominant factors increasing DD risk, but anthropometric measures of adiposity, (osteo)arthritis, anti-inflammatory and antirheumatic drugs, high-density lipoprotein levels, triglyceride levels, alcohol use, diabetes and diabetes medication also contributed significantly to the final risk model for DD. In particular, the joint related factors are of interest because previous evidence for these risk factors was inconclusive.

Clinical relevance

Our risk model presents an opportunity for prevention of DD. Future studies should elucidate the role of rheumatoid arthritis in DD. Risk models may possibly enable the creation of accurate individual risk profiles of DD leading to optimization of care.

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