塞梅尔维斯大学研究人员发现，类风湿性关节炎患者即使在炎症得到控制后仍可能持续经历疼痛和疲劳，这些症状可能由抑郁症、睡眠障碍、肥胖和吸烟等多重因素驱动，而非单纯源于炎症1。该研究团队在《自然风湿病学评论》和《柳叶刀风湿病学》上发表了相关成果，并开发了一个新模型帮助医生识别症状的根本原因，从而提供更具针对性的治疗方案1。
在匈牙利，类风湿性关节炎影响数万人，其中6-28%的患者属于"难治性"群体，即尽管接受治疗仍无法实现持久缓解1。研究人员开发的模型可在可测量的炎症指标改善但患者仍感到疼痛和疲劳时发挥"早期预警系统"的作用1。这一发现表明，现有的"达标治疗"模式需要调整，医生应在症状持续存在时深入调查慢性疼痛综合征、抑郁症、睡眠障碍或肥胖等潜在维持因素，以避免不必要的药物调整1。
该研究团队关于"难治性"疾病的概念已被引用超过1000次，现已应用于其他疾病的讨论1。研究人员计划利用人工智能的模式识别能力，进一步开发更有效的个性化治疗策略1。
Researchers at Semmelweis University have identified that continued pain and fatigue in rheumatoid arthritis patients may be driven by depression, sleep disorders, obesity, and smoking rather than inflammation alone.1 The team published their findings in Nature Reviews Rheumatology and The Lancet Rheumatology, developing a model to help physicians identify the underlying causes of symptoms and deliver more personalized treatment approaches while avoiding unnecessary medication adjustments.1
In Hungary, rheumatoid arthritis affects tens of thousands of people, with 6–28% of patients belonging to a "treatment-resistant" group that fails to achieve sustained remission despite receiving therapy.1 The researchers discovered that depression, smoking, obesity, and sleep problems may be associated with treatment-resistant rheumatoid arthritis and sustain symptom activity.1 The new model functions as an "early warning system" when measurable inflammatory markers improve but patients continue to experience pain and fatigue.1
The current "treat-to-target" approach can be adapted, with physicians encouraged to investigate underlying causes—such as chronic pain syndrome, depression, sleep disorders, or obesity—when symptoms persist despite treatment.1 The team's concept of "treatment-resistant" disease has been cited over 1,000 times and has since been applied to discussions of other conditions.1 Researchers plan to leverage artificial intelligence pattern recognition to develop more effective individualized treatment strategies.1
评论
还没有评论，欢迎留下第一条。