To the Editor:

We read with interest the recent publication in the Journal of Cachexia, Sarcopenia and Muscle by Moradi et al. ‘Cachexia in preclinical rheumatoid arthritis: Longitudinal observational study of thigh magnetic resonance imaging from osteoarthritis initiative cohort’ [1], which sheds light on the emerging evidence of cachexia in early rheumatoid arthritis stages. As healthcare professionals, we are heartened by the authors' focus on this significant issue, which brings attention to critical concerns in autoimmune disease and musculoskeletal health. This paper aims to commend the study's contributions while discussing areas necessitating cautious interpretation and further exploration.

Firstly, the study uses propensity score matching (PS matching) for data analysis. In comparison, the generalized estimating equation (GEE) model might be more suitable for this study. GEE can capture and analyse time-varying effects and has advantages in analysing data on muscle composition and fat changes measured repeatedly at multiple time points in the same rheumatoid arthritis (RA) patients. It is suitable for studying trends over different time points and can provide robust parameter estimates.

Secondly, potential confounding variables. Although the article adjusts for several confounding factors (e.g., age, gender, body mass index and knee osteoarthritis status), there are still some potential confounding variables that may not have been fully considered. For example, participants' socioeconomic factors, physical activity levels, dietary habits, lifestyle factors (such as smoking, alcohol consumption and sleep quality) and other health conditions may significantly influence the progression and outcomes of RA [2]. Considering these factors can further enhance the robustness of the study results.

Thirdly, the impact of whole-body and thigh muscle and fat distribution on RA development can be further explored through detailed subgroup analysis. Such analysis can help understand the specific effects of changes in whole-body and regional muscle and fat distribution on overall metabolic health, providing a basis for personalized health management. Studies have shown that individuals with significant thigh muscle atrophy are more likely to experience increased disability and metabolic disorders [3]. While changes in muscle and fat distribution in other parts of the body also affect health, the specific impact of thigh muscle and fat distribution on overall health outcomes may differ from other regions, highlighting the importance of a comprehensive whole-body approach [4].

Despite the authors' recommendations, some reflection may be necessary for us as healthcare professionals. The study emphasizes the importance of early detection and intervention in the preclinical rheumatoid arthritis (Pre-RA) stage. The article highlights the importance of providing comprehensive medical support to these patients, addressing the multifaceted challenges posed by the disease through a range of support services. For Pre-RA patients, healthcare professionals can guide them in utilizing medical and social resources, including comprehensive health assessments, personalized diet and exercise plans, and regular body composition analysis and inflammation biomarker testing. Additionally, healthcare professionals can provide education and counselling services to help patients understand Pre-RA risk factors and prevention strategies, thereby improving their quality of life and long-term health outcomes.

Finally, we appreciate the authors' innovative and insightful work in assessing muscle and fat changes in rheumatoid arthritis patients, and we hope our comments will inspire further discussion and more comprehensive evaluations in rheumatoid arthritis research.

 致编辑：

我们饶有兴趣地阅读了 Moradi 等人最近在《恶病质、肌肉减少症和肌肉杂志》上发表的文章。 “临床前类风湿性关节炎的恶病质：来自骨关节炎倡议队列的大腿磁共振成像的纵向观察研究”[ 1 ]，该研究揭示了早期类风湿性关节炎阶段恶病质的新证据。作为医疗保健专业人员，我们对作者对这一重大问题的关注感到鼓舞，这引起了人们对自身免疫性疾病和肌肉骨骼健康的关键问题的关注。本文旨在赞扬该研究的贡献，同时讨论需要谨慎解释和进一步探索的领域。

首先，研究采用倾向得分匹配（PS匹配）进行数据分析。相比之下，广义估计方程（GEE）模型可能更适合本研究。 GEE 可以捕获和分析随时间变化的效应，并且在分析同一类风湿性关节炎 (RA) 患者多个时间点重复测量的肌肉成分和脂肪变化数据方面具有优势。它适合研究不同时间点的趋势，并可以提供稳健的参数估计。

其次，潜在的混杂变量。尽管文章调整了几个混杂因素（例如年龄、性别、体重指数和膝骨关节炎状况），但仍然存在一些可能尚未充分考虑的潜在混杂变量。例如，参与者的社会经济因素、身体活动水平、饮食习惯、生活方式因素（如吸烟、饮酒和睡眠质量）和其他健康状况可能会显着影响RA的进展和结果[ 2 ]。考虑这些因素可以进一步增强研究结果的稳健性。

第三，通过详细的亚组分析可以进一步探讨全身和大腿肌肉和脂肪分布对RA发展的影响。这样的分析可以帮助了解全身和局部肌肉和脂肪分布变化对整体代谢健康的具体影响，为个性化健康管理提供基础。研究表明，大腿肌肉明显萎缩的人更有可能出现残疾和代谢紊乱[ 3 ]。虽然身体其他部位的肌肉和脂肪分布的变化也会影响健康，但大腿肌肉和脂肪分布对整体健康结果的具体影响可能与其他部位不同，这凸显了全面的全身方法的重要性[ 4 ]。

尽管作者提出了建议，但作为医疗保健专业人员，我们可能需要进行一些反思。该研究强调了在临床前类风湿关节炎（Pre-RA）阶段早期发现和干预的重要性。该文章强调了为这些患者提供全面医疗支持的重要性，通过一系列支持服务解决该疾病带来的多方面挑战。对于类风湿性关节炎前期患者，医疗保健专业人员可以指导他们利用医疗和社会资源，包括全面的健康评估、个性化饮食和运动计划、定期身体成分分析和炎症生物标志物检测。此外，医疗保健专业人员可以提供教育和咨询服务，帮助患者了解 RA 前的危险因素和预防策略，从而提高他们的生活质量和长期健康结果。

最后，我们感谢作者在评估类风湿关节炎患者肌肉和脂肪变化方面所做的创新和富有洞察力的工作，我们希望我们的评论能够激发类风湿关节炎研究的进一步讨论和更全面的评估。