Dear Editor,
We sincerely thank Drs. Daungsupawong and Wiwanitkit for their thoughtful and constructive comments on our article, “Utility of Plasma Myostatin as a Predictive Biomarker for Post‐Intensive Care Syndrome (PICS) in Patients with Sepsis.” Their insights provide an important opportunity to clarify several aspects of our study.
First, we acknowledge that this was a single‐center, exploratory study that predominantly included elderly patients. As stated in the manuscript, our findings should be regarded as hypothesis‐generating, and confirmation in larger, multicenter cohorts is necessary. Major limitations include the small sample size, potential selection bias, and limited adjustment for confounding variables.
Second, regarding the assessment of sarcopenia, skeletal muscle mass was evaluated using the psoas muscle index at the third lumbar vertebral level on computed tomography. Previous studies have demonstrated that computed tomography‐derived psoas muscle index correlates with whole‐body skeletal muscle mass and serves as a reasonable surrogate marker for sarcopenia in critically ill patients [1]. Sarcopenia was included as a covariate in our multivariable model, and lower plasma myostatin levels remained independently associated with long‐term functional impairment. Because of the retrospective study design, muscle assessment was limited to a single time point. Future prospective studies should incorporate longitudinal muscle evaluation using ultrasound, which has been shown to detect low muscularity at ICU admission [1]. Serial ultrasound measurements combined with myostatin kinetics may better capture dynamic muscle changes after sepsis.
Third, the predictive performance of plasma myostatin, with area‐under‐the‐curve values ranging from 0.70 to 0.76, indicates fair discrimination. According to commonly accepted criteria, an AUC of 0.70–0.79 reflects fair accuracy, 0.80–0.89 good accuracy, and ≥ 0.90 excellent accuracy [2]. Therefore, although statistically significant, our findings should be interpreted cautiously and require external validation before clinical application.
Finally, low plasma myostatin levels may reflect enhanced muscle catabolism or compensatory adaptation within the inflammatory–metabolic axis. In critically ill populations, lower circulating myostatin has been associated with systemic inflammation and unfavorable outcomes [3]. Experimental studies suggest that interleukin‐6 may modulate myostatin expression via the JAK/STAT3 signaling pathway, providing a mechanistic link between inflammation and muscle atrophy. Although myostatin (GDF‐8) and GDF‐11 share substantial sequence homology, they are distinct gene products with different tissue distributions. Myostatin is primarily expressed in skeletal muscle and the central nervous system, whereas GDF‐11 is more prominent in neural and cardiovascular tissues [4]. This difference may partly explain the stronger association observed between myostatin and PICS. Recent longitudinal studies further support a multimarker approach incorporating inflammatory, neuromuscular, and metabolic biomarkers [5]. Combining plasma myostatin with markers such as IL‐6 and neurofilament light chain may therefore provide a more comprehensive framework for early identification of PICS.
We appreciate the opportunity to respond to these comments and to further clarify the clinical and mechanistic implications of our findings.
Sincerely,
The Authors
Funding
This work was supported by JSPS KAKENHI Grant Number JP24K19491.
Ethics Statement
Ethics approval was obtained from the Institutional Review Board for Clinical Research at Kobe University Hospital (Approval No. B210116).
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors thank the ICU staff of Kobe University Hospital for their support.
Shirahata A., Nakanishi N., Ono Y., Inoue S., and Kotani J., “Response to “RE: Utility of Plasma Myostatin as a Predictive Biomarker for Post Intensive Care Syndrome in Patients With Sepsis”,” Acute Medicine & Surgery 13, no. 1 (2026): e70118, 10.1002/ams2.70118.
References
- 1. Nakanishi N., “Intensive Care Unit‐Acquired Muscle Atrophy and Weakness in Critical Illness: A Review of Long‐Term Recovery Strategies,” Acute and Critical Care 40 (2025): 361–372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Çorbacıoğlu Ş. K. and Aksel G., “Receiver Operating Characteristic Curve Analysis in Diagnostic Accuracy Studies: A Guide to Interpreting the Area Under the Curve Value,” Turkish Journal of Emergency Medicine 23 (2023): 195–198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Wirtz T. H., Loosen S. H., Buendgens L., et al., “Low Myostatin Serum Levels Are Associated With Poor Outcome in Critically Ill Patients,” Diagnostics 10 (2020): 574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Walker R. G., Poggioli T., Katsimpardi L., et al., “Biochemistry and Biology of GDF11 and Myostatin: Similarities, Differences, and Questions for Future Investigation,” Circulation Research 118 (2016): 1125–1141; discussion 42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Klawitter F., Laukien F., Fischer D. C., et al., “Longitudinal Assessment of Blood‐Based Inflammatory, Neuromuscular, and Neurovascular Biomarker Profiles in Intensive Care Unit‐Acquired Weakness: A Prospective Single‐Center Cohort Study,” Neurocritical Care 42 (2025): 118–130. [DOI] [PMC free article] [PubMed] [Google Scholar]