RE: Utility of Plasma Myostatin as a Predictive Biomarker for Post‐Intensive Care Syndrome in Patients With Sepsis

Dear Editor,

The publication on “Utility of Plasma Myostatin as a Predictive Biomarker for Post‐Intensive Care Syndrome in Patients With Sepsis [1]” is interesting. The intriguing hypothesis that plasma myostatin levels may be a predictor of Post‐Intensive Care Syndrome (PICS) in patients with severe sepsis is brought up by this study. However, there are a number of limitations to the study that need to be taken into account. First, given the high mean patient age of 71 years, it is a single‐center prospective study, which restricts extrapolation to other groups. Confounding variables that have not been sufficiently controlled could include frailty or sarcopenia. Additionally, there is a considerable danger of statistical instability and large confidence intervals due to the small sample size of 77 patients, especially in logistic regression. This could result in “significant” results that are less definite than claimed.

In terms of statistics, even though multivariable logistic regression was employed, no information was given on control variables such baseline muscle mass, nutritional status, inflammation indicators, the severity of delirium, or drugs that impact the system. Neuromuscular levels are significant confounders of PICS results as well as myostatin dynamics. Additionally, even though the AUC values of 0.70 and 0.76 indicate “fair discrimination” prediction abilities, they are not yet appropriate for use as clinical biomarkers, particularly as the biomarker to be utilized should have an AUC of ≥ 0.80 or higher for dependability. Additionally, the sample was only measured three times, which might not accurately represent the sepsis response's myostatin kinetics, which exhibit a more complicated pattern than a linear change over a single day.

From a discussion standpoint, a number of issues need extensive discussion such as (1) Do lower myostatin levels indicate muscle catabolism or an adaptive response of the inflammatory–metabolic axis? (2) are the links shown to be causative or just epiphenomena of the severity of the disease? (3) for years, there has been discussion in the literature on whether myostatin measures can actually be differentiated from structurally identical proteins, such as GDF‐11, and (4) whether a more potent multibiomarker panel can be produced by combining myostatin with other biomarkers such as IL‐6, NGAL, or neurofilament light chain.

In the future, generalizability will be confirmed by multicenter trials with a variety of sample groups, especially in younger patients or those without sarcopenia. The biological consequences will also be clarified by longitudinal studies that monitor myostatin trajectories on a daily basis or connect them to imaging‐based muscle measurements like CT or ultrasound muscle mass. To improve predictive power, myostatin data could be combined with other variables using machine learning models. In order to translate biomarkers into specific intervention paths, it will be crucial to investigate whether restoring myostatin levels (via dietary therapy or rehabilitation) can truly lower the risk of PICS.

Funding

The authors have nothing to report.

Ethics Statement

The authors have nothing to report.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Daungsupawong H. and Wiwanitkit V., “ 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): e70119, 10.1002/ams2.70119.