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Review 2: "Sex Differences in Symptomatology and Immune Profiles of Long COVID"

The reviewers found the study to be compelling, but they pointed out that more detail regarding the machine learning methods could help make this study reproducible and better understand the results.

Published onApr 17, 2024
Review 2: "Sex Differences in Symptomatology and Immune Profiles of Long COVID"
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key-enterThis Pub is a Review of
Sex differences in symptomatology and immune profiles of Long COVID
Sex differences in symptomatology and immune profiles of Long COVID
Description

Summary Strong sex differences in the frequencies and manifestations of Long COVID (LC) have been reported with females significantly more likely than males to present with LC after acute SARS-CoV-2 infection1–7. However, whether immunological traits underlying LC differ between sexes, and whether such differences explain the differential manifestations of LC symptomology is currently unknown. Here, we performed sex-based multi-dimensional immune-endocrine profiling of 165 individuals8 with and without LC in an exploratory, cross-sectional study to identify key immunological traits underlying biological sex differences in LC. We found that female and male participants with LC experienced different sets of symptoms, and distinct patterns of organ system involvement, with female participants suffering from a higher symptom burden. Machine learning approaches identified differential sets of immune features that characterized LC in females and males. Males with LC had decreased frequencies of monocyte and DC populations, elevated NK cells, and plasma cytokines including IL-8 and TGF-β-family members. Females with LC had increased frequencies of exhausted T cells, cytokine-secreting T cells, higher antibody reactivity to latent herpes viruses including EBV, HSV-2, and CMV, and lower testosterone levels than their control female counterparts. Testosterone levels were significantly associated with lower symptom burden in LC participants over sex designation. These findings suggest distinct immunological processes of LC in females and males and illuminate the crucial role of immune-endocrine dysregulation in sex-specific pathology.

RR:C19 Evidence Scale rating by reviewer:

  • Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.

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Review: The manuscript presents an exploratory, comprehensive study aimed at understanding the sex-based differences in symptomatology and immune profiles among individuals with Long COVID, highlighting the complex interplay between immune responses and hormone levels. The research team, leveraging a dataset from 165 participants, utilized machine learning approaches with extensive immune-endocrine profiling to study these differences. The authors observed that women with Long COVID have a higher symptom burden and demonstrated distinct immune signatures characterized by exhausted T cells and higher reactivity to latent herpesviruses. On the other hand, men with long COVID displayed different immunological profiles, with lower levels of monocytes and dendritic cells and higher levels of NK cells and certain plasma cytokines. This study stands out for its in-depth data analysis and the observation of the correlation between lower testosterone levels to a higher symptom burden across sexes, which may be a stronger predictor of the severity of Long COVID symptoms rather than simply biological sex. This suggests the potential of personalized hormone-based treatments to manage such disease complex. 

Overall, the paper’s conclusions are well-substantiated by the experimental and data analysis result, and the scientific rigor is high. The authors have well-described their methodology, from participant selection and data collection to statistical analysis. The use of a well-defined cohort, along with the exclusion criteria to mitigate confounding factors, strengthens the study's validity. Furthermore, the application of machine learning to analyze the immune-endocrine profiles adds novelty to the research. Some of the minor review comments are provided below:

  1. Details on Machine Learning Models: The rationale of using machine learning models (PLS-DA) rather than conventional statistical regression in analyzing complex, high-dimensional immune-endocrine profiles were not well-articulated. Greater details on how the models were employed (e.g., model parameters, validation methods) and data handling would enhance the manuscript's transparency and reproducibility. 

  2. Broader Implications: The manuscript could benefit from a discussion on the broader implications of the findings, particularly in relation to how these findings like testosterone levels might influence current treatment protocols for Long COVID, and what future clinical study or trail might be helpful to explore therapeutic interventions based on sex-specific immune differences.

The key take-home message for this paper is that the authors found testosterone levels, rather than simply biological sex, may be a stronger predictor of the severity of Long COVID symptoms, suggesting the potential of personalized hormone-based treatments to manage this disease complex.

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