Sex differences in symptomatology and immune profiles of Long COVID

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Abstract

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 infection 1–7 . 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 individuals 8 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.

Article activity feed

  1. Yujing Song

    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.

  2. Anshu Agrawal

    Review 1: "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.