Variant connective tissue as a risk factor for Long COVID: a case-control study
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Abstract
Objectives
This study explored the extent to which two measures of joint hypermobility, a marker of variant connective tissue, predict the development of Long COVID after COVID-19 infection, and whether the severity of initial COVID-19 symptoms impacts this relationship.
Design
Retrospective online survey. The survey was co-designed with Long COVID patients and carers.
Setting
Respondents were recruited from representative online panels in the U.S. and U.K. maintained by the data firm Dynata in early 2024.
Participants
After data cleaning, the final dataset comprised 1,816 respondents, 352 (19.4%) who reported Long COVID and 1,464 (80.6%) who did not.
Primary and secondary outcome measures
The primary outcome was self-reported Long COVID, defined as experiencing symptoms related to a COVID-19 infection at least three months after the infection began. Participants were also asked to indicate whether they experienced no, mild or severe initial symptoms during each COVID-19 infection and to respond to nine questions that assessed different aspects of hypermobility: the Hakim and Grahame 5-part questionnaire (5PQ) and four additional co-produced questions. The 5PQ was used to identify individuals with generalized joint hypermobility (GJH). All nine questions were used to develop a measure of extreme hypermobility, based on the top 10 th percentile of hypermobility among respondents in each of six groups sorted by age and gender.
Results
In separate binomial logistic regressions of the dataset controlling for sex assigned at birth, age, number of infections, and number of vaccine doses, both GJH (OR 1.29, 95% CI 1.00 to 1.65) and extreme hypermobility (OR 2.12, 95% CI 1.43 to 3.16) were found to be predictive of Long COVID. Hypermobility influences the odds of getting Long COVID through two pathways. First, both GJH and extreme hypermobility increase the risk that individuals with no or moderate initial symptoms from a COVID-19 infection experience Long COVID. Second, both GJH and extreme hypermobility are significant predictors of developing severe initial symptoms from (a) COVID-19 infection(s), which is independently associated with increased Long COVID risk. A mediation analysis confirmed that extreme hypermobility influences the odds of developing Long COVID in part by increasing the likelihood that individuals experience severe initial symptoms from (a) COVID infection(s).
Conclusions
Both GJH and extreme hypermobility are significant risk factors for Long COVID. People with extreme hypermobility are at particularly high risk of developing Long COVID after an initial COVID-19 infection. Further research is needed to replicate these findings with other datasets and clarify the pathophysiology that explains why people with hypermobility may be at greater risk of Long COVID.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17995240.
Summary
The study examines whether connective tissue variation, measured through joint hypermobility, is associated with Long COVID. The authors used a retrospective online survey of 1,816 participants from the United States and United Kingdom. Of these, 352 reported Long COVID and 1,464 reported full recovery.
Hypermobility was assessed with the 5-part questionnaire and with a new measure that identifies the top 10 percent of respondents in age- and sex-adjusted hypermobility scores. Logistic regression models that controlled for sex, age, number of infections, and vaccination found that generalized joint hypermobility increased the odds of Long COVID (OR 1.29, 95 percent CI 1.00 to 1.65). …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17995240.
Summary
The study examines whether connective tissue variation, measured through joint hypermobility, is associated with Long COVID. The authors used a retrospective online survey of 1,816 participants from the United States and United Kingdom. Of these, 352 reported Long COVID and 1,464 reported full recovery.
Hypermobility was assessed with the 5-part questionnaire and with a new measure that identifies the top 10 percent of respondents in age- and sex-adjusted hypermobility scores. Logistic regression models that controlled for sex, age, number of infections, and vaccination found that generalized joint hypermobility increased the odds of Long COVID (OR 1.29, 95 percent CI 1.00 to 1.65). The new extreme hypermobility measure showed a stronger association (OR 2.12, 95 percent CI 1.43 to 3.16).
The authors conclude that connective tissue variation may be an underrecognized risk factor for Long COVID.
Discussion
Strengths
The topic is timely and relevant.
The sample size is large and includes participants from two countries.
The study evaluates both standard and newly developed hypermobility measures.
The statistical models control for several important variables.
The mediation analysis adds useful context about possible pathways.
Limitations
Both hypermobility and Long COVID status are self-reported. This increases the risk of misclassification and recall bias.
The extreme hypermobility measure is new and has not been validated outside this dataset.
The online survey design introduces selection bias, because individuals with chronic symptoms may be more likely to participate.
Hypermobility often overlaps with conditions such as dysautonomia, chronic pain, and chronic fatigue. These conditions were not controlled for and may confound the findings.
The cross-sectional design prevents strong conclusions about causality.
Proposed biological pathways are speculative and not directly tested.
The clinical relevance of the findings is uncertain, especially for the modest effect size of generalized hypermobility.
Results and Critical Appraisal
The association between generalized joint hypermobility and Long COVID is statistically marginal, showing that any increased risk in this group may be small or sensitive to residual confounding.
The stronger association for extreme hypermobility is more compelling and suggests a possible gradient of risk.
Sensitivity analyses show similar results, which supports the main findings within the constraint of the study design.
Mediation analysis is informative but should be interpreted cautiously because both exposure and outcomes are self-reported.
Conclusions and Recommendations
This study presents an interesting early finding that hypermobility traits may increase the risk of Long COVID. The work is valuable as exploratory research. To strengthen the manuscript, the authors should:
Expand the discussion of limitations related to self-report and sample selection.
Clarify how the extreme hypermobility metric was constructed and discuss plans for validation.
Consider additional confounders that are common in people with hypermobility, such as chronic pain and other pre-existing conditions.
Provide more detail on potential biological mechanisms, specifying how and what.
Outline future research using prospective designs and clinical hypermobility assessments.
Competing interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they used generative AI to come up with new ideas for their review.
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