Background Validated tools to assess gender are essential for clinical and population research but remain scarce and unstandardized. Understanding how sociocultural gender interacts with biological sex in shaping disease risk is critical for precision medicine. We aimed to investigate the association of sex and gender with cardiovascular risk factors and common comorbidities, and to refine an existing composite Gender score in a Swiss cohort.Methods This multicenter prospective study conducted in three tertiary and one regional hospital included outpatients and inpatients with PCR-confirmed SARS-CoV-2 infection (n = 2,690; estimation and internal validation sets) and outpatients with periodontitis (n = 337; external validation set). Gender-related variables were acquired via questionnaire. Logistic regression models and propensity score matching were used to assess associations between sex, gender, and the prevalence of cardiometabolic and chronic health conditions.Results A total of 3,027 individuals (46.3% women; mean age 45 ± 17 years) were included. The composite Gender Score predicted biological sex with good accuracy (ROC 0.776-0.809). Biological sex was more strongly associated with most cardiometabolic risk factors, stroke, immune disorders, and bone diseases, whereas gender showed limited independent associations. For diabetes, female sex was inversely associated with diabetes prevalence, while a more feminine gender profile was positively associated. These opposing associations persisted after accounting for gender in matched analyses, illustrating that sex and gender may contribute differently to specific metabolic outcomes.Conclusion Sociocultural variables can accurately approximate biological sex and are differentially associated with health outcomes. In this cohort, biological sex explained most associations with cardiometabolic and chronic conditions, while gender effects were modest and condition-specific. The divergent associations observed for diabetes highlight that sex and gender are related but distinct constructs that should be considered jointly. Integrating both dimensions may improve future approaches to risk stratification, although these findings require confirmation in prospective and diverse populations.
Gebhard et al. (Mon,) studied this question.