Comorbidity patterns, family history and breast cancer risk: a latent class analysis

Background Limited evidence exists on how the presence of multiple conditions affects breast cancer (BC) risk. Methods We used data from a network hospital-based case-control study conducted in Italy and Switzerland, including 3034 BC cases and 3392 controls. Comorbidity patterns were identified using latent class analysis on a set of specific health conditions/diseases. A multiple logistic regression model was used to derive ORs and the corresponding 95% CIs for BC according to the patterns, adjusting for several covariates. A second model was fitted including an additional effect of FH on the comorbidity patterns. Results With respect to the 'healthy' pattern, the 'metabolic disorders' one reported an OR of 1.23 (95% CI 1.02 to 1.49) and the 'breast diseases' an OR of 1.86 (95% CI 1.23 to 2.83). The remaining two patterns reported an inverse association with BC, with ORs of 0.77, significant only for the 'hysterectomy, uterine fibroids and bilateral ovariectomy'. In the second model, FH was associated with an increased risk of the 'breast diseases' pattern (OR=4.09, 95% CI 2.48 to 6.74). Non-significant increased risk of the other patterns according to FH emerged. Conclusion We identified mutually exclusive patterns of comorbidity, confirming the unfavourable role of those related to metabolic and breast disorders on the risk of BC, and the protective effect of those related to common surgical procedures. FH reported an incremented risk of all the comorbidity patterns. Impact Identifying clusters of comorbidity in patients with BC may help understand their effects and enable clinicians and policymakers to better organise patient and healthcare management.

Automated summary

This study explores the impact of comorbidity patterns on breast cancer risk. It identifies the unfavorable role of metabolic and breast disorders, as well as the protective effect of common surgical procedures. Family history also plays a role in comorbidity patterns. The findings suggest the importance of understanding these clusters of comorbidity for better patient and healthcare management.