Aug 2026· Frontiers in Oncology· Vol 16· 0 citations· 30 references
Medicine
TL;DR
Findings suggest that a comorbidity-pattern-based approach can improve the understanding of breast cancer risk and identify specific comorbidity or clinical conditions, particularly the metabolic and gynecologic–breast comorbidity patterns, could inform personalized risk stratification and targeted prevention strategies for breast cancer.
Abstract
Background Most studies to date have examined individual comorbidities in relation to breast cancer risk but have largely ignored that these conditions often cluster in the same person. Looking at comorbidity patterns rather than isolated diseases may therefore offer a better understanding of breast cancer risk. Methods In this case–control study, latent class analysis (LCA) was performed in the pooled case–control sample based on 10 comorbidities/conditions, including diabetes mellitus, hypertension, coronary heart disease, benign breast disease, breast lump, nipple discharge, history of gynecological tumors, severe lobular hyperplasia of the breast, papillomatous lesions of the breast, and dysfunctional uterine bleeding. Multivariable unconditional logistic regression was then used to examine the association between the identified latent classes and breast cancer risk and to test for potential interaction with body mass index (BMI). Results The LCA identified three comorbidity patterns in the pooled study sample: a low-comorbidity pattern, a metabolic comorbidity pattern, and a gynecologic–breast comorbidity pattern, accounting for 53.90%, 19.90%, and 26.20% of the study participants, respectively. Compared with the low-comorbidity pattern, both the metabolic comorbidity pattern (aOR = 2.55, 95% CI: 1.69–3.84) and the gynecologic–breast comorbidity pattern (aOR = 3.32, 95% CI: 2.25–4.89) were associated with higher breast cancer risk. Furthermore, a significant interaction was observed between BMI and the metabolic comorbidity pattern, suggesting that the association between this pattern and breast cancer risk varied across BMI categories. Conclusion These findings suggest that a comorbidity-pattern-based approach can improve the understanding of breast cancer risk. Identifying specific comorbidity or clinical conditions, particularly the metabolic and gynecologic–breast comorbidity patterns, could inform personalized risk stratification and targeted prevention strategies for breast cancer. While BMI modified the association between the metabolic comorbidity pattern and breast cancer risk, no evidence of BMI-related effect modification was observed for the gynecologic–breast comorbidity pattern.
The co-occurrence of diabetes and tuberculosis may simultaneously influence the risk of lung cancer, potentially through altered lipid metabolism, potentially through altered lipid metabolism.
S. T. Yau, Chi-tim Hung, Eman Yee-man Leung et al.· Cancer Epidemiology· 0 citations
This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity.
Daniel Adams, Amber N. Hurson, T. Ahearn et al.· Journal of the National Canc...· 0 citations
Evaluating the occurrence of new-onset CVD in women with breast cancer undergoing chemotherapy and identifying factors associated with increased risk of cardiovascular disease highlights the need for sustained cardiovascular surveillance in breast cancer survivors.
V. Dvorovy, L. Kováčová, M. Selvek et al.· European Heart Journal, Supp...· 0 citations
Higher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease, alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT).
L. Johns, Martina Brayley, R. Frost et al.· Breast Cancer Research· 0 citations
IMPORTANCE
Patients diagnosed with breast cancer (BCa) are at increased risk of multiple common diseases; however, the spectrum of these diseases and the contribution of inherited genetic susceptibility remain incompletely characterized.
METHODS
We evaluated 15 common diseases and tested their associations with BCa exposure and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N = 254,736). Analyses were performed using cause-specific Cox proportional hazards models within a full-cohort framework, with time-updated BCa status, delayed entry at study recruitment, and age as the underlying time scale.
RESULTS
After recruitment, incident BCa was diagnosed in 11,386 women (4.47%), including 2,742 (24.08%) with metastatic BCa. Patients with BCa had an increased risk of nine diseases spanning cardiovascular, metabolic, and neuropsychiatric domains (P<0.003, Bonferroni-corrected). Elevated risks were generally observed among patients with both early staged and advanced BCa. Inherited susceptibility further stratified disease risk, with the highest risks observed among patients with BCa with elevated disease-specific PRS. For example, compared with women without BCa, the hazard ratio (HR; 95% CI) for osteoporosis was 2.33 (2.15-2.52) among women with any BCa, 2.38 (2.18-2.59) among those with non-metastatic BCa, and 2.12 (1.78-2.54) among those with metastatic BCa; the HR was 4.48 (3.99-5.02) among patients with BCa in the highest quartile of osteoporosis-specific PRS (all P<0.001). In contrast, BCa was not significantly associated with risk of coronary artery disease.
CONCLUSION
BCa and inherited genetic susceptibility jointly contribute to increased risk of multiple common diseases, supporting the integration of genetic risk stratification into survivorship care.
Annabelle Ashworth, Zhu-Qing Shi, Huy Tran et al.· JNCI Cancer Spectrum· 0 citations
Pancreatic cancer is a highly lethal malignancy, and its incidence is increasing among younger individuals. Although smoking, alcohol use, and unhealthy diet are established risk factors, the cumulative impact of multiple lifestyle exposures on age at diagnosis remains unclear. This study aimed to estimate the combined burden of lifestyle and environmental risks associated with the timing of pancreatic cancer diagnosis.
We conducted a hospital-based retrospective case-only study including 134 patients diagnosed with pancreatic cancer between 2020 and 2025. Data on family history of pancreatic cancer, cigarette smoking, alcohol consumption, dietary patterns, exercise, and occupational chemical exposure were collected through structured interviews and medical record reviews. Cox proportional hazards models using age at diagnosis as the time scale were used to evaluate associations between exposures and earlier diagnosis. Lifestyle factors were integrated into a cumulative exposure index and analyzed using Cox models with adjustment for the remaining covariates.
The mean age at diagnosis was 64.8 years. A significant difference in age at diagnosis was observed across smoking categories, with current smokers having the youngest mean age, whereas no significant difference was observed across alcohol consumption categories. Patients with triple exposure had the youngest mean age at diagnosis (55.5 years), followed by those with double (63.5 years), single (64.8 years), and no (69.0 years) exposures. Compared with no exposure, single, double, and triple exposures were associated with progressively higher hazards of earlier diagnosis (HR = 1.79, 95% CI: 1.14–2.81,
p
= 0.011; HR = 2.97, 95% CI: 1.67–5.28,
p
< 0.001; and HR = 6.66, 95% CI: 3.35–13.22,
p
< 0.001, respectively).
Our findings indicate a significant association between a higher combined exposure index and earlier age at pancreatic cancer diagnosis. This exposure–response gradient suggests that a greater accumulation of adverse lifestyle exposures corresponds to earlier age at diagnosis among affected patients.
Not applicable.
Fu-Jen Lee, L. Chien, Jaw-Town Lin et al.· BMC Gastroenterology· 0 citations
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