Higher BMI, pre- or post-diagnosis, was associated with a higher risk of BCSM in older patients, independent of comorbidities and stage at diagnosis. Weight management should be discussed even with women aged 65 years or older to lower rates of BCSM.

Data from the Atlanta Care Center suggest that chronic MRSA and PA co-infection may be associated with increased rate of lung function decline and rate of intravenous antibiotics compared with patients with either pathogen alone.

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Background: Crown-like structures in breast adipose tissue (CLS-B), composed of necrotic adipocytes encircled by macrophages, are associated with obesity and hypothesized to worsen breast cancer prognosis; however, data are sparse, particularly in multi-racial populations. Methods:We assessed specimens for CLS-B from 174 African-American and 168 White women with stage I-III breast cancer treated by mastectomy. Benign breast tissue from an uninvolved quadrant was immunohistochemically stained for CD68 to determine CLS-B presence and density (per cm 2 of adipose tissue). Demographic and lifestyle factors, collected via medical record review, were analyzed for associations with CLS-B using logistic regression. Multivariable Cox proportional hazards models were used to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between CLS-B and overall (OS) or progression-free (PFS) survival.Results: Detection of any CLS-B was similar between African-American (32%) and White (29%) patients with no evidence of an association between race and CLS-B in multivariable models (OR = 0.82, 95% CI = 0.49-1.36). Detection of CLS-B was associated with obesity (OR = 4.73, 95% CI = 2.48-9.01) and age ≥ 60 years at diagnosis (OR = 1.78, 95% CI = 0.99-3.21). There was some evidence of associations with parity and current smoking status. Detection of CLS-B was not associated with OS (HR = 1.02, 95% CI = 0.55-1.87) or PFS (HR = 0.99, 95% CI = 0.59-1.67). Conclusions: Our results show a strong, positive association between BMI and CLS-B in non-tumor tissue similar to previous findings. Detection of CLS-B did not vary by race and was not associated with worse OS or PFS.

Background Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet. Objective This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores—the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015—and used multivariate methods to identify discriminatory metabolites for each pattern. Methods Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected “training” set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the “testing” set based on the use of receiver operating characteristic curves. Results Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED = 16; AHEI = 17; DASH = 13; HEI = 12). Spearman partial correlation analyses, applying a cut-point (|r| ≥ 0.15) and Bonferroni correction (P < 1.05 × 10−5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique. Conclusions Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.