No Association Between Bone Mineral Density and Breast Arterial Calcification Among Postmenopausal Women.

CONTEXT: The association between bone mineral density (BMD) and breast arterial calcification (BAC) remains poorly understood and controversial. OBJECTIVE: The objective of this article is to examine the association between BMD and BAC in a large cohort of postmenopausal women undergoing routine mammography. DESIGN: A cross-sectional analysis of baseline data from a multiethnic cohort was performed. SETTING: The setting for this analysis is an integrated health care delivery system in Northern California in the United States. PATIENTS: A total of 1273 women age 60 to 79 years (mean age, 67 years) were recruited within 12 months of screening mammography. MAIN OUTCOME MEASURE: A BAC score (mg) was obtained from digital mammograms using a novel densitometry method. BAC presence was defined as a BAC score greater than 0 mg, and severe BAC as a BAC score greater than 20 mg. RESULTS: Overall, 53% of women had osteopenia and 21% had osteoporosis. The prevalence of BAC greater than 0 mg was 29%, 30%, and 29% among women with normal BMD, osteopenia, and osteoporosis, respectively (P = 0.98). The prevalence of BAC greater than 20 mg was 5%, 3%, and 5% among women with normal BMD, osteopenia and osteoporosis, respectively (P = .65). The odds ratios (ORs) of BAC greater than 0 mg vs BAC = 0 mg after multivariable adjustment were 1.09 (95% CI, 0.81-1.48; P = .54) for osteopenia and 0.99 (95% CI, 0.69-1.48; P = .98) for osteoporosis. The adjusted ORs for BAC greater than 20 mg vs BAC 20 mg or less were 1.03 (95% CI, 0.52-2.01; P = .93) for osteopenia and 1.89 (95 CI, 0.81-4.47; P = .14) for osteoporosis. CONCLUSION: Our findings do not support an association of either osteopenia or osteoporosis with BAC presence or severity among postmenopausal women.

Kidney function, proteinuria and breast arterial calcification in women without clinical cardiovascular disease: The MINERVA study.

BACKGROUND: Breast arterial calcification (BAC) may be a predictor of cardiovascular events and is highly prevalent in persons with end-stage kidney disease. However, few studies to date have examined the association between mild-to-moderate kidney function and proteinuria with BAC. METHODS: We prospectively enrolled women with no prior cardiovascular disease aged 60 to 79 years undergoing mammography screening at Kaiser Permanente Northern California between 10/24/2012 and 2/13/2015. Urine albumin-to-creatinine ratio (uACR), along with specific laboratory, demographic, and medical data, were measured at the baseline visit. Baseline estimated glomerular filtration rate (eGFR), medication history, and other comorbidities were identified from self-report and/or electronic medical records. BAC presence and gradation (mass) was measured by digital quantification of full-field mammograms. RESULTS: Among 3,507 participants, 24.5% were aged ≥70 years, 63.5% were white, 7.5% had eGFR <60 ml/min/1.73m2, with 85.7% having uACR ≥30 mg/g and 3.3% having uACR ≥300 mg/g. The prevalence of any measured BAC (>0 mg) was 27.9%. Neither uACR ≥30 mg/g nor uACR ≥300 were significantly associated with BAC in crude or multivariable analyses. Reduced eGFR was associated with BAC in univariate analyses (odds ratio 1.53, 95% CI: 1.18-2.00), but the association was no longer significant after adjustment for potential confounders. Results were similar in various sensitivity analyses that used different BAC thresholds or analytic approaches. CONCLUSIONS: Among women without cardiovascular disease undergoing mammography screening, reduced eGFR and albuminuria were not significantly associated with BAC.

Association of Breast Arterial Calcification Presence and Gradation with the Ankle-Brachial Index among Postmenopausal Women.

OBJECTIVE: To examine the association of breast arterial calcification (BAC) with the ankle brachial index (ABI), a sensitive metric of peripheral arterial disease (PAD), among postmenopausal women. Background: BAC is an emerging risk marker of cardiovascular disease (CVD). METHODS: MINERVA (MultIethNic study of brEast aRterial calcium gradation and cardioVAscular disease) is a cohort of women aged 60 to 79 at baseline (10/24/2012 - 2/13/2015) who were free of symptomatic CVD at baseline. The analytical sample comprised 3,800 women with available ABI, BAC assessment and covariates. We performed cross-sectional logistic regression analysis. RESULTS: 203 women (5.3%) had an ABI < 0.90 indicative of PAD, 26 (0.7%) had an ABI > 1.3 and 94% (n=3,571) had an ABI within normal limits. After adjustment for age, race/ethnicity, body mass index, smoking status, diabetes, hypertension, LDL-C, HDL-C, hs-CRP, estimated-GFR, urinary albumin/creatinine ratio, serum calcium, serum vitamin D and serum PTH, BAC presence remained significantly associated with ABI < 0.90 (OR=1.37; 95% CI, 1.01-1.87; p=0.04). After further adjustment for menopausal hormone therapy, parity and history of breast feeding, the association became marginally significant (OR=1.36; 95% CI, 0.99-1.85; p=0.05). No clear pattern of association was observed for increased gradation of BAC and ABI<0.9, and no significant associations were noted between BAC presence vs. absence or BAC gradation with ABI > 1.3. CONCLUSIONS: Among asymptomatic postmenopausal women, presence of BAC was associated with PAD independently of traditional risk factors. Additional prospective studies are required to establish the value of BAC for prediction of incident PAD in the general population.

Detecting Cardiovascular Disease from Mammograms With Deep Learning.

Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected inmammograms, can be useful riskmarkers associated with the disease. We investigate the feasibility of automated and accurate detection ofBACsinmammograms for risk assessment of coronary artery disease. We develop a 12-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixelwise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists. We evaluate the performance using a set of 840 full-field digital mammograms from 210 cases, using both free-responsereceiveroperatingcharacteristic (FROC) analysis and calcium mass quantification analysis. The FROC analysis shows that the deep learning approach achieves a level of detection similar to the human experts. The calcium mass quantification analysis shows that the inferred calcium mass is close to the ground truth, with a linear regression between them yielding a coefficient of determination of 96.24%. Taken together, these results suggest that deep learning can be used effectively to develop an automated system for BAC detection inmammograms to help identify and assess patients with cardiovascular risks.