In vitro activity of sulbactam-durlobactam against colistin-resistant and/or cefiderocol-non-susceptible, carbapenem-resistant Acinetobacter baumannii collected in U.S. hospitals.

The activity of a novel ß-lactamase inhibitor combination, sulbactam-durlobactam (SUL-DUR), was tested against 87 colistin-resistant and/or cefiderocol-non-susceptible carbapenem-resistant Acinetobacter baumannii clinical isolates collected from U.S. hospitals between 2017 and 2019. Among them, 89% and 97% were susceptible to SUL-DUR and imipenem plus SUL-DUR, with MIC50/MIC90 values of 2 µg/mL/8 µg/mL and 1 µg/mL/4 µg/mL, respectively. The presence of amino acid substitutions in penicillin-binding protein 3, including previously reported A515V or T526S, was associated with SUL-DUR non-susceptibility.

Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women.

PURPOSE: There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density. METHODS: We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity. RESULTS: Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041). CONCLUSION: Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.

Molecular characterization of clinical carbapenem-resistant Enterobacterales from Qatar.

One hundred forty-nine carbapenem-resistant Enterobacterales from clinical samples obtained between April 2014 and November 2017 were subjected to whole genome sequencing and multi-locus sequence typing. Klebsiella pneumoniae (81, 54.4%) and Escherichia coli (38, 25.5%) were the most common species. Genes encoding metallo-ß-lactamases were detected in 68 (45.8%) isolates, and OXA-48-like enzymes in 60 (40.3%). blaNDM-1 (45; 30.2%) and blaOXA-48 (29; 19.5%) were the most frequent. KPC-encoding genes were identified in 5 (3.6%) isolates. Most common sequence types were E. coli ST410 (8; 21.1%) and ST38 (7; 18.4%), and K. pneumoniae ST147 (13; 16%) and ST231 (7; 8.6%).

Mammographic Variation Measures, Breast Density, and Breast Cancer Risk.

OBJECTIVE. Our previous work showed that variation measures, which represent breast architecture derived from mammograms, were significantly associated with breast cancer. For replication purposes, we examined the association of three variation measures (variation [V], which is measured in the image domain, and P1 and p1 [a normalized version of P1], which are derived from restricted regions in the Fourier domain) with breast cancer risk in an independent population. We also compared these measures to volumetric density measures (volumetric percent density [VPD] and dense volume [DV]) from a commercial product. MATERIALS AND METHODS. We examined 514 patients with breast cancer and 1377 control patients from a screening practice who were matched for age, date of examination, mammography unit, facility, and state of residence. Spearman rank-order correlation was used to evaluate the monotonic association between measures. Breast cancer associations were estimated using conditional logistic regression, after adjustment for age and body mass index. Odds ratios were calculated per SD increment in mammographic measure. RESULTS. These variation measures were strongly correlated with VPD (correlation, 0.68-0.80) but not with DV (correlation, 0.31-0.48). Similar to previous findings, all variation measures were significantly associated with breast cancer (odds ratio per SD: 1.30 [95% CI, 1.16-1.46] for V, 1.55 [95% CI, 1.35-1.77] for P1, and 1.51 [95% CI, 1.33-1.72] for p1). Associations of volumetric density measures with breast cancer were similar (odds ratio per SD: 1.54 [95% CI, 1.33-1.78] for VPD and 1.34 [95% CI, 1.20-1.50] for DV). When DV was included with each variation measure in the same model, all measures retained significance. CONCLUSION. Variation measures were significantly associated with breast cancer risk (comparable to the volumetric density measures) but were independent of the DV.

Initiatives to Reduce Maternal Mortality and Severe Maternal Morbidity in the United States : A Narrative Review.

Maternal mortality and severe maternal morbidity are critical health issues in the United States, with unacceptably high rates and racial, ethnic, and geographic disparities. Various factors contribute to these adverse maternal health outcomes, ranging from patient-level to health system-level factors. Furthermore, a majority of pregnancy-related deaths are preventable. This review briefly describes the epidemiology of maternal mortality and severe maternal morbidity in the United States and discusses selected initiatives to reduce maternal mortality and severe maternal morbidity in the areas of data and surveillance; clinical workforce training and patient education; telehealth; comprehensive models and strategies; and clinical guidelines, protocols, and bundles. Related Health Resources and Services Administration initiatives are also described.

KPC-3-Producing Serratia marcescens Outbreak between Acute and Long-Term Care Facilities, Florida, USA.

We describe an outbreak caused by Serratia marcescens carrying blaKPC-3 that was sourced to a long-term care facility in Florida, USA. Whole-genome sequencing and plasmid profiling showed involvement of 3 clonal lineages of S. marcescens and 2 blaKPC-3-carrying plasmids. Determining the resistance mechanism is critical for timely implementation of infection control measures.

Empirically-derived synthetic populations to mitigate small sample sizes.

Limited sample sizes can lead to spurious modeling findings in biomedical research. The objective of this work is to present a new method to generate synthetic populations (SPs) from limited samples using matched case-control data (n = 180 pairs), considered as two separate limited samples. SPs were generated with multivariate kernel density estimations (KDEs) with unconstrained bandwidth matrices. We included four continuous variables and one categorical variable for each individual. Bandwidth matrices were determined with Differential Evolution (DE) optimization by covariance comparisons. Four synthetic samples (n = 180) were derived from their respective SPs. Similarity between observed samples with synthetic samples was compared assuming their empirical probability density functions (EPDFs) were similar. EPDFs were compared with the maximum mean discrepancy (MMD) test statistic based on the Kernel Two-Sample Test. To evaluate similarity within a modeling context, EPDFs derived from the Principal Component Analysis (PCA) scores and residuals were summarized with the distance to the model in X-space (DModX) as additional comparisons. Four SPs were generated from each sample. The probability of selecting a replicate when randomly constructing synthetic samples (n = 180) was infinitesimally small. MMD tests indicated that the observed sample EPDFs were similar to the respective synthetic EPDFs. For the samples, PCA scores and residuals did not deviate significantly when compared with their respective synthetic samples. The feasibility of this approach was demonstrated by producing synthetic data at the individual level, statistically similar to the observed samples. The methodology coupled KDE with DE optimization and deployed novel similarity metrics derived from PCA. This approach could be used to generate larger-sized synthetic samples. To develop this approach into a research tool for data exploration purposes, additional evaluation with increased dimensionality is required. Moreover, given a fully specified population, the degree to which individuals can be discarded while synthesizing the respective population accurately will be investigated. When these objectives are addressed, comparisons with other techniques such as bootstrapping will be required for a complete evaluation.

A Prospective Study of Acinetobacter baumannii Complex Isolates and Colistin Susceptibility Monitoring by Mass Spectrometry of Microbial Membrane Glycolipids.

Acinetobacter baumannii is a prevalent nosocomial pathogen with a high incidence of multidrug resistance. Treatment of infections due to this organism with colistin, a last-resort antibiotic of the polymyxin class, can result in the emergence of colistin-resistant strains. Colistin resistance primarily occurs via modifications of the terminal phosphate moieties of lipopolysaccharide-derived lipid A, which reduces overall membrane electronegativity. These modifications are readily identified by mass spectrometry (MS). In this study, we prospectively collected Acinetobacter baumannii complex clinical isolates from a hospital system in Pennsylvania over a 3-year period. All isolates were evaluated for colistin resistance using standard MIC testing by both agar dilution and broth microdilution, as well as genospecies identification and lipid A profiling using MS analyses. Overall, an excellent correlation between colistin susceptibility and resistance, determined by MIC testing, and the presence of a lipid A modification, determined by MS, was observed with a sensitivity of 92.9% and a specificity of 94.0%. Additionally, glycolipid profiling was able to differentiate A. baumannii complex organisms based on their membrane lipids. With the growth of MS use in clinical laboratories, a reliable MS-based glycolipid phenotyping method that identifies colistin resistance in A. baumannii complex clinical isolates, as well as other Gram-negative organisms, represents an alternative or complementary approach to existing diagnostics.

Radiation-induced autophagy potentiates immunotherapy of cancer via up-regulation of mannose 6-phosphate receptor on tumor cells in mice.

There is a significant body of evidence demonstrating that radiation therapy (XRT) enhances the effect of immune therapy. However, the precise mechanisms by which XRT potentiates the immunotherapy of cancer remain elusive. Here, we report that XRT potentiates the effect of immune therapy via induction of autophagy and resultant trafficking of mannose-6-phopsphate receptor (MPR) to the cell surface. Irradiation of different tumor cells caused substantial up-regulation of MPR on the cell surface in vitro and in vivo. Down-regulation of MPR in tumor cells with shRNA completely abrogated the combined effect of XRT and immunotherapy (CTLA4 antibody) in B16F10-bearing mice without changes in the tumor-specific responses of T cells. Radiation-induced MPR up-regulation was the result of redistribution of the receptor to the cell surface. This effect was caused by autophagy with redirection of MPR to autophagosomes in a clathrin-dependent manner. In autophagosomes, MPR lost its natural ligands, which resulted in subsequent trafficking of empty receptor(s) back to the surface. Together, our data demonstrated a novel mechanism by which XRT can enhance the effect of immunotherapy and the molecular mechanism of this process.

Neutrophil-mediated hypoxia drives pathogenic CD8+ T cell responses in cutaneous leishmaniasis.

Cutaneous leishmaniasis caused by Leishmania parasites exhibits a wide range of clinical manifestations. Although parasites influence disease severity, cytolytic CD8+ T cell responses mediate disease. Although these responses originate in the lymph node, we found that expression of the cytolytic effector molecule granzyme B was restricted to lesional CD8+ T cells in Leishmania-infected mice, suggesting that local cues within inflamed skin induced cytolytic function. Expression of Blimp-1 (Prdm1), a transcription factor necessary for cytolytic CD8+ T cell differentiation, was driven by hypoxia within the inflamed skin. Hypoxia was further enhanced by the recruitment of neutrophils that consumed oxygen to produce ROS and ultimately increased the hypoxic state and granzyme B expression in CD8+ T cells. Importantly, lesions from patients with cutaneous leishmaniasis exhibited hypoxia transcription signatures that correlated with the presence of neutrophils. Thus, targeting hypoxia-driven signals that support local differentiation of cytolytic CD8+ T cells may improve the prognosis for patients with cutaneous leishmaniasis, as well as for other inflammatory skin diseases in which cytolytic CD8+ T cells contribute to pathogenesis.

Comparison of percent density from raw and processed full-field digital mammography data.

INTRODUCTION: Mammographic density has been established as a strong risk factor for breast cancer, primarily using digitized film mammograms. Full-field digital mammography (FFDM) is replacing film mammography, has different properties than film, and provides both raw and processed clinical display representation images. We evaluated and compared FFDM raw and processed breast density measures and their associations with breast cancer. METHODS: A case-control study of 180 cases and 180 controls matched by age, postmenopausal hormone use, and screening history was conducted. Mammograms were acquired from a General Electric Senographe 2000D FFDM unit. Percent density (PD) was assessed for each FFDM representation using the operator-assisted Cumulus method. Reproducibility within image type (n = 80) was assessed using Lin's concordance correlation coefficient (rc). Correlation of PD between image representations (n = 360) was evaluated using Pearson's correlation coefficient (r) on the continuous measures and the weighted kappa statistic (κ) for quartiles. Conditional logistic regression was used to estimate odds ratios (ORs) for the PD and breast cancer associations for both image representations with 95% confidence intervals. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminatory accuracy. RESULTS: Percent density from the two representations provided similar intra-reader reproducibility (rc= 0.92 for raw and rc= 0.87 for processed images) and was correlated (r = 0.82 and κ = 0.64). When controlling for body mass index, the associations of quartiles of PD with breast cancer and discriminatory accuracy were similar for the raw (OR: 1.0 (ref.), 2.6 (1.2 to 5.4), 3.1 (1.4 to 6.8), 4.7 (2.1 to 10.6); AUC = 0.63) and processed representations (OR: 1.0 (ref.), 2.2 (1.1 to 4.1), 2.2 (1.1 to 4.4), 3.1 (1.5 to 6.6); AUC = 0.64). CONCLUSIONS: Percent density measured with an operator-assisted method from raw and processed FFDM images is reproducible and correlated. Both percent density measures provide similar associations with breast cancer.

A comparison of calibration data from full field digital mammography units for breast density measurements.

BACKGROUND: Breast density is a significant breast cancer risk factor measured from mammograms. The most appropriate method for measuring breast density for risk applications is still under investigation. Calibration standardizes mammograms to account for acquisition technique differences prior to making breast density measurements. We evaluated whether a calibration methodology developed for an indirect x-ray conversion full field digital mammography (FFDM) technology applies to direct x-ray conversion FFDM systems. METHODS: Breast tissue equivalent (BTE) phantom images were used to establish calibration datasets for three similar direct x-ray conversion FFDM systems. The calibration dataset for each unit is a function of the target/filter combination, x-ray tube voltage, current × time (mAs), phantom height, and two detector fields of view (FOVs). Methods were investigated to reduce the amount of calibration data by restricting the height, mAs, and FOV sampling. Calibration accuracy was evaluated with mixture phantoms. We also compared both intra- and inter-system calibration characteristics and accuracy. RESULTS: Calibration methods developed previously apply to direct x-ray conversion systems with modification. Calibration accuracy was largely within the acceptable range of ± 4 standardized units from the ideal value over the entire acquisition parameter space for the direct conversion units. Acceptable calibration accuracy was maintained with a cubic-spline height interpolation, representing a modification to previous work. Calibration data is unit specific, can be acquired with the large FOV, and requires a minimum of one reference mAs sample. The mAs sampling, calibration accuracy, and the necessity for machine specific calibration data are common characteristics and in agreement with our previous work. CONCLUSION: The generality of our calibration approach was established under ideal conditions. Evaluation with patient data using breast cancer status as the endpoint is required to demonstrate that the approach produces a breast density measure associated with breast cancer.

Techniques to produce and evaluate realistic multivariate synthetic data.

Data modeling requires a sufficient sample size for reproducibility. A small sample size can inhibit model evaluation. A synthetic data generation technique addressing this small sample size problem is evaluated: from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; synthetic data can be generated from this class with univariate kernel density estimation (KDE); and synthetic samples are statistically like their respective samples. Three samples (n = 667) were investigated with 10 input variables (X). KDE was used to augment the sample size in X. Maps produced univariate normal variables in Y. Principal component analysis in Y produced uncorrelated variables in T, where the probability density functions were approximated as normal and characterized; synthetic data was generated with normally distributed univariate random variables in T. Reversing each step produced synthetic data in Y and X. All samples were approximately multivariate normal in Y, permitting the generation of synthetic data. Probability density function and covariance comparisons showed similarity between samples and synthetic samples. A class of samples has a latent normal characteristic. For such samples, this approach offers a solution to the small sample size problem. Further studies are required to understand this latent class.

Breast density analysis of digital breast tomosynthesis.

Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.

Breast Density Analysis Using Digital Breast Tomosynthesis.

We evaluated an automated percentage of breast density (BD) technique (PDa) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values. Volumetric measures were derived theoretically, and PDa was modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was investigated using the volume results. A matched case-control study (n = 426 pairs) was analyzed. Conditional logistic regression modeling, controlling body mass index and ethnicity, was used to estimate odds ratios (ORs) for each measure with 95% confidence intervals provided parenthetically. There were several significant findings: volumetric measure [OR = 1.43 (1.18, 1.72)], which produced an identical OR as the slice-mean measure as predicted; [OR =1.44 (1.18, 1.75)] when applied to the synthetic images; and mean of the pixel values (volume or 2D synthetic) [ORs ~ 1.31 (1.09, 1.57)]. PDa was modeled as 2nd degree polynomial (concave-down): its maximum value occurred at 0.41×(compressed breast thickness), which was similar across case-control groups, and was significant from this position [OR = 1.47 (1.21, 1.78)]. A standardized 2D synthetic image was produced, where each pixel value represents the percentage of BD above its location. The significant findings indicate the validity of the technique. Derivations supported by empirical analyses produced a new synthetic 2D standardized image technique. Ancillary to the objectives, the results provide evidence for understanding the percentage of BD measure applied to 2D mammograms. Notwithstanding the findings, the study design provides a template for investigating other measures such as texture.

Pathogenic CD8 T cell responses are driven by neutrophil-mediated hypoxia in cutaneous leishmaniasis.

Cutaneous leishmaniasis caused by Leishmania parasites exhibits a wide range of clinical manifestations. Although parasites influence disease severity, cytolytic CD8 T cell responses mediate disease. While these responses originate in the lymph node, we find that expression of the cytolytic effector molecule granzyme B is restricted to lesional CD8 T cells in Leishmania - infected mice, suggesting that local cues within inflamed skin induce cytolytic function. Expression of Blimp-1 ( Prdm1 ), a transcription factor necessary for cytolytic CD8 T cell differentiation, is driven by hypoxia within the inflamed skin. Hypoxia is further enhanced by the recruitment of neutrophils that consume oxygen to produce reactive oxygen species, ultimately increasing granzyme B expression in CD8 T cells. Importantly, lesions from cutaneous leishmaniasis patients exhibit hypoxia transcription signatures that correlate with the presence of neutrophils. Thus, targeting hypoxia-driven signals that support local differentiation of cytolytic CD8 T cells may improve the prognosis for patients with cutaneous leishmaniasis, as well as other inflammatory skin diseases where cytolytic CD8 T cells contribute to pathogenesis.

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data.

BACKGROUND: Statistical learning (SL) techniques can address non-linear relationships and small datasets but do not provide an output that has an epidemiologic interpretation. METHODS: A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer patients was used to evaluate an approach for using SL methods as a preprocessing step for survival analysis. A stochastic method of training a probabilistic neural network (PNN) was used with differential evolution (DE) optimization. Survival scores were derived stochastically by combining CVs with the PNN. Patients (n = 151) were dichotomized into favorable (n = 92) and unfavorable (n = 59) survival outcome groups. These PNN derived scores were used with logistic regression (LR) modeling to predict favorable survival outcome and were integrated into the survival analysis (i.e. Kaplan-Meier analysis and Cox regression). The hybrid modeling was compared with the respective modeling using raw CVs. The area under the receiver operating characteristic curve (Az) was used to compare model predictive capability. Odds ratios (ORs) and hazard ratios (HRs) were used to compare disease associations with 95% confidence intervals (CIs). RESULTS: The LR model with the best predictive capability gave Az = 0.703. While controlling for gender and tumor grade, the OR = 0.63 (CI: 0.43, 0.91) per standard deviation (SD) increase in age indicates increasing age confers unfavorable outcome. The hybrid LR model gave Az = 0.778 by combining age and tumor grade with the PNN and controlling for gender. The PNN score and age translate inversely with respect to risk. The OR = 0.27 (CI: 0.14, 0.53) per SD increase in PNN score indicates those patients with decreased score confer unfavorable outcome. The tumor grade adjusted hazard for patients above the median age compared with those below the median was HR = 1.78 (CI: 1.06, 3.02), whereas the hazard for those patients below the median PNN score compared to those above the median was HR = 4.0 (CI: 2.13, 7.14). CONCLUSION: We have provided preliminary evidence showing that the SL preprocessing may provide benefits in comparison with accepted approaches. The work will require further evaluation with varying datasets to confirm these findings.

In Vitro Evolution of Cefiderocol Resistance in an NDM-Producing Klebsiella pneumoniae Due to Functional Loss of CirA.

By serially exposing an NDM-producing Klebsiella pneumoniae clinical strain to cefiderocol, we obtained a mutant with cefiderocol MIC of >128 µg/ml. The mutant contained an early stop codon in the iron transporter gene cirA, and its complementation fully restored susceptibility. The cirA-deficient mutant was competed out by the parental strain in vitro, suggesting reduced fitness. IMPORTANCE Cefiderocol, a newly approved cephalosporin agent with an extensive spectrum of activity against Gram-negative bacteria, is a siderophore cephalosporin that utilizes iron transporters to access the bacterial periplasm. Loss of functional CirA, an iron transporter, has been associated with cefiderocol resistance. Here, we show that such genetic change can be selected under selective pressure and cause high-level cefiderocol resistance, but with a high fitness cost. Whether these resistant mutants can survive beyond selective pressure will inform stewardship of this agent in the clinic.

Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study.

Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.

Early-Life and Adult Anthropometrics in Relation to Mammographic Image Intensity Variation in the Nurses' Health Studies.

BACKGROUND: The V measure captures grayscale intensity variation on a mammogram and is positively associated with breast cancer risk, independent of percent mammographic density (PMD), an established marker of breast cancer risk. We examined whether anthropometrics are associated with V, independent of PMD. METHODS: The analysis included 1,700 premenopausal and 1,947 postmenopausal women without breast cancer within the Nurses' Health Study (NHS) and NHSII. Participants recalled their body fatness at ages 5, 10, and 20 years using a 9-level pictogram (level 1: most lean) and reported weight at age 18 years, current adult weight, and adult height. V was estimated by calculating standard deviation of pixels on screening mammograms. Linear mixed models were used to estimate beta coefficients (ß) and 95% confidence intervals (CI) for the relationships between anthropometric measures and V, adjusting for confounders and PMD. RESULTS: V and PMD were positively correlated (Spearman r = 0.60). Higher average body fatness at ages 5 to 10 years (level ≥ 4.5 vs. 1) was significantly associated with lower V in premenopausal (ß = -0.32; 95% CI, -0.48 to -0.16) and postmenopausal (ß = -0.24; 95% CI, -0.37 to -0.10) women, independent of current body mass index (BMI) and PMD. Similar inverse associations were observed with average body fatness at ages 10 to 20 years and BMI at age 18 years. Current BMI was inversely associated with V, but the associations were largely attenuated after adjustment for PMD. Height was not associated with V. CONCLUSIONS: Our data suggest that early-life body fatness may reflect lifelong impact on breast tissue architecture beyond breast density. However, further studies are needed to confirm the results. IMPACT: This study highlights strong inverse associations of early-life adiposity with mammographic image intensity variation.