Intensity and timing of physical activity in relation to postmenopausal breast cancer risk: the prospective NIH-AARP diet and health study.

BACKGROUND: Despite strong evidence of an inverse association of physical activity with postmenopausal breast cancer risk, whether a certain intensity or time of life of physical activity is most effective for lowering breast cancer risk is not known. METHODS: In 118,899 postmenopausal women in the prospective NIH-AARP Diet and Health Study, we examined the relations of light and moderate-to-vigorous intensity physical activity during four periods of life ("historical": ages 15-18, 19-29, 35-39 years; "recent": past 10 years) to postmenopausal breast cancer risk. Physical activity was assessed by self-report at baseline, and 4287 incident breast cancers were identified over 6.6 years of follow-up. RESULTS: In age-adjusted and multivariate Cox regression models, >7 hours/week of moderate-to-vigorous activity during the past 10 years was associated with 16% reduced risk of postmenopausal breast cancer (RR:0.84; 95%CI:0.76,0.93) compared with inactivity. The association remained statistically significant after adjustment for BMI (RR:0.87; 95%CI:0.78,0.96). Neither moderate-to-vigorous activity during other periods of life nor light intensity activity during any period of life was related to breast cancer risk, and associations did not vary by tumor characteristics. CONCLUSION: A high level of recent, but not historical, physical activity of moderate-to-vigorous intensity is associated with reduced postmenopausal breast cancer risk. More precise recall of recent physical activity than activity in the distant past is one possible explanation for our findings.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Epidemiologic Risk Factors for In Situ and Invasive Breast Cancers Among Postmenopausal Women in the National Institutes of Health-AARP Diet and Health Study

Comparing risk factor associations between invasive breast cancers and possible precursors may further our understanding of factors related to initiation versus progression. Accordingly, among 190,325 postmenopausal participants in the National Institutes of Health-AARP Diet and Health Study (1995-2011), we compared the association between risk factors and incident ductal carcinoma in situ (DCIS; n = 1,453) with that of risk factors and invasive ductal carcinomas (n = 7,525); in addition, we compared the association between risk factors and lobular carcinoma in situ (LCIS; n = 186) with that of risk factors and invasive lobular carcinomas (n = 1,191). Hazard ratios and 95% confidence intervals were estimated from multivariable Cox proportional hazards regression models. We used case-only multivariable logistic regression to test for heterogeneity in associations. Younger age at menopause was associated with a higher risk of DCIS but lower risks of LCIS and invasive ductal carcinomas (P for heterogeneity < 0.01). Prior breast biopsy was more strongly associated with the risk of LCIS than the risk of DCIS (P for heterogeneity = 0.04). Increased risks associated with use of menopausal hormone therapy were stronger for LCIS than DCIS (P for heterogeneity = 0.03) and invasive lobular carcinomas (P for heterogeneity < 0.01). Associations were similar for race, age at menarche, age at first birth, family history, alcohol consumption, and smoking status, which suggests that most risk factor associations are similar for in situ and invasive cancers and may influence early stages of tumorigenesis. The differential associations observed for various factors may provide important clues for understanding the etiology of certain breast cancers

Relation of Serum Estrogen Metabolites with Terminal Duct Lobular Unit Involution Among Women Undergoing Diagnostic Image-Guided Breast Biopsy

Higher levels of circulating estrogens and estrogen metabolites (EMs) have been associated with higher breast cancer risk. In breast tissues, reduced levels of terminal duct lobular unit (TDLU) involution, as reflected by higher numbers of TDLUs and acini per TDLU, have also been linked to elevated breast cancer risk. However, it is unknown whether reduced TDLU involution mediates the risk associated with circulating EMs. In a cross-sectional analysis of 94 premenopausal and 92 postmenopausal women referred for clinical breast biopsy at an academic facility in Vermont, we examined the associations of 15 EMs, quantified using liquid chromatography-tandem mass spectrometry, with the number of TDLUs and acini count/TDLU using zero-inflated Poisson regression with a robust variance estimator and ordinal logistic regression models, respectively. All analyses were stratified by menopausal status and adjusted for potential confounders. Among premenopausal women, comparing the highest vs. the lowest tertiles, levels of unconjugated estradiol (risk ratio (RR) = 1.74, 95 % confidence interval (CI) = 1.06-2.87, p trend = 0.03), 2-hydroxyestrone (RR = 1.74, 95 % CI = 1.01-3.01, p trend = 0.04), and 4-hydroxyestrone (RR = 1.74, 95 % CI = 0.99-3.06, p trend = 0.04) were associated with significantly higher TDLU count. Among postmenopausal women, higher levels of estradiol (RR = 2.09, 95 % CI = 1.01-4.30, p trend = 0.04) and 16α-hydroxyestrone (RR = 2.27, 95 % CI = 1.29-3.99, p trend = 0.02) were significantly associated with higher TDLU count. Among postmenopausal women, higher levels of EMs, specifically conjugated estrone and 2- and 4-pathway catechols, were also associated with higher acini count/TDLU. Our data suggest that higher levels of serum EMs are generally associated with lower levels of TDLU involution

The ECCO-Darwin Data-Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean \u3cem\u3ep\u3c/em\u3eCO\u3csub\u3e2\u3c/sub\u3e and Air-Sea CO\u3csub\u3e2\u3c/sub\u3e Flux

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO2. To address this challenge, we have updated and improved ECCO-Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint-based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data-constrained ECCO physics, a Green\u27s function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO-Darwin exhibits broad-scale consistency with observed surface ocean pCO2 and air-sea CO2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO2 uptake occur in subpolar seasonally stratified biomes, where ECCO-Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO-Darwin has a time-mean global ocean CO2 sink (2.47 ± 0.50 Pg C year−1) and interannual variability that are more consistent with interpolation-based products. Compared to interpolation-based methods, ECCO-Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO-Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate-related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property-conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies

Remote sensing of sea surface glacial meltwater on the Antarctic Peninsula shelf

Glacial meltwater is an important environmental variable for ecosystem dynamics along the biologically productive Western Antarctic Peninsula (WAP) shelf. This region is experiencing rapid change, including increasing glacial meltwater discharge associated with the melting of land ice. To better understand the WAP environment and aid ecosystem forecasting, additional methods are needed for monitoring and quantifying glacial meltwater for this remote, sparsely sampled location. Prior studies showed that sea surface glacial meltwater (SSGM) has unique optical characteristics which may allow remote sensing detection via ocean color data. In this study, we develop a first-generation model for quantifying SSGM that can be applied to both spaceborne (MODIS-Aqua) and airborne (PRISM) ocean color platforms. In addition, the model was prepared and verified with one of the more comprehensive in-situ stable oxygen isotope datasets compiled for the WAP region. The SSGM model appears robust and provides accurate predictions of the fractional contribution of glacial meltwater to seawater when compared with in-situ data (r = 0.82, median absolute percent difference = 6.38%, median bias = −0.04), thus offering an additional novel method for quantifying and studying glacial meltwater in the WAP region

Attribution of space-time variability in global-ocean dissolved inorganic Carbon

The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean-Darwin ocean biogeochemistry state estimate to generate a global-ocean, data-constrained DIC budget and investigate how spatial and seasonal-to-interannual variability in three-dimensional circulation, air-sea CO2 flux, and biological processes have modulated the ocean sink for 1995–2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper-ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24-year DIC mass increase of 64 Pg C (2.7 Pg C year−1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2 (1.4 Pg C). In the upper 100 m, which stores roughly 13 (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year−1) and biological processes are the largest loss (8.6 Pg C year−1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997–1998 El Niño-Southern Oscillation causing the largest year-to-year change in upper-ocean DIC (2.1 Pg C). Our results provide a novel, data-constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink. © 2022. The Authors

The ECCO‐Darwin Data‐Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean pCO₂ and Air‐Sea CO₂ Flux

Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO₂. To address this challenge, we have updated and improved ECCO‐Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint‐based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data‐constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO‐Darwin exhibits broad‐scale consistency with observed surface ocean pCO₂ and air‐sea CO₂ flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO₂ uptake occur in subpolar seasonally stratified biomes, where ECCO‐Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO‐Darwin has a time‐mean global ocean CO₂ sink (2.47 ± 0.50 Pg C year⁻¹) and interannual variability that are more consistent with interpolation‐based products. Compared to interpolation‐based methods, ECCO‐Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO‐Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate‐related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property‐conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies