CONTEXT:Prior reports of plasma estradiol concentrations and their reference ranges in postmenopausal women identified marked variation, in part due to assay method and body mass index (BMI). OBJECTIVE:To develop an international reference range for plasma estradiol in postmenopausal women, to support accurate assessment of concentrations during systemic and local (e.g., vaginal) estradiol therapy, and to improve risk prediction for diseases, including osteoporosis, breast and endometrial cancers. METHODOLOGY:Estradiol concentrations were measured using primarily liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays in five international cohorts of community-dwelling, postmenopausal women of primarily European ancestry aged 38-100 years who were not using exogenous estrogens (n=7206). Measurements were harmonized to the Centers for Disease Control and Prevention (CDC) reference measurement procedure (RMP) by re-assaying a subset of samples for each cohort and generating recalibration equations. Reference intervals were determined for all women, non-obese women, and specific BMI categories. RESULTS:Estradiol concentrations from separate LC-MS/MS assays correlated closely with the CDC RMP, and harmonization minimized inter-assay variability. Estradiol concentrations correlated with BMI but not with chronological age. The reference range (2.5th-97.5th percentile) was 1.1-18.2 pg/mL (4.0 - 66.8 pmol/L) with median of 4.9 pg/mL (18.0 pmol/L) for all postmenopausal women and 1.1-12.5 pg/mL (4.0 - 45.9 pmol/L) with median of 4.1 pg/mL (15.1 pmol/L) for non-obese women (BMI<30). Reference ranges increased progressively with higher BMI categories. CONCLUSION:Cross-calibration of estradiol measurements with the CDC RP enabled the development of harmonized plasma estradiol reference ranges for postmenopausal women who were not using exogenous estrogens, suitable for future studies to establish clinical utility. Increasing obesity in postmenopausal women was associated with increasing estradiol concentration and BMI category-specific reference ranges.

Non-medical drivers of health (NMDH) impact the risks and outcomes of intimate partner violence (IPV). Here, we investigate the relationship between NMDH and radiological manifestations of IPV. This study analyzed female cases (N = 151) reporting IPV between 2013 and 2018 and controls (N = 146) who did not report IPV, each with at least one radiologic study and social work note. NMDH variables were collected, including age, race, language, primary care provider, social support, social services, housing stability, and area deprivation index. Radiology reports were reviewed to determine injuries and severity. Each patient received a cumulative NMDH index ranging from 0 (no NMDH risk factors) to 5 (maximal risk factors). Poisson and logistic regression were used to calculate adjusted incidence rate ratios (aIRR) and odds ratios (aOR) for imaging utilization and injuries with respect to NMDH risk factors. NMDH variables associated with IPV included lack of a primary care provider (p = 0.0034), lack of social support ( p < 0.0001), use of social services (p < 0.001), housing instability (p < 0.0001), and a higher NMDH index (p = 0.0002). Imaging utilization rates were not significantly different between patients with a higher versus lower NMDH index for cases (aIRR: 0.84, p = 0.24) or controls (aIRR: 0.72, p = 0.19). Among cases, a higher NMDH index was associated with increased odds of injury detection on radiologic studies (aOR: 1.78, p = 0.008). Multiple NMDH risk factors are associated with higher odds of IPV-related injuries visible on imaging. Integrating NMDH variables into clinical risk stratification tools may facilitate earlier identification and intervention for patients who experience IPV.

Stroma may play an important role in breast carcinogenesis. There is no data on the expression of stromal markers αSMA, FAP, MMP14, TNC, and s100a6 in the breast tissue of cancer-free women. We compared the multiplex immunofluorescence (IF) expression assessment for these markers in normal terminal duct-lobular units (TDLUs) by an expert pathologist with the automated image analysis results and assessed the homogeneity of the markers across multiple cores pertaining to each woman. We included 73 cancer-free women with biopsy-confirmed benign breast disease in the Nurses' Health Study (NHS) and NHSII cohorts. IF was conducted with commercial antibodies (αSMA: 1:400 dilution; FAP: 1:50; MMP14: 1:150; TNC: 1:200; s100a6: 1:300). For each tissue microarray core, the percent positivity was assessed by the pathologist and inForm v2.6.0. Using the pathologist scores as the gold standard, correlations between pathologist and inForm scores were evaluated with Spearman correlation (for categorical positivity: 0, >0 - <1, 1 - 10, >10 - 50, and >50%) and sensitivity/specificity (for binary positivity defined with 1%, 10% and 25% cut-offs). Pathologist and inForm readings were available for 149 and 134 cores, respectively; 105 cores had both. The correlation in the expression across available cores for a woman (median =3, range 1-6) was strong for FAP, MMP14, and s100a6 (Intra-class correlation [ICC]=0.69, 0.72, 0.63, respectively), moderate for αSMA (ICC=0.35), and poor for TNC (ICC=0.21). Correlation between pathologist and inForm was strong for s100a6, FAP, and MMP14 (correlation coefficient =0.77, 0.70, and 0.78, respectively) and moderate for αSMA (0.37) and TNC (0.42). With 1% positivity cut-off, sensitivity was the lowest for TNC (0.30) and ranged between 0.84-0.93 for other markers. Specificity ranged between 0.43-0.98 across all markers with the lowest estimates for αSMA. Sensitivity declined for all markers while using 10% and 25% cut-offs, while specificity increased. Our findings show that computational assessments for αSMA, FAP, MMP14, TNC, and s100a6 exhibit variable correlations with manual assessment. These findings support the use of computational platforms for IF evaluation of most, but not all, stromal markers in large-scale epidemiologic studies and the importance of pilot studies for identification of appropriate cut-offs for defining staining positivity.

BACKGROUND:We explored the associations of alcohol consumption with expression of α-Smooth Muscle Actin (α-SMA), Tenascin-C (TNC), Fibroblast Activation Protein (FAP), Matrix Metalloproteinase-14 (MMP14), and Calcyclin (s100a6) stromal markers in benign breast biopsy samples. METHODS:The study included 683 cancer-free women from the Nurses' Health Study II who had biopsy-confirmed benign breast disease (BBD). Alcohol consumption was assessed with semi-quantitative Food Frequency Questionnaires. The data on breast cancer (BCa) risk factors were obtained from biennial questionnaires. Immunofluorescence for stromal markers was performed on tissue microarrays. For each core, the % positivity was quantified by inForm v2.6.0. Generalised linear regression was used to examine associations between alcohol consumption (recent [at biopsy date] and cumulative average from all available questionnaires before the biopsy date) and log-transformed expression of each marker, adjusting for BCa risk factors and BBD subtype. RESULTS:In multivariate analysis, we observed a suggestive positive association of cumulative average alcohol with TNC (β per 11 g/day = 0.59, 95% CI -0.03,1.22, p = 0.06). Alcohol consumption was not associated with α-SMA, FAP, s100a6, and MMP14. CONCLUSION:Alcohol consumption may be associated with an increased normal stromal fibroblast activation as measured by TNC expression in histologically normal breast tissue. Future studies are warranted to confirm our findings.

BACKGROUND:Given that primary prevention strategies for ovarian cancer, such as surgery and medications, have inherent risks, identifying those at high risk of lethal ovarian cancer is critical. We examined prediagnosis factors and risk of developing and dying from ovarian cancer among cancer-free women. METHODS:Analyses were conducted in three 12-year periods from 1980 to 2017 in the Nurses' Health Study (NHS) and NHSII cohorts. Potential risk factors were reproductive and hormonal variables, endometriosis history, smoking, low-dose aspirin, self-identified race, family history, depression, and adiposity over the life course. We used a multistate survival model to estimate relative risks and 95% lower and upper confidence limits for lethal ovarian cancer among 211,420 cancer-free women, among whom 1,730 developed ovarian cancer and 660 died because of ovarian cancer in the same risk period as diagnosis. RESULTS:Of the 22 exposures evaluated, 10 were associated with lethal ovarian cancer. For example, nulliparity had an amplified association with lethal ovarian cancer (1.62, 1.23-2.13) due to associations with both incidence and mortality in the same direction. Oral contraceptive use ≥10 versus 0 years was associated with lethal ovarian cancer (0.65, 0.43-0.97) primarily due to association with incidence, whereas ≥20 versus 0 pack-years of smoking was associated with lethal ovarian cancer (1.25, 1.02-1.53) primarily due to the mortality relationship. CONCLUSIONS:Several reproductive factors, depression, and self-identified race were associated with risk of lethal ovarian cancer. IMPACT:Evaluations of lethal ovarian cancer risk must consider differential associations of exposures with incidence and mortality.

Abstract In 2019, the EAT-Lancet Commission recommended a dietary pattern that is both good for human health and environmentally sustainable. The EAT-Lancet reference diet emphasizes high consumption of vegetables, fruits, whole grains, legumes, nuts, and unsaturated oils, low to moderate intake of seafood and poultry, and no to low intake of red meat, processed meat, added sugar, added saturated fat, refined grains, and starchy vegetables. The Planetary Health Dietary Index (PHDI) was subsequently developed to quantify adherence to this reference diet. Adolescence is marked by rapid breast tissue development and hormonal changes, presenting a susceptible window for breast carcinogenesis. Exposures during this period, including diet, could influence breast cancer risk.This study examines the association between the PHDI during adolescence and subsequent risk of invasive breast cancer in adulthood. We analyzed data from 47,355 women aged 33-52 years old in the Nurses’ Health Study II (NHSII) who recalled their adolescent diet using a food frequency questionnaire in 1998. PHDI scores were calculated to reflect adherence to the EAT-Lancet reference diet in adolescence. Participants were followed from 1998 until breast or other cancer diagnosis, death, loss to follow-up, or the end of 2019, whichever occurred first. Multivariable Cox Proportional Hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI).Over 896,674 person-years of follow-up, 1945 invasive breast cancer cases were documented, including 1245 estrogen receptor (ER) - positive and 233 ER-negative cases. Adolescent PHDI was not significantly associated with overall invasive breast cancer risk (HR for highest vs. lowest quartile = 0.89; 95% CI: 0.78, 1.02 ; p trend = 0.29), or with ER-positive breast cancer (HR = 1.00; 95% CI: 0.85, 1.18; p trend = 0.50). However, higher PHDI during adolescence was significantly associated with reduced risk of ER-negative breast cancer (HR = 0.69; 95% CI: 0.47, 1.00; p trend = 0.05), which was slightly attenuated after adjusting for weight change since age 18 (HR = 0.70; 95% CI: 0.48, 1.01; p trend = 0.06). The association between PHDI during adolescence and overall invasive breast cancer risk did not differ by menopausal status or by subgroups of body mass index at age 18.In conclusion, adherence to the EAT-Lancet reference diet during adolescence may reduce the risk of more aggressive breast cancer subtypes, particularly ER-negative cases, but not overall breast cancer. These findings highlight a dietary pattern that could be both health-promoting and environmentally sustainable during a key developmental period. Citation Format: Phuong Anh Le, Walter C. Willett, Bernard Rosner, Wendy Y. Chen, Michelle D. Holmes, Andrea Romanos-Nanclares, A. Heather Eliassen. Adherence to the EAT-Lancet reference diet in adolescence and risk of invasive breast cancer among US women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1374.

Abstract Purpose: Prior studies show the importance of stroma in breast tumorigenesis. However, there is no data on the expression of stromal markers alpha-smooth muscle actin (αSMA), fibroblast activation protein (FAP), matrix metallo-peptidase (MMP14), tenascin-C (TNC), and calcyclin (s100A6) in the breast tissue of cancer-free women. We compared the immunofluorescence (IF) expression assessment of these markers in histologically normal terminal duct-lobular unit tissue cores between an expert pathologist and an automated image analysis. We also assessed the homogeneity of these markers across multiple cores pertaining to each woman. Methods: We included 73 cancer-free women with biopsy-confirmed benign breast disease in the Nurses’ Health Study (NHS) and NHSII cohorts. IF was conducted with commercial antibodies (αSMA: 1:400 dilution; FAP: 1:50; MMP14: 1:150; TNC: 1:200; s100A6: 1:300). For each core, the % positivity was quantified by the pathologist and inForm v2.6.0. Using the pathologist scores as the gold standard, correlations between the two methods were evaluated with Spearman correlation (for categorical positivity: 0, >0 -<1, 1- 10, >10-50, and >50%) and sensitivity/specificity (for binary positivity with 1%, 10% and 25% cut-offs). Results: Pathologist and inForm readings were available for 149 and 134 cores, respectively; 105 cores had both manual and automated readings. The correlation in the expression across available cores for a woman (median=3, range 1-6) was strong for FAP, MMP14, and S100A6 (Intra-class correlation [ICC]=0.69, 0.72, 0.63, respectively), moderate for αSMA (ICC=0.35), and poor for TNC (ICC=0.21). Correlation between pathologist and inForm was strong for s100A6, FAP, and MMP14 (0.77, 0.70, and 0.78, respectively) and moderate for αSMA (0.37) and TNC (0.42). With a 1% cut-off, sensitivity was the lowest for TNC (0.30) and ranged between 0.84-0.93 for other markers. Specificity ranged between 0.43-0.98 with the lowest estimates for αSMA. Sensitivity declined for all markers while using 10% and 25% cut-offs, while specificity increased. Conclusion: Our findings show that computational assessments for αSMA, FAP, MMP14, TNC, and s100A6 exhibit variable correlations with manual assessment. These findings support the use of computational platforms for IF evaluation of stromal markers in large-scale epidemiologic studies and the importance of pilot studies for identification of appropriate cut-offs for defining staining positivity. Citation Format: Lusine Yaghjyan, Yaileen D. Guzman-Arocho, Yu Jing J. Heng, Brian R. Sardella, Gurzhikhan Murtazaalieva, Graham A. Colditz, Dongtao Fu, Krishna Patel, Bernard Rosner, Maisey Ratcliffe, Rulla M. Tamimi. Reliability of stromal markers multiplex immunofluorescent staining: Pathologist assessment compared to quantitative image analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2356.

Supplementary Figure S3: Simple directed acyclic graph showing a new marker partially mediated through an existing predictor

Background: Surgery for rotator cuff syndrome (RCS) is painful and expensive with 3 months rehabilitation thereafter. Physical therapy has short-term success, but no longer-term studies confirm its permanence. We report the efficacy, safety and mechanism of triangular forearm support (TFS) at a wall, to reduce pain and improve active range of motion in MRI-confirmed RCS. Noteworthy is that several, or even a single repetition of the maneuver gives long term benefits in many cases. Methods: This single-visit randomized controlled crossover trial with intervention group (IG) doing TFS and placebo (CG) group patients had 3 self-rated visual analogue scale (VAS) ratings before performing intervention or sham maneuvers. IG: n = 80; mean age 65.3 and CG: n = 87; mean age 63.8. Difference in age: t = - 0.14854; p = 0.8821; df = 165. Groups held TFS or placebo for 45 seconds, then rated pain in maximal abduction and flexion immediately three times. CG patients then immediately crossed over and performed TFS and underwent three trials of abduction and flexion after doing TFS, as above. Results: averaged each set of 3 trials and summed each case’s change before comparing IC and CG. We took the mean of the differences, not the differences of the means. Abduction: mean immediate post-TFS and post-sham VAS dropped 1.98 and 1.08 points from 6.14 and 5.03 respectively or 32.3% vs. 21%, respectively (p = 0.004). Flexion: baseline IG and CG values: 5.13 and 4.57 immediately dropped 1.08 and .93, 32% and 20.4% lower, respectively (p =.002); (CI: - 0.0317 - 0.0317). Mean 52-month telephone, email or Internet follow-up: Abduction and flexion VAS improvement from initial VAS: 3.2 points (95% CI: 0.13 to 1.71), p = 0.001 and 3.04 points (95% CI 0.54 - 1.73), p <0.001) respectively. VAS values for abduction and flexion were 67.6% and 74.5% below VAS values at study onset. Full abduction/flexion ranges of motion were reported by 31/54 and 32/54 patients. Since follow-up was done remotely, goniometric measurement was impossible. Conclusion: Standing TFS may improve abduction and flexion ROM and reduce pain in RCS.

Supplementary Table S4: NRI and IDI of the risk prediction models for high-risk SPs.

Supplemental Figure 2 provides representative images from the immune checkpoint panel for high-grade serous, endometrioid, clear cell, and mucinous tumors.

Purpose: To evaluate the performance of various analysis approaches for skewed correlated eye data from 2 eyes of a subject in the same comparison group, which is common in ophthalmology and vision research. Design: Simulation study and real data analysis. Subjects: Simulated subjects and participants of the Dry Eye Assessment and Management (DREAM) study. Methods: We simulated skewed correlated data using (skewness, kurtosis) = (27, 50) and (0.06, 5.9), intereye correlation (ρ = 0, 0.25, 0.50, and 0.75), sample sizes (n = 20, 50, 100, and 200), and mean differences between 2 groups (0 for type 1 error rate, 0.2 for statistical power). Each simulated data set was analyzed without and with applying rank-based normalization: (1) 2-sample t test of 2 eye data; (2) 2-sample t test of random eye data; (3) Wald test from generalized estimating equations (GEE Wald); (4) GEE score test (GEE score); (5) F-test from linear mixed effects model (LMM); (6) clustered Wilcoxon test of Rosner, Glynn, and Lee; (7) clustered Wilcoxon test of Datta and Satten; (8) Wilcoxon rank sum test of 2 eyes ignoring intereye correlation; and (9) Wilcoxon rank sum test on average of 2 eyes. We demonstrated analysis of skewed tear break-up time (TBUT) data from the DREAM study. Main Outcome Measures: Type 1 error rate and statistical power. Results: T test and Wilcoxon test on 2 eye data without accounting for intereye correlation inflated type 1 error rate up to 0.13, and GEE Wald inflated type 1 error rate to 0.08 when sample size is small, whereas all other tests maintained type 1 error rate close to 0.05. For skewed data without normalization, t test of random eye, GEE Wald, GEE score, and LMM had substantially lower power than clustered Wilcoxon methods. After normalization, GEE score and LMM achieved similar or slightly higher power than clustered Wilcoxon methods. Results from analysis of TBUT are consistent with simulation findings. Conclusions: When comparing skewed correlated eye measures between 2 groups of subjects with their 2 eyes in the same comparison group, clustered Wilcoxon methods can be used. Alternatively, skewed data can be normalized before applying GEE score or LMM, which may achieve slightly higher statistical power than clustered Wilcoxon methods and offers flexibility to adjust for other covariates. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Supplementary Figure S2: Distribution of estimated risk scores at cohort entry

Supplementary Table S2: Associations of the included predictors with the risk of high-risk SPs by sex.

Supplementary Data from A Multi-State Survival Model for Time to Breast Cancer Mortality among a Cohort of Initially Disease-Free Women

Supplemental Table 4 shows beta binomial models, odds ratios and 95% confidence intervals for marker positivity in the tumor, by tumor and participant characteristics

Case-control studies of sun exposure and ultraviolet radiation (UVR) have consistently reported inverse associations with non-Hodgkin lymphoma (NHL) risk, but prospective studies have yielded mixed results. Few studies have explored these exposures in relation to multiple myeloma (MM) risk. To further evaluate these associations with NHL and MM risk and identify etiologically relevant exposure timing, we pooled data on 566 693 individuals from 6 US prospective cohort studies (11 636 incident NHL; 2749 incident MM; median follow-up: 20 years) and used geographic information systems models to estimate residential ambient UVR levels at time points from birth to adulthood. Using Cox proportional hazards models, we calculated hazard ratios (HRs) and 95% CIs for associations of residential ambient UVR levels with NHL overall, NHL subtypes, and MM, adjusted for study, age, and other putative risk factors. No UVR measures were significantly associated with NHL or NHL subtypes. Higher residential UVR levels during cohort follow-up were inversely associated with MM overall and among female patients (longitudinally updated HR per IQR increase: 0.74; 95% CI, 0.63-0.86) but not male patients (1.08; 95% CI, 0.90-1.29). Our results do not confirm an inverse association of adult ambient UVR levels with NHL risk. The MM findings require further investigation.

Supplementary Methods S1: Statistical justification. Supplementary Table S1: Estimated association and C-index for two working models in the simulation study. Supplementary Table S2: Estimated association and C-index for two working models in the simulation study. Supplementary Table S3: Estimated C-index for working models under various simulated settings by correlation between x and z as well as odds ratio between z and the outcome. Supplementary Table S4: Estimated C-index for working models under various simulated settings by correlation between x and z as well as odds ratio between z and the outcome