Metformin and other anti-diabetic medication use and breast cancer incidence in the Nurses' Health Studies.

We aimed to examine the association between the use of metformin and other anti-diabetic medications and breast cancer incidence within two large prospective cohort studies. We followed 185,181 women who participated in the Nurses' Health Study (NHS; 1994-2016) and the NHSII (1995-2017), with baseline corresponding to the date metformin was approved for type 2 diabetes (T2D) treatment in the US Information on T2D diagnosis, anti-diabetes medications, and other covariates was self-reported at baseline and repeatedly assessed by follow-up questionnaires every 2 years. Breast cancer cases were self-reported and confirmed by medical record review. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between medication use and breast cancer were estimated using Cox proportional hazards regression models, adjusting for breast cancer risk factors. During 3,324,881 person-years of follow-up, we ascertained 9,192 incident invasive breast cancer cases, of which 451 were among women with T2D. Compared with women without T2D (n = 169,263), neither metformin use (HR = 0.97; 95% CI = 0.81-1.15) nor other anti-diabetic medications use (HR = 1.11; 95% CI = 0.90-1.36) associated with significantly lower breast cancer incidence. Among women with T2D (n = 15,918), compared with metformin never users, metformin ever use was not significantly inversely associated with breast cancer (HR = 0.92; 95% CI = 0.74-1.15). Although we observed that past use of metformin was inversely associated with breast cancer in the T2D population (HR = 0.67; 95% CI = 0.48-0.94), current use (HR = 1.01; 95% CI = 0.80-1.27) and longer duration of metformin use were not associated with breast cancer (each 2-year interval: HR = 1.01; 95% CI = 0.95-1.07). Overall, metformin use was not associated with the risk of developing breast cancer among the overall cohort population or among women with T2D.

Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older.

BACKGROUND: To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. METHODS: We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. RESULTS: When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. CONCLUSIONS: Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.

Incidence of and Risk Factors for Cataract in Anterior Uveitis.

PURPOSE: To estimate the incidence/risk factors for cataract in noninfectious anterior uveitis. DESIGN: Retrospective multicenter cohort study (6 US tertiary uveitis sites, 1978-2010). METHODS: Data were harvested by trained expert reviewers, using protocol-driven review of experts' charts. We studied cataract incidence-newly reduced visual acuity worse than 20/40 attributed to cataract; or incident cataract surgery-in 3923 eyes of 2567 patients with anterior uveitis. RESULTS: Cataract developed in 507 eyes (54/1000 eye-years, 95% CI 49-59). Time-updated risk factors associated with cataract included older age (≥65 vs <18 years: adjusted hazard ratio [aHR] 5.04, 95% CI 3.04-8.33), higher anterior chamber cell grade (P(trend)=0.001), prior incisional glaucoma surgery (aHR 1.86, 95% CI 1.10-3.14), band keratopathy (aHR 2.23, 95% CI 1.47-3.37), posterior synechiae (aHR 3.71, 95% CI 2.83-4.87), and elevated intraocular pressure ≥30 vs 6-20 mm Hg (aHR 2.57, 95% CI 1.38-4.77). Primary acute (aHR 0.59, 95% CI 0.30-1.15) and recurrent acute (aHR 0.74, 95% CI 0.55-0.98) had lower cataract risk than chronic anterior uveitis. Higher-dose prednisolone acetate 1%-equivalent use (≥2 drops/day) was associated with >2-fold higher cataract risk in eyes with anterior chamber cell grades 0.5+ or lower but was not associated with higher cataract risk in the presence of anterior chamber cells of grade 1+ or higher. CONCLUSIONS: Cataract complicates anterior uveitis in ∼5.4/100 eye-years. Several fixed and modifiable risk factors were identified, yielding a point system to guide cataract risk minimization. Topical corticosteroids only were associated with increased cataract risk when anterior chamber cells were absent or minimally present, suggesting their use to treat active inflammation (which itself is cataractogenic) does not cause a net increase in cataract incidence.

Associations of low-carbohydrate diets with breast cancer survival.

BACKGROUND: The objective of this study was to evaluate the role of low-carbohydrate diets after breast cancer diagnosis in relation to breast cancer-specific and all-cause mortality. METHODS: For 9621 women with stage I-III breast cancer from two ongoing cohort studies, the Nurses' Health Study and Nurses' Health Study II, overall low-carbohydrate, animal-rich low-carbohydrate, and plant-rich low-carbohydrate diet scores were calculated by using food frequency questionnaires collected after breast cancer diagnosis. RESULTS: Participants were followed up for a median 12.4 years after breast cancer diagnosis. We documented 1269 deaths due to breast cancer and 3850 all-cause deaths. With the use of Cox proportional hazards regression and after controlling for potential confounding variables, we observed a significantly lower risk of overall mortality among women with breast cancer who had greater adherence to overall low-carbohydrate diets (hazard ratio for quintile 5 vs. quintile 1 [HRQ5vsQ1 ], 0.82; 95% CI, 0.74-0.91; ptrend  = .0001) and plant-rich low-carbohydrate diets (HRQ5vsQ1 , 0.73; 95% CI, 0.66-0.82; ptrend < .0001) after breast cancer diagnosis but not animal-rich low-carbohydrate diets (HRQ5vsQ1 , 0.93; 95% CI, 0.84-1.04; ptrend  = .23). However, greater adherence to overall, animal-rich, or plant-rich low-carbohydrate diets was not significantly associated with a lower risk of breast cancer-specific mortality. CONCLUSIONS: This study showed that greater adherence to low-carbohydrate diets, especially plant-rich low-carbohydrate diets, was associated with better overall survival but not breast cancer-specific survival among women with stage I-III breast cancer.

Consumption of olive oil and risk of breast cancer in U.S. women: results from the Nurses' Health Studies.

BACKGROUND: Olive oil consumption may reduce breast cancer risk, but it is unclear whether olive oil is beneficial for breast cancer prevention in populations outside of Mediterranean regions, namely in the U.S., where the average consumption of olive oil is low compared with Mediterranean populations. We examined whether olive oil intake was associated with breast cancer risk in two prospective cohorts of U.S. women. METHODS: We used multivariable-adjusted time-varying Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence interval (CI) for breast cancer among 71,330 (Nurses' Health Study, 1990-2016) and 93,295 women (Nurses' Health Study II, 1991-2017) who were free of cancer at baseline. Diet was assessed by a validated semi-quantitative food frequency questionnaire every 4 years. RESULTS: During 3,744,068 person-years of follow-up, 9,638 women developed invasive breast cancer. The multivariable-adjusted HR (95% CI) for breast cancer among women who had the highest consumption of olive oil (>1/2 tablespoon/d or >7 g/d) compared with those who never or rarely consumed olive oil, was 1.01 (0.93, 1.09). Higher olive oil consumption was not associated with any subtype of breast cancer. CONCLUSION: We did not observe an association between higher olive oil intake and breast cancer risk in two large prospective cohorts of U.S. women, whose average olive oil consumption was low. Prospective studies are needed to confirm these findings and to further investigate whether different varieties of olive oil (e.g., virgin and extra virgin olive oil) may play a role in breast cancer risk.

Development and Validation of a Risk Model for Predicting Contrast-Associated Acute Kidney Injury in Patients With Cancer: Evaluation in Over 46,000 CT Examinations.

BACKGROUND. Patients with cancer undergo frequent CT examinations with iodinated contrast media and may be uniquely predisposed to contrast-associated acute kidney injury (CA-AKI). OBJECTIVE. The purpose of this study was to develop and validate a model for predicting the risk of CA-AKI after contrast-enhanced CT in patients with cancer. METHODS. This retrospective study included 25,184 adult patients (12,153 men, 13,031 women; mean age, 62.3 ± 13.7 [SD] years) with cancer who underwent 46,593 contrast-enhanced CT examinations between January 1, 2016, and June 20, 2020, at one of three academic medical centers. Information was recorded regarding demographics, malignancy type, medication use, baseline laboratory values, and comorbid conditions. CA-AKI was defined as a 0.3-mg/dL or greater increase in serum creatinine level from baseline within 48 hours after CT or a 1.5-fold or greater increase in the peak measurement within 14 days after CT. Multivariable models accounting for correlated data were used to identify risk factors for CA-AKI. A risk score for predicting CA-AKI was generated in a development set (n = 30,926) and tested in a validation set (n = 15,667). RESULTS. CA-AKI occurred after 5.8% (2682/46,593) of CT examinations. The final multivariable model for predicting CA-AKI included hematologic malignancy, diuretic use, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use, chronic kidney disease (CKD) stage 3a, CKD stage 3b, CKD stage 4 or 5, serum albumin level less than 3.0 g/dL, platelet count less than 150 × 103/µL, 1+ or greater proteinuria on baseline urinalysis, diabetes mellitus, heart failure, and contrast medium volume 100 mL or greater. A risk score (range, 0-53 points) was generated with these variables. The most points (13) were for CKD stage 4 or 5 and for albumin level less than 3 g/dL. The frequency of CA-AKI progressively increased in higher risk categories. For example, in the validation set, CA-AKI occurred after 2.2% of CT examinations in the lowest risk category (score ≤ 4) and after 32.7% of CT examinations in the highest risk category (score ≥ 30). The Hosmer-Lemeshow test result indicated that the risk score was a good fit (p = .40). CONCLUSION. A risk model in which readily available clinical data are used to predict the likelihood of CA-AKI after contrast-enhanced CT in patients with cancer was developed and validated. CLINICAL IMPACT. The model may help facilitate appropriate implementation of preventive measures in the care of patients at high risk of CA-AKI.

Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk.

Importance: Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk. Objective: To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer. Design, Setting, and Participants: This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis. Exposures: Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment. Main Outcomes and Measures: Longitudinal changes over time in each woman's volumetric breast density by case and control status. Results: The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis (10th percentile, 1.0 year; 90th percentile, 3.9 years). Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; P = .04). Conclusions and Relevance: This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.

Associations of alcohol consumption with breast tissue composition.

BACKGROUND: We investigated the associations of alcohol with percentage of epithelium, stroma, fibroglandular tissue (epithelium + stroma), and fat in benign breast biopsy samples. METHODS: We included 857 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII cohorts. Percentage of each tissue was measured on whole slide images using a deep-learning algorithm and then log-transformed. Alcohol consumption (recent and cumulative average) was assessed with semi-quantitative food frequency questionnaires. Regression estimates were adjusted for known breast cancer risk factors. All tests were 2-sided. RESULTS: Alcohol was inversely associated with % of stroma and fibroglandular tissue (recent ≥ 22 g/day vs. none: stroma: ß = - 0.08, 95% Confidence Interval [CI] - 0.13; - 0.03; fibroglandular: ß = - 0.08, 95% CI - 0.13; - 0.04; cumulative ≥ 22 g/day vs. none: stroma: ß = - 0.08, 95% CI - 0.13; - 0.02; fibroglandular: ß = - 0.09, 95% CI - 0.14; - 0.04) and positively associated with fat % (recent ≥ 22 g/day vs. none: ß = 0.30, 95% CI 0.03; 0.57; cumulative ≥ 22 g/day vs. none: ß = 0.32, 95% CI 0.04; 0.61). In stratified analysis, alcohol consumption was not associated with tissue measures in premenopausal women. In postmenopausal women, cumulative alcohol use was inversely associated with % of stroma and fibroglandular tissue and positively associated with fat % (≥ 22 g/day vs. none: stroma: ß = - 0.16, 95% CI - 0.28; - 0.07; fibroglandular: ß = - 0.18, 95% CI - 0.28; - 0.07; fat: ß = 0.61, 95% CI 0.01; 1.22), with similar results for recent alcohol use. CONCLUSION: Our findings suggest that alcohol consumption is associated with smaller % of stroma and fibroglandular tissue and a greater % of fat in postmenopausal women. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.

Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk.

PURPOSE: We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS: Using nested case-control data from the Nurses' Health Study, we compared the models' association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk. RESULTS: The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035). CONCLUSION: BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.

Risk prediction models for endometrial cancer: development and validation in an international consortium.

BACKGROUND: Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors. METHODS: We developed endometrial cancer risk prediction models using data on postmenopausal White women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium (E2C2). Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in 3 cohorts: Nurses' Health Study (NHS), NHS II, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. RESULTS: Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59). CONCLUSIONS: Using data from the largest, most heterogeneous study population to date (to our knowledge), prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.

A model for predicting both breast cancer risk and non-breast cancer death among women > 55 years old.

BACKGROUND: Guidelines recommend shared decision making (SDM) for mammography screening for women ≥ 75 and not screening women with < 10-year life expectancy. High-quality SDM requires consideration of women's breast cancer (BC) risk, life expectancy, and values but is hard to implement because no models simultaneously estimate older women's individualized BC risk and life expectancy. METHODS: Using competing risk regression and data from 83,330 women > 55 years who completed the 2004 Nurses' Health Study (NHS) questionnaire, we developed (in 2/3 of the cohort, n = 55,533) a model to predict 10-year non-breast cancer (BC) death. We considered 60 mortality risk factors and used best-subsets regression, the Akaike information criterion, and c-index, to identify the best-fitting model. We examined model performance in the remaining 1/3 of the NHS cohort (n = 27,777) and among 17,380 Black Women's Health Study (BWHS) participants, ≥ 55 years, who completed the 2009 questionnaire. We then included the identified mortality predictors in a previously developed competing risk BC prediction model and examined model performance for predicting BC risk. RESULTS: Mean age of NHS development cohort participants was 70.1 years (± 7.0); over 10 years, 3.1% developed BC, 0.3% died of BC, and 20.1% died of other causes; NHS validation cohort participants were similar. BWHS participants were younger (mean age 63.7 years [± 6.7]); over 10-years 3.1% developed BC, 0.4% died of BC, and 11.1% died of other causes. The final non-BC death prediction model included 21 variables (age; body mass index [BMI]; physical function [3 measures]; comorbidities [12]; alcohol; smoking; age at menopause; and mammography use). The final BC prediction model included age, BMI, alcohol and hormone use, family history, age at menopause, age at first birth/parity, and breast biopsy history. When risk factor regression coefficients were applied in the validation cohorts, the c-index for predicting 10-year non-BC death was 0.790 (0.784-0.796) in NHS and 0.768 (0.757-0.780) in BWHS; for predicting 5-year BC risk, the c-index was 0.612 (0.538-0.641) in NHS and 0.573 (0.536-0.611) in BWHS. CONCLUSIONS: We developed and validated a novel competing-risk model that predicts 10-year non-BC death and 5-year BC risk. Model risk estimates may help inform SDM around mammography screening.

Impact of smoking status on health-related quality of life (HRQoL) in cancer survivors.

Introduction: The Health-Related Quality of Life (HRQoL) often declines among cancer survivors due to many factors. Some cancer patients who smoke before the cancer diagnosis continue this harmful habit, potentially contributing to a more significant decline in their HRQoL. Therefore, this study investigates the association between smoking status and HRQoL in cancer survivors. Methods: We conducted a cross-sectional study utilizing self-reported cancer history from 39,578 participants of the Behavioral Risk Factor Surveillance System (BRFSS) database, leveraging 2016 and 2020 year questionaries. A multidimensional composite outcome was created to assess HRQoL, integrating four distinct dimensions - general health, mental health, physical health, and activity limitations. After accounting for the complex survey design, logistic regression models were used to analyze the association between smoking status and poor HRQoL, adjusting for demographic, socioeconomic, and health-related confounders. Results: Our study found that, after adjusting for potential confounders, current smokers exhibited a significantly poorer HRQoL than never smokers (OR 1.65, 95%CI 1.40-1.93). Furthermore, former smokers showed a poorer HRQoL than never smokers; however, this association was not as strong as current smokers (OR 1.22, 95%CI 1.09-1.38). Conclusion: Our findings highlight the adverse association of smoking with poor HRQoL in cancer survivors, underscoring the importance of healthcare professionals prioritizing smoking cessation and providing tailored interventions to support this goal.

A prospective analysis of red blood cell membrane polyunsaturated fatty acid levels and risk of non-Hodgkin lymphoma.

Published studies report inconsistent associations of polyunsaturated fatty acid (PUFA) intake with non-Hodgkin lymphoma (NHL) risk. We conducted a nested case-control study in Nurses' Health Study and Health Professionals Follow-Up Study participants to evaluate a hypothesis of inverse association of pre-diagnosis red blood cell (RBC) membrane PUFA levels with risk of NHL endpoints. We confirmed 583 NHL cases and matched 583 controls by cohort/sex, age, race and blood draw date/time. We estimated odds ratios (OR) and 95% confidence intervals (CI) for risk of NHL endpoints using logistic regression. RBC PUFA levels were not associated with all NHL risk; cis 20:2n-6 was associated with follicular lymphoma risk (OR [95% CI] per one standard deviation increase: 1.35 [1.03-1.77]), and the omega-6/omega-3 PUFA ratio was associated with diffuse large B-cell lymphoma risk (2.33 [1.23-4.43]). Overall, PUFA did not demonstrate a role in NHL etiology; the two unexpected positive associations lack clear biologic explanations.

The association between anthropometric factors and anti-Müllerian hormone levels in premenopausal women.

Adiposity has been associated with several health conditions as well as timing of menopause. Prior epidemiologic studies on the association of adiposity and anti-Müllerian hormone (AMH) have been inconsistent. We evaluated the relations of anthropometric measures with AMH at two time periods in a subset of premenopausal participants in the Nurses' Health Study II. This prospective study included 795 women who provided a premenopausal sample in 1996-1999 and in 2010-2012. Current weight and height, and weight at age 18 were assessed in 1989 and weight again in 1996-1999. Waist and hip circumference were measured and reported in 1993. In linear regression models adjusted for smoking, reproductive events, and other factors, AMH was inversely related to BMI at age 18 (P = .03) and in 1996-1999 (P < .0001). Higher waist circumference was related to lower AMH levels in 1996-1999 (p = .0009). BMI in 1996-1999 was inversely associated with AMH levels in 2010-2012 (P = .005). Weight gain between age 18 and 1996-1999 was strongly inversely associated with AMH levels in 1996-1999 (P < .0001) and in 2010-2012 (P < .0001). Our results indicate that adiposity and weight gain are associated with lower AMH levels, suggesting an adverse impact on ovarian function.

Anthropometric traits and risk of multiple myeloma: a pooled prospective analysis.

BACKGROUND: Obesity is a risk factor for multiple myeloma (MM), yet results of prior studies have been mixed regarding the importance of early and/or later adult obesity; other measures of body composition have been less well studied. METHODS: We evaluated associations of early adult (ages 18-21) and usual adult body mass index (BMI), waist circumference, and predicted fat mass with MM by pooling data from six U.S. prospective cohort studies comprising 544,016 individuals and 2756 incident diagnoses over 20-37 years of follow-up. We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations, adjusted for age and other risk factors. RESULTS: Each 5 kg/m2 increase in usual adult BMI was associated with a 10% increased risk of MM (HR: 1.10; 95% CI: 1.05-1.15). Positive associations were also noted for early adult BMI (HR per 5 kg/m2: 1.14; 95% CI: 1.04-1.25), height (HR per 10 cm: 1.28; 95% CI: 1.20-1.37), waist circumference (HR per 15 cm: 1.09; 95% CI: 1.00-1.19), and predicted fat mass (HR per 5 kg: 1.06; 95% CI: 1.01-1.11). CONCLUSIONS: These findings highlight the importance of avoidance of overweight/obesity and excess adiposity throughout adulthood as a potential MM risk-reduction strategy.

Body size and risk of non-Hodgkin lymphoma by subtype: A pooled analysis from six prospective cohorts in the United States.

In 2022, more than 100 000 non-Hodgkin lymphoma (NHL) diagnoses are expected, yet few risk factors are confirmed. In this study, data from six US-based cohorts (568 717 individuals) were used to examine body size and risk of NHL. Over more than 20 years of follow-up, 11 263 NHLs were identified. Hazard ratios (HRs) and 95% confidence intervals (CI) estimated associations with NHLs for adult body mass index (BMI), height, weight change, waist circumference and predicted fat mass. Adult height was broadly associated with NHL, but most strongly with B-cell NHLs among non-White participants (e.g. HRBLACK  = 2.06, 95% CI: 1.62-2.62). However, the strongest association among the anthropometric traits examined was for young adult BMI and risk of diffuse large B-cell lymphoma (DLBCL), particularly those who maintained a higher BMI into later adulthood. Individuals with BMI over 30 kg/m2 throughout adulthood had more than double the DLBCL risk (HR = 2.67, 95% CI: 1.71-4.17) compared to BMI 18.5-22.9 kg/m2 . Other anthropometric traits were not associated with NHL after controlling for BMI. These results suggest that sustained high BMI is a major driver of DLBCL risk. If confirmed, we estimate that up to 23.5% of all DLBCLs (and 11.1% of all NHLs) may be prevented with avoidance of young adult obesity.

Association of In Utero Exposures With Risk of Early Natural Menopause.

Suboptimal pregnancy conditions may affect ovarian development in the fetus and be associated with early natural menopause (ENM) for offspring. A total of 106,633 premenopausal participants in Nurses' Health Study II who provided data on their own prenatal characteristics, including diethylstilbestrol (DES) exposure, maternal cigarette smoking exposure, multiplicity, prematurity, and birth weight, were followed from 1989 to 2017. Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of in utero exposures with ENM. During 1.6 million person-years of follow-up, 2,579 participants experienced ENM. In multivariable models, women with prenatal DES exposure had higher risk of ENM compared with those without it (HR = 1.33, 95% CI: 1.06, 1.67). Increased risk of ENM was observed for those with low (<5.5 pounds (<2.5 kg)) versus normal (7.0-8.4 pounds (3.2-3.8 kg)) birth weight (HR = 1.21, 95% CI: 1.01, 1.45). Decreasing risk was observed per 1-pound (0.45-kg) increase in birth weight (HR = 0.93, 95% CI: 0.90, 0.97). Prenatal smoking exposure, being part of a multiple birth, and prematurity were not associated with ENM. In this large cohort study, lower birth weight and prenatal DES exposure were associated with higher risk of ENM. Our results support a need for future research to examine in utero exposures that may affect offspring reproductive health.

A Metabolomics Analysis of Circulating Carotenoids and Breast Cancer Risk.

BACKGROUND: Higher circulating carotenoids are associated with lower breast cancer risk. The underlying biology remains under-explored. METHODS: We profiled 293 prediagnostic plasma metabolites in a nested case-control study (n = 887 cases) within the Nurses' Health Studies. Associations between circulating carotenoids and metabolites were identified using linear-mixed models (FDR ≤ 0.05), and we further selected metabolites most predictive of carotenoids with LASSO. Metabolic signatures for carotenoids were calculated as weighted sums of LASSO selected metabolites. We further evaluated the metabolic signatures in relation to breast cancer risk using conditional logistic-regression. RESULTS: We identified 48 to 110 metabolites associated with plasma levels of α-carotene, ß-carotene, ß-cryptoxanthin, estimated-vitamin-A-potential, lutein/zeaxanthin, and lycopene, which included primarily positively associated metabolites implicated in immune regulation (tryptophan), redox balance (plasmalogens, glutamine), epigenetic regulations (acetylated-/methylated-metabolites), and primarily inversely associated metabolites involved in ß-oxidation (carnitines; FDR ≤ 0.05). The metabolomic signatures derived for ß-carotene (Q4 vs. Q1 relative risk RR = 0.74, P trend = 0.02), and estimated-vitamin-A-potential (Q4 vs. Q1 RR = 0.74, P trend = 0.02)-measured ≥10 years before diagnosis-were associated with lower breast cancer risk. Modest attenuations of RR for measured levels of ß-carotene and estimated-vitamin-A-potential were seen when we adjusted for their corresponding metabolic signatures. CONCLUSIONS: Metabolites involved in immune regulation, redox balance, membrane signaling, and ß-oxidation were associated with plasma carotenoids. Although some metabolites may reflect shared common food sources or compartmental colocalization with carotenoids, others may signal the underlying pathways of carotenoids-associated lowered breast cancer risk. IMPACT: Consumption of carotenoid-rich diet is associated with a wide-range of metabolic changes which may help to reduce breast cancer risk.

The association of parity and breastfeeding with anti-Müllerian hormone levels at two time points.

OBJECTIVE: To evaluate the association between parity and breastfeeding and anti-Müllerian hormone levels (AMH) and change in AMH levels over time. Furthermore, we examined whether AMH levels mediate the relation of parity and breastfeeding with age at menopause. STUDY DESIGN: Observational, prospective cohort study. MAIN OUTCOME MEASURES: AMH levels were assessed in a subset of premenopausal participants in the Nurses' Health Study II, including 1619 women who provided a blood sample in 1996-1999 and an additional 800 women who provided a second premenopausal sample in 2010-2012. RESULTS: In multivariable linear regression models adjusted for parity, body mass index, smoking, and other factors, mean log AMH levels in 1996-1999 were 39% higher in women reporting ≥25 months of total breastfeeding vs. <1 month (P for trend = 0.009). Parity was not associated with AMH levels after adjustment for breastfeeding. Neither parity nor breastfeeding was associated with decline in AMH levels over 11 to 15 years. Breastfeeding duration was positively associated with age at menopause (P for trend = 0.01), with evidence that the association was mediated via AMH. CONCLUSIONS: Our results suggest that breastfeeding is associated with higher AMH levels and later onset of menopause, and support the hypothesis that observed relations of parity with AMH levels and menopause timing may be largely attributable to breastfeeding.

Dietary and Supplemental Vitamin C Intake and Risk of Breast Cancer: Results from the Nurses' Health Studies.

BACKGROUND: Some previous studies suggested that high supplemental vitamin C intake may be associated with an increased risk of breast cancer, although evidence is inconsistent. OBJECTIVES: Our objective was to study the association between vitamin C intake and breast cancer risks using regularly updated assessments of intake over a long follow-up. METHODS: We prospectively followed 88,041 women aged 33 to 60 years from the Nurses' Health Study (1980-2014) and 93,372 women aged 26 to 45 years from the Nurses' Health Study II (1991-2013). A total of 11,258 incident invasive breast cancers among 181,413 women were diagnosed. Data on vitamin C intake were collected every 2-4 years via a validated FFQ and specific questions on dietary supplement use. Multivariate HRs and 95% CIs for incident invasive breast cancer were estimated with Cox models. RESULTS: During follow-up, 82% of participants ever used supplements containing vitamin C, including multivitamins. Cumulative total vitamin C intake (HR for quintiles 5 compared with 1 = 0.97; 95% CI: 0.91-1.03; Ptrend = 0.81), dietary vitamin C intake (HR for quintiles 5 compared with 1 = 0.98; 95% CI: 0.92-1.04; Ptrend = 0.57), and supplemental vitamin C intake (HR for quintiles 5 compared with 1 in users = 1.02; 95% CI: 0.94-1.09; Ptrend = 0.77) were not associated with breast cancer risks. Results were unchanged when different exposure latencies were considered. The results did not differ by menopausal status, postmenopausal hormone therapy use, or BMI. No differences were observed by estrogen receptor status of the tumor. CONCLUSIONS: Our results do not support any important association between total, dietary, or supplemental vitamin C intake and breast cancer risks.