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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; P(trend) = 0.81), dietary vitamin C intake (HR for quintiles 5 compared with 1 = 0.98; 95% CI: 0.92–1.04; P(trend) = 0.57), and supplemental vitamin C intake (HR for quintiles 5 compared with 1 in users = 1.02; 95% CI: 0.94–1.09; P(trend) = 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.

BACKGROUND Development of cardiovascular disease in adults has been directly linked to an adverse metabolic phenotype. While there is evidence that development of these risk factors in childhood persists into adulthood and the development of cardiovascular disease, less is known about whether these risk factors are associated with target organ damage during adolescence. METHODS We collected data from 379 adolescents (mean age 15.5, 60% male) with blood pressure between the 75th and 95th percentile to determine if there is a metabolic phenotype that predicts cardiovascular changes (left ventricular mass, systolic and diastolic function, pulse wave velocity, and renal function). We determined the number of risk factors for cardiovascular disease (hypertension, dyslipidemia, obesity, and insulin resistance) present in each participant. Generalized linear models were constructed to determine if the number of cardiovascular risk factors (CVRFs) were associated with measures of target organ damage. RESULTS The number of CVRFs present were associated with statistically significant differences in increased left ventricular mass index, increased pulse wave velocity, decreased peak longitudinal strain, urine albumin to creatine ratio and echocardiographic parameters of diastolic dysfunction. Generalized linear models showed that dyslipidemia and insulin resistance were independently associated with markers of diastolic dysfunction (P ≤ .05) while increased blood pressure was associated with all makers of target organ damage (P ≤ .03). CONCLUSIONS These data suggest the of the number of CVRFs present is independently associated with early changes in markers of target organ damage during adolescence.

Epidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations depends on the quality of data. Even though statistical methods have been proposed to adjust for measurement errors, they often rely on unverifiable assumptions and could lead to biased estimates if those assumptions are violated. Therefore, methods for detecting potential `outlier' evaluators are needed to improve data quality during data collection stage. In this paper, we propose a two-stage algorithm to detect `outlier' evaluators whose evaluation results tend to be higher or lower than their counterparts. In the first stage, evaluators' effects are obtained by fitting a regression model. In the second stage, hypothesis tests are performed to detect `outlier' evaluators, where we consider both the power of each hypothesis test and the false discovery rate (FDR) among all tests. We conduct an extensive simulation study to evaluate the proposed method, and illustrate the method by detecting potential `outlier' audiologists in the data collection stage for the Audiology Assessment Arm of the Conservation of Hearing Study, an epidemiologic study for examining risk factors of hearing loss in the Nurses' Health Study II. Our simulation study shows that our method not only can detect true `outlier' evaluators, but also is less likely to falsely reject true `normal' evaluators. Our two-stage `outlier' detection algorithm is a flexible approach that can effectively detect `outlier' evaluators, and thus data quality can be improved during data collection stage.

Additional file 1: Table S1. Pearson correlation coefficients between total whole grain and individual whole grain foods. Table S2. Pooled hazard ratios (95% confidence intervals) of coronary heart disease for individual whole grain food consumption in Nurses’ Health Study (1984-2016), Nurses’ Health Study II (1991-2017), and Health Professionals Follow-up Study (1986-2016). Table S3. Pooled hazard ratios (95% confidence intervals) of nonfatal myocardial infarction for individual whole grain food consumption in Nurses’ Health Study (1984-2016), Nurses’ Health Study II (1991-2017), and Health Professionals Follow-up Study (1986-2016). Table S4. Pooled hazard ratios (95% confidence intervals) of fatal coronary heart disease for individual whole grain food consumption in Nurses’ Health Study (1984-2016), Nurses’ Health Study II (1991-2017), and Health Professionals Follow-up Study (1986-2016). Table S5. Association between regular and light/fat free popcorn intake and risk of coronary heart disease in Nurses’ Health Study (2002-2016), Nurses’ Health Study II (2003-2017) and Health Professionals Follow-up study (2002-2016). Table S6. Pooled hazard ratios (95% confidence intervals) of coronary heart disease for individual whole grain food consumption in Nurses’ Health Study (1984-2016), Nurses’ Health Study II (1991-2017), Health Professionals Follow-up Study (1986-2016) using baseline intake, simple updated intake, or 4-year lag consumption. Table S7. Pooled hazard ratios (95% confidence intervals) of coronary heart disease for individual whole grain food consumption in Nurses’ Health Study (1984-2016), Nurses’ Health Study II (1991-2017), Health Professionals Follow-up Study (1986-2016) in analyses that resumed dietary update after 8 years of chronic diseases occurrence. Figure S1. Association between total whole grains and coronary heart disease risk stratified by body mass index, family history of diabetes, physical activity, and smoking status.

Additional file 1: Table S1. Distribution of various tissue elements by the type of benign breast disease. Table S2. Associations of reproductive factors with tissue types in benign breast biopsy samples using proc glimmix procedure (Odds Ratios and 95% Confidence Intervals). Table S3. Associations of reproductive variables with tissue types in benign breast biopsy samples of premenopausal women using proc glimmix procedure (Odds Ratios and 95% Confidence Intervals). Table S4. Associations of reproductive variables with percentage of different tissue types (log-transformed) in benign breast biopsy samples (β coefficients and 95% Confidence Intervals), additionally adjusted for BBD subtype. Table S5. Associations of reproductive variables with percentage of different tissue types (log-transformed) in benign breast biopsy samples of premenopausal women (β coefficients and 95% Confidence Intervals), additionally adjusted for BBD subtype.

Additional file 1: S1. Structural nested cumulative failure time models with a time-varying instrumental variable. Supplementary Figure 1. Distributions of �� estimates across 1,000 iterations using g-estimation with an instrumental variable under different data-generating mechanisms, different sample sizes (n = 10,000; n = 25,000 or n = 50,000), and different instrument-exposure strengths (��ZA = 0.10; �� = 0.25; or �� = 0.45). The lower and upper hinges correspond to the 25th and 75th percentile. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5*IQR from the hinge, where IQR is the interquartile range. The median is represented by the line between the hinges, and the mean is represented by the diamond point symbol. The percentages provided above each box plot represents the percentage of iterations in which the model did not converge. Supplementary Figure 2. A. Distributions of marginal risk differences across 1,000 iterations using different g-estimation approaches under different data-generating mechanisms with �� = 5%. The lower and upper hinges correspond to the 25th and 75th percentile. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5*IQR from the hinge, where IQR is the interquartile range. The median is represented by the line between the hinges, and the mean is represented by the diamond point symbol. B. Distributions of marginal risk ratios across 1,000 iterations using different g-estimation approaches under different data-generating mechanisms with �� = 5%. The lower and upper hinges correspond to the 25th and 75th percentile. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5*IQR from the hinge, where IQR is the interquartile range. The median is represented by the line between the hinges, and the mean is represented by the diamond point symbol. Supplementary Figure 3. A. Distributions of marginal risk differences across 1,000 iterations using different g-estimation approaches under different data-generating mechanisms with baseline hazards of 5%, 10% and 25%. The lower and upper hinges correspond to the 25th and 75th percentile. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5*IQR from the hinge, where IQR is the interquartile range. The median is represented by the line between the hinges, and the mean is represented by the diamond point symbol. B. Distributions of marginal risk ratios across 1,000 iterations using different g-estimation approaches under different data-generating mechanisms with baseline hazards of 5%, 10% and 25%. The lower and upper hinges correspond to the 25th and 75th percentile. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5*IQR from the hinge, where IQR is the interquartile range. The median is represented by the line between the hinges, and the mean is represented by the diamond point symbol. Supplementary Figure 4. Flowchart of inclusion and exclusion of Nurses��� Health Study participants. Supplementary Table 1. Characteristics of Nurses��� Health Study I participants at start of follow-up. Supplementary Table 2. SNPs identified as proposed instruments for alcohol intake. Supplementary Table 3. A. Associations of proposed instrument (weighted allele score) with alcohol intake across baseline five-year age groups. B. Distributions of alcohol intake and proportion of heavy drinkers across quartiles of the proposed instrument (weighted allele score) across baseline five-year age groups. Supplementary Table 4. Associations of the proposed instrument (weighted allele score) with hazard of endometrial cancer across baseline five-year age groups.

Supplemental material, 2019-Supplement for Social Network Mapping and Functional Recovery Within 6 Months of Ischemic Stroke by Amar Dhand, Catherine E. Lang, Douglas A. Luke, Angela Kim, Karen Li, Liam McCafferty, Yi Mu, Bernard Rosner, Steven K. Feske and Jin-Moo Lee in Neurorehabilitation and Neural Repair

Although there is good evidence that emotions are associated with chronic airways obstruction, evidence for the influence of psychological factors on the level and decline of pulmonary function is sparse. Optimism has been linked to enhanced well-being, whereas pessimism has been identified as a risk factor for poor physical health. This investigation examines prospectively the effects of optimism versus pessimism on pulmonary function. Data are from the Veterans Administration Normative Aging Study, an ongoing cohort of older men. In 1986, 670 men completed the revised Minnesota Multiphasic Personality Inventory from which we derived the bipolar Revised Optimism-Pessimism Scale. During an average of 8 years of follow-up, an average of 3 pulmonary function exams were obtained. Men with a more optimistic explanatory style had significantly higher levels of forced expiratory volume in 1 sec (FEV1) and forced vital capacity (both p is lesser than 01). Interactions between time and optimism suggest that rate of decline in FEV1 over time was slower in men with a more optimistic explanatory style relative to men who were more pessimistic. These data are the first to link optimism with higher levels of pulmonary function and slower rate of pulmonary function decline in older men, a protective effect that is independent of smoking.

PurposeAccurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death.MethodsWe included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years).ResultsWithin 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p

BackgroundThere is limited evidence that supports etiologically distinct molecular subtypes of prostate cancer, the identification of which may improve prevention. Given their antioxidant properties, we hypothesized that lycopene and tomato sauce may be especially protective against diseases harboring the common gene fusion transmembrane protease, serine 2 (TMPRSS2):v-ets avian erythroblastosis virus E26 oncogene homolog (ERG).ObjectiveWe aimed to examine associations between estimated lycopene and tomato sauce intake and the risk of prostate cancer defined by ERG protein expression subtype.DesignOur study population consisted of a prospective cohort of 46,719 men from the Health Professionals Follow-Up Study. TMPRSS2:ERG was assessed by ERG immunohistochemistry on tumor tissue microarrays constructed from radical prostatectomy specimens. We used multivariable competing risk models to calculate HRs and 95% CIs for the risk of ERG-positive and, separately, ERG-negative disease. We implemented inverse probability weighting to account for evaluating ERG status only in surgically treated cases.ResultsDuring 23 y of follow-up, 5543 men were diagnosed with prostate cancer, among whom 884 were assayed for ERG (426 ERG-positive). With inclusion of only the latter cases, increasing cumulative average tomato sauce intake was associated with a decreased risk of prostate cancer overall (≥2 servings/wk compared with

ObjectiveFew prospective studies have examined separate forms of screen time in relation to adiposity. Our objective was to assess independent relations of television, electronic games (video/computer), and digital versatile disc (DVD)/videos and total screen time with change in adolescent BMI.MethodsUsing data from the 2004, 2006, and 2008 waves of the ongoing Growing up Today Study II, we assessed baseline and 2-year change in reported screen time in relation to concurrent change in BMI among 4287 girls and 3505 boys aged 9 to 16 years in 2004. Gender-specific models adjusted for previous BMI, age, race/ethnicity, growth/development, months between questionnaires, and physical activity.ResultsAmong girls and boys, each hour per day increase in reported television viewing was associated with a 0.09 increase in BMI (Ps < .001), and each hour per day increase in total screen time was associated with a 0.07 increase among girls and 0.05 increase among boys (Ps < .001). Among girls only, greater baseline television, games, and total screen time and change in DVDs/videos were associated with gains in BMI (Ps < .05). BMI gains associated with change in television and total screen time were stronger among overweight girls than lean girls (Ps-heterogeneity < .001).ConclusionsTelevision, which remains the steadiest source of food advertising, was most consistently associated with BMI gains. Among girls, electronic games and DVDs/videos were also related to increased BMI, possibly due to influences of product placements and advergames on diet and/or distracted eating. Adolescents, especially overweight adolescents, may benefit from reduced time with multiple types of media.

Selected risk factors (at time of mammography) by quartiles of percent mammographic density and menopausal status at mammogram among controls (NHS/NHSII). (DOC 61 kb)

BACKGROUND: Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. METHODS: We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. RESULT: The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. CONCLUSIONS: The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.

Differences in mammographic density measures by breast cancer risk factors in 1727 premenopausal and 1695 postmenopausal controls (NHS/NHSII). (DOC 65 kb)

Mediation analysis in women postmenopausal at mammogram, modeling interaction between the exposure and percent MD (NHS/NHS2). (DOC 124 kb)

Mediation analysis in women premenopausal at mammogram, modeling interaction between the exposure and percent MD (NHS/NHS2). (DOC 108 kb)

Table S1. Multivariate-adjusteda hazard ratios (HRs) and 95% confidence intervals for breast cancer by ER/PR status in relation to parity and lactation: Nurses’ Health Study (1976–2012) and Nurses’ Health Study II (1989–2013). Table S2. Multivariate-adjusteda hazard ratios (HRs) and 95% confidence intervals for breast cancer by ER status in relation to parity and lactation, stratified by time since last birth: Nurses’ Health Study (1976–2012) and Nurses’ Health Study II (1989–2013) (DOCX 30 kb)

Additional file 1: Table S1. Baseline characteristics across Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), European Prospective Investigation into Cancer and Nutrition (EPIC), Nurses’ Health Studies (NHS/NHSII), and New England Case-Control Study (NEC).Table S2. Age-adjusted association between predictors and CA125 levels above 35 U/mL in Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO).

Mediation analysis in women premenopausal at mammogram, modeling interaction between the exposure and percent MD (NHS/NHS2). (DOC 108 kb)

Mediation analysis in women postmenopausal at mammogram, modeling interaction between the exposure and percent MD (NHS/NHS2). (DOC 124 kb)

Selected risk factors (at time of mammography) by quartiles of percent mammographic density and menopausal status at mammogram among controls (NHS/NHSII). (DOC 61 kb)

Differences in mammographic density measures by breast cancer risk factors in 1727 premenopausal and 1695 postmenopausal controls (NHS/NHSII). (DOC 65 kb)

Additional file 1: Table S1. Baseline characteristics across Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), European Prospective Investigation into Cancer and Nutrition (EPIC), Nurses’ Health Studies (NHS/NHSII), and New England Case-Control Study (NEC).Table S2. Age-adjusted association between predictors and CA125 levels above 35 U/mL in Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO).

Table S1. Multivariate-adjusteda hazard ratios (HRs) and 95% confidence intervals for breast cancer by ER/PR status in relation to parity and lactation: Nurses’ Health Study (1976–2012) and Nurses’ Health Study II (1989–2013). Table S2. Multivariate-adjusteda hazard ratios (HRs) and 95% confidence intervals for breast cancer by ER status in relation to parity and lactation, stratified by time since last birth: Nurses’ Health Study (1976–2012) and Nurses’ Health Study II (1989–2013) (DOCX 30 kb)

BACKGROUND:Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. METHODS:We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. RESULT:The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. CONCLUSIONS:The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.