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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

Introduction Adverse effects of medications taken during pregnancy are traditionally studied through post-marketing pregnancy registries, which have limitations. Social media data may be an alternative data source for pregnancy surveillance studies. Objective The objective of this study was to assess the feasibility of using social media data as an alternative source for pregnancy surveillance for regulatory decision making. Methods We created an automated method to identify Twitter accounts of pregnant women. We identified 196 pregnant women with a mention of a birth defect in relation to their baby and 196 without a mention of a birth defect in relation to their baby. We extracted information on pregnancy and maternal demographics, medication intake and timing, and birth defects. Results Although often incomplete, we extracted data for the majority of the pregnancies. Among women that reported birth defects, 35% reported taking one or more medications during pregnancy compared with 17% of controls. After accounting for age, race, and place of residence, a higher medication intake was observed in women who reported birth defects. The rate of birth defects in the pregnancy cohort was lower (0.44%) compared with the rate in the general population (3%). Conclusions Twitter data capture information on medication intake and birth defects; however, the information obtained cannot replace pregnancy registries at this time. Development of improved methods to automatically extract and annotate social media data may increase their value to support regulatory decision making regarding pregnancy outcomes in women using medications during their pregnancies.

Introduction: The Healthy Heart Score (HHS) is a lifestyle-based risk prediction tool developed to predict cardiovascular disease (CVD) events in young and middle-aged adults. We evaluated the performance of the HHS to predict cardiovascular health (CVH). Methods: We applied the HHS to 3288 white and black adults ages 18-30 years at the CARDIA study baseline exam in 1985-6. The HHS includes age, smoking status, body mass index, alcohol intake (g/d), exercise (hours/week) and a diet score composed of self-reported daily intake of cereal fiber, fruits/vegetables, nuts, sugar-sweetened beverages, and red/processed meats. We tested the utility of the HHS to predict having all ideal CVH factors at the year 25 exam when participants were ages 43-55 years, defined as the simultaneous presence of unmedicated blood pressure Results: Mean (SD) age at baseline was 25.1 (3.6) years; 57% were female, 46% black, and 8% had at least one clinical CVD RF at baseline. Only 593 participants (18%) had all ideal CVH factors in middle age; the prevalence was lower in men compared to women, blacks compared to whites, and in those with vs. without clinical CVD RFs in young adulthood (Table). The HHS showed moderate discrimination for CVH prediction (Table). The HHS was adequately calibrated overall and within each sub-group. Conclusions: The HHS performs moderately well predicting CVH 25 years later when applied to young adults. Its reliance on self-reported, modifiable lifestyle factors makes it an attractive tool for the promotion of CVH across the lifespan.

Background: Intracranial hemorrhage (ICH) is a serious complication for patients with Hemophilia. In the Universal Data Collection (UDC) program (Witmer C et al., 2011), the rate of ICH in patients with hemophilia (PWH) was 3.9 per 1000 patient-years, with the majority of events (74.4%) taking place in patients with severe hemophilia. The incidence of mortality for hemophilia patients experiencing ICH approaches 20%. Prophylaxis with replacement factor remains the standard of care for PWH and is efficacious in reducing the incidence of ICH by 48% in HIV-negative, severe hemophilia patients and by 50% in severe hemophilia patients with inhibitors. Multiple investigational non-factor products are currently undergoing clinical evaluation for the treatment of Hemophilia. Given their different mechanisms of action, it is unknown if they will provide the same level of protection from ICH as the current standard of care. Aims: We aimed to estimate the sample size needed to power a clinical study adequately to detect a reduction in the risk of ICH in PWH using a hypothetical investigational agent compared to the rates of ICH observed in the UDC Hemophilia cohort. Methods: We conducted a simulated power analysis to quantify the number of patients needed to detect a reduction in risk of ICH in PWH exposed to a hypothetical investigational agent compared to the risk of ICH observed in the UDC cohort. Based on published data, we assumed an overall ICH risk of 1.9% over a 10-year follow-up (average follow-up 5 years). We assumed a risk of ICH of 1.7% over an average of 5 years in patients prescribed prophylaxis with replacement factor and a risk of mortality due to ICH within patients with hemophilia of 0.4%. We used a two-sided Z test with pooled variance and targeted the significance level of the test at 0.05 and the power at 0.8. Results: A sample size of 11,595 patients per group followed for 1 year is required to achieve 80% power to detect a 2-fold reduction in ICH risk for patients receiving investigational treatment compared to the overall observed risk in PWH receiving treatment at federally funded HTCs. Sample sizes of 13,650 would be required per group, to demonstrate a 2-fold reduction in ICH risk for patients receiving investigational treatment vs. patients prescribed prophylaxis with replacement factor. Additionally, sample sizes of 11,737 per group, would be required to achieve 80% power to detect a 2-fold reduction in the risk of ICH mortality in a population of patients treated with an investigational agent vs. patients with hemophilia under the current standard of care. Conclusions: The relatively high mortality associated with incidence of ICH, highlights a need to determine how efficacious investigational agents are in protecting from these events. However, the relatively infrequent incidence of ICH represents an obstacle for current clinical studies to adequately evaluate protection against these types of events. These analyses demonstrate that clinical trials of investigational non-factor products would require extremely large samples sizes in order to demonstrate a level of protection that is comparable or superior to that observed with the current standard of care. Additional clinical and scientific strategies will be required in order to thoroughly assess the efficacy of non-factor agents in protecting from ICH. Disclosures Gallo: Shire: Employment. Chiuve:Shire: Employment. Rowan:Baxalta: Consultancy. Valentino:Shire: Employment.

Background: Obesity is a well-established risk factor for coronary heart disease (CHD). However, the influence of other lifestyle and clinical risk factors on the association between body-mass index (BMI: weight in kg/height in m2) and CHD remains uncertain. Methods and Results: In the Danish Diet, Cancer and Health study, we followed 29,262 women and 26,088 men, 50 to 64 years of age, who were free of acute coronary syndrome (ACS) and cancer at baseline in 1993-1997. During a mean follow-up of 8 years, we documented 262 female and 845 male cases of ACS. Lifestyle risk factors were categorized as current smoking, 25 kg/m2) and obesity (BMI>30 kg/m2) were significantly associated with a higher risk of ACS, as were each of the individual lifestyle and clinical risk factors. Joint effects of obesity and each risk factor were close to additive. When all lifestyle factors were summed, obese individuals with the greatest number of lifestyle factors had the highest risk, but no synergism was observed. Similar results were seen for clinical risk factors. Of importance, a strong direct association between BMI and ACS was observed even among participants with the lowest number of other risk factors present. Conclusions: Our results illustrate that BMI is an important, independent predictor of ACS risk, even among individuals who have few CHD risk factors. BMI and other CHD risk factors appear to work in an additive fashion on risk of ACS. Background: Obesity is a well-established risk factor for coronary heart disease (CHD). However, the influence of other lifestyle and clinical risk factors on the association between body-mass index (BMI: weight in kg/height in m2) and CHD remains uncertain. Methods and Results: In the Danish Diet, Cancer and Health study, we followed 29,262 women and 26,088 men, 50 to 64 years of age, who were free of acute coronary syndrome (ACS) and cancer at baseline in 1993-1997. During a mean follow-up of 8 years, we documented 262 female and 845 male cases of ACS. Lifestyle risk factors were categorized as current smoking, 25 kg/m2) and obesity (BMI>30 kg/m2) were significantly associated with a higher risk of ACS, as were each of the individual lifestyle and clinical risk factors. Joint effects of obesity and each risk factor were close to additive. When all lifestyle factors were summed, obese individuals with the greatest number of lifestyle factors had the highest risk, but no synergism was observed. Similar results were seen for clinical risk factors. Of importance, a strong direct association between BMI and ACS was observed even among participants with the lowest number of other risk factors present. Conclusions: Our results illustrate that BMI is an important, independent predictor of ACS risk, even among individuals who have few CHD risk factors. BMI and other CHD risk factors appear to work in an additive fashion on risk of ACS.