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Background: Cardiovascular disease (CVD), including elevated blood pressure (BP), is known to promote Alzheimer’s disease (AD) risk. Although brain amyloid load is a recognized hallmark of pre-symptomatic AD, its relationship to increased BP is less known. The objective of this study was to examine the relationship of BP to brain estimates of amyloid-β (Aβ) and standard uptake ratio (SUVr). We hypothesized that increased BP is associated with increased SUVr. Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we stratified BP according to the Seventh Joint National Committee (JNC) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure Classification (JNC VII). Florbetapir (AV-45) SUVr was derived from the averaged frontal, anterior cingulate, precuneus, and parietal cortex relative to the cerebellum. A linear mixed-effects model enabled the elucidation of amyloid SUVr relationships to BP. The model discounted the effects of demographics, biologics, and diagnosis at baseline within APOE genotype groups. The least squares means procedure was used to estimate the fixed-effect means. All analyses were performed using the Statistical Analysis System (SAS). Results: In non-ɛ4 carrier MCI subjects, escalating JNC categories of BP was associated with increasing mean SUVr using JNC-4 as a reference point (low-normal (JNC1) p = 0.018; normal (JNC-1) p = 0.039; JNC-2 p = 0.018 and JNC-3 p = 0.04). A significantly higher brain SUVr was associated with increasing BP despite adjustment for demographics and biological variables in non-ɛ4 carriers but not in ɛ4-carriers. This observation supports the view that CVD risk may promote increased brain amyloid load, and potentially, amyloid-mediated cognitive decline. Conclusion: Increasing levels of JNC classification of BP is dynamically associated with significant changes in brain amyloid burden in non-ɛ4 carriers but not in ɛ4-carrier MCI subjects. Though not statistically significant, amyloid burden tended to decrease with increasing BP in ɛ4 homozygote, perhaps motivated by increased vascular resistance and the need for higher brain perfusion pressure.

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

Weight reduction continues to be first-line therapy in the treatment of hypertension (HTN). However, the long-term effect of bariatric malabsorptive surgical techniques such as Roux-en-Y Gastric Bypass (RYGB) surgery in the management of hypertension (HTN) is less clear. African Americans (AA) are disproportionately affected by obesity and hypertension and have inconsistent outcomes after bariatric surgery (BS). Despite a plethora of bariatric literature, data about characteristics of a predominantly AA bariatric hypertensive cohort including hypertension in obese (HIO) are scarce and underreported. The aims of this study were, (1) to describe the preoperative clinical characteristics of HIO with respect to HTN status and age, and (2) to identify predictors of HTN resolution one year after RYGB surgery in an AA bariatric cohort enrolled at the Howard University Center for Wellness and Weight Loss Surgery (HUCWWS). In the review of 169 AA bariatric patients, the average BMI was 48.50 kg/m2 and the average age was 43.86 years. Obese hypertensive patients were older (46 years vs. 37.89 years; p

Thomas V Fungwe,1 Julius S Ngwa,2 Oyonumo E Ntekim,1 Joanne S Allard,3 Sheeba Nadarajah,4 Saba Wolday,5 Oludolapo O Ogunlana,5 Steven P Johnson,5 Kakra Hughes,6 Daniel Larbi,5 Richard F Gillum,7 Thomas O Obisesan5 1Department of Nutritional Sciences, School of Nursing and Allied Health Sciences, Howard University, Washington, DC, USA; 2Division of Cardiology, Department of Medicine, Howard University, Washington, DC, USA; 3Division of Cardiology, Department of Physiology and Biophysics, Howard University, Washington, DC, USA; 4Division of Nursing, College of Nursing and Allied Health Sciences, Howard University, Washington, DC, USA; 5Division of Geriatrics, Department of Medicine and Clinical, Howard University Hospital, Washington, DC, USA; 6Department of Surgery, Howard University College of Medicine, Washington, DC, USA; 7Department of Internal Medicine, Howard University Hospital, Washington, DC, USACorrespondence: Thomas O ObisesanDivision of Geriatrics, Department of Medicine and Clinical/Translational Science Program, Howard University Hospital, 2041 Georgia Ave NW, Washington, DC 20060, USATel +1 202 865 3397Fax +1 202 865 3777Email tobisesan@howard.eduPurpose: Poor cardiorespiratory fitness (CRF) is linked to cognitive deterioration, but its effects on lipid heterogeneity and functional properties in older African American (AA) subjects with mild cognitive impairment (MCI) need elucidation. This study determined whether exercise training-induced changes in blood lipid particle sizes (LPS) were associated with CRF determined by VO2Max in elderly AAs with MCI. Given the pivotal role of brain-derived neurotrophic factor (BDNF) on glucose metabolism, and therefore, “diabetic dyslipidemia”, we also determined whether changes in LPS were associated with the levels of serum BDNF.Methods: This analysis included 17 of the 29 randomized elderly AAs with MCI who had NMR data at baseline and after a 6-month training. We used Generalized Linear Regression (GLM) models to examine cardiorespiratory fitness (VO2Max) effects on training-induced change in LPS in the stretch and aerobic groups. Additionally, we determined whether the level of BDNF influenced change in LPS.Results: Collectively, mean VO2Max (23.81±6.17) did not differ significantly between aerobic and stretch groups (difference=3.17±3.56, P=0.495). Training-related changes in very low-density lipoprotein, chylomicrons, and total low-density lipoprotein (LDL) particle sizes correlated significantly with VO2Max, but not after adjustment for age and gender. However, increased VO2Max significantly associated with reduced total LDL particle size after similar adjustments (P = 0.046). While stretch exercise associated with increased protective large high-density lipoprotein particle size, the overall effect was not sustained following adjustments for gender and age. However, changes in serum BDNF were associated with changes in triglyceride and cholesterol transport particle sizes (P < 0.051).Conclusion: Promotion of stretch and aerobic exercise to increase CRF in elderly AA volunteers with MCI may also promote beneficial changes in lipoprotein particle profile. Because high BDNF concentration may reduce CVD risk, training-related improvements in BDNF levels are likely advantageous. Large randomized studies are needed to confirm our observations and to further elucidate the role for exercise therapy in reducing CVD risk in elderly AAs with MCI.Keywords: lipoprotein, elderly, exercise, cardiovascular disease, Alzheimer’s disease, brain-derived neurotrophic factor, cardiovascular fitness

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e. platelets and megakaryocytes). Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

There is presently an ongoing debate on the relative merits of suggested criteria for spirometric airway obstruction. This study tests the null hypothesis that no superiority exists with the use of fixed ratio (FR) of forced expiratory volume in the first second (FEV1)/forced vital capacity (FVC) 0.7 versus less than lower limit predicted (LLN) criteria with or without FEV180% predicted in regards to future mortality.In 1988-1994 the Third National Health and Nutrition Examination Survey (NHANES III) measured FEV1 and FVC with mortality follow-up data through December 31, 2011. For this survival analysis 7472 persons aged 40 and over with complete data formed the analytic sample.There were a total of 3554 deaths. Weighted Cox proportional hazards regression revealed an increased hazard ratio in persons with both fixed ratio and lower limit of normal with a low FEV1 (1.79, p 0.0001), in those with fixed ratio only with a low FEV1 (1.77, p 0.0001), in those with abnormal fixed ratio only with a normal FEV1 (1.28, p 0.0001) compared with persons with no airflow obstruction (reference group). These remained significant after adjusting for demographic variables and other confounding variables.The addition of FEV1 80% of predicted increased the prognostic power of the fixed ratio0.7 and/or below the lower limit of predicted criteria for airway obstruction.

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10(−6)) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p

Little is understood about best educational strategies to engage diverse occupational therapy students in professional advocacy. The purpose of this research was to use design-based research (DBR) methods to design a novel educational intervention to promote professional engagement of diverse students over three subsequent years. This study used a pre/post design to design, implement, evaluate, and revise an educational program entitled “Student Engagement in Professional Advocacy” (SEPA). This manuscript reports on the first year of that study. The participants (N=27) were entry-level Master of Science occupational therapy students. The outcome measure was a survey measuring student knowledge, attitude, and participation specific to professional advocacy. All participants completed the pre and post surveys and participated in four SEPA educational modules. Significant differences were observed in pre and post scores in knowledge (p < 0.0001), action (p= 0.004), and attitude (p=0.012). This suggests that SEPA was effective at increasing student knowledge of, attitudes toward, and participation in professional advocacy. Pearson correlation of domains revealed a strong positive association between knowledge and attitude (p= 0.0003), actions and attitude (p = 0.0018), and action and knowledge (p=0.0262). This indicates that educational programs promoting professional engagement should address knowledge and attitudes and provide opportunities for participation. Qualitative data provided additional information on how students integrated professional engagement into their identity as students, and practical feedback on how to improve the program. Study findings supported the use of SEPA to promote student professional engagement. Further research is indicated, including conducting additional iterations of the project to continue to refine SEPA.

Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Simulation studies provide an important statistical tool in evaluating survival methods, requiring an appropriate data-generating process to simulate data for an underlying statistical model. Many studies with time-to-event outcomes use the Cox proportional hazard model. While methods for simulating such data with time-invariant predictors have been described, methods for simulating data with time-varying covariates are sorely needed. Here, we describe an approach for generating data for the Cox proportional hazard model with time-varying covariates when event times follow an Exponential or Weibull distribution. For each distribution, we derive a closed-form expression to generate survival times and link the time-varying covariates with the hazard function. We consider a continuous time-varying covariate measured at regular intervals over time, as well as time-invariant covariates, in generating time-to-event data under a number of scenarios. Our results suggest this method can lead to simulation studies with reliable and robust estimation of the association parameter in Cox-Weibull and Cox-Exponential models.

Objectives This study sought to evaluate the influence of race/ethnicity on the relationship between body mass index (BMI) and mortality in heart failure with preserved ejection fraction (HFpEF) and HF with reduced EF (HFrEF) patients. Background Prior studies demonstrated an “obesity paradox” among overweight and obese patients, where they have a better HF prognosis than normal weight patients. Less is known about the relationship between BMI and mortality among diverse patients with HF, particularly given disparities in obesity and HF prevalence. Methods The authors used Get With The Guidelines–Heart Failure data to assess the relationship between BMI and in-hospital mortality by using logistic regression modeling. The authors assessed 30-day and 1-year rates of all-cause mortality following discharge by using Cox regression modeling. Results A total of 39,647 patients with HF were included (32,434 [81.8%] white subjects; 3,809 [9.6%] black subjects; 1,928 [4.9%] Hispanic subjects; 544 [1.4%] Asian subjects; and 932 [2.3%] other subjects); 59.7% of subjects had HFpEF, and 30.7% were obese. More black and Hispanic patients had Class I or higher obesity (BMI ≥30 kg/m 2 ) than whites, Asians, or other racial/ethnic groups (p 2 with a small risk increase above 30 kg/m 2 (BMI: 30 vs. 18.5 kg/m 2 ), hazard ratio (HR) of 0.63 (95% confidence interval [CI]: 0.54 to 0.73). A modest relationship was observed in HFrEF subjects (BMI: 30 vs. 18.5 kg/m 2 ; HR: 0.73; 95% CI: 0.60 to 0.89), with no risk increase above 30 kg/m 2 . There were no significant interactions between BMI and race or ethnicity related to 30-day mortality (p > 0.05). Conclusions This work is one of the first suggesting the obesity paradox for 30-day mortality exists at all BMI levels in HFrEF but not in patients with HFpEF. Higher BMI was associated with lower 30-day mortality across racial/ethnic groups in a manner inconsistent with the J-shaped relationship noted for coronary artery disease. The differential slope of obesity and mortality among HFpEF and patients with HFrEF potentially suggests differing mechanistic factors, requiring further exploration.

Background: It is increasingly evident that high blood pressure can promote reduction in global and regional brain volumes. While these effects may preferentially affect the hippocampus, reports are inconsistent. Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we examined the relationships of hippocampal volume to pulse pressure (PPR) and systolic (SBP) and diastolic (DBP) blood pressure according to apolipoprotein (APOE) ɛ4 positivity and cognitive status. The ADNI data included 1,308 participants: Alzheimer disease (AD = 237), late mild cognitive impairment (LMCI = 454), early mild cognitive impairment (EMCI = 254), and cognitively normal (CN = 365), with up to 24 months of follow-up. Results: Higher quartiles of PPR were significantly associated with lower hippocampal volumes (Q1 vs. Q4, p = 0.034) in the CN and AD groups, but with increasing hippocampal volume (Q1, p = 0.008; Q2, p = 0.020; Q3, p = 0.017; Q4 = reference) in the MCI groups. In adjusted stratified analyses among non-APOE ɛ4 carriers, the effects in the CN (Q1 vs. Q4, p = 0.006) and EMCI groups (Q1, p = 0.002; Q2, p = 0.013; Q3, p = 0.002; Q4 = reference) remained statistically significant. Also, higher DBP was significantly associated with higher hippocampal volume (p = 0.002) while higher SBP was significantly associated with decreasing hippocampal volume in the EMCI group (p = 0.015). Conclusion: Changes in PPR, SBP, and DBP differentially influenced hippocampal volumes depending on the cognitive and APOE genotypic categories.

The effects of oral intake of capsaicin oleoresin (CO) on arterial blood pressure was examined in male spontaneously hypertensive (SHR) rats. Animals were randomized into control (C), capsaicin oleoresin (CO), capsaicin oleoresin-irbesartan (COI) and capsaicin oleoresin-amlodipine besylate (COAB) fed groups. The experimental groups received 50 mg of Capsaicin Oleoresin only, CO with 25 mg of Irbesartan, and CO with 2.5 mg of Amlodipine per kilogram of body weight every other day for 4 weeks. Blood pressure was measured weekly by tail-cuff for 4 weeks. Biological samples were obtained at baseline, day 18, and day 34 (terminal) for aldosterone, arginine vasopressin (AVP), renin, 6-Keto-PGF-1α, Prostaglandin E2, catecholamines, adrenocorticotropic hormone (ACTH) levels, and serum minerals. Statistical significance was set at p≤0.05. The results trended towards decreasing (p=0.190) systolic blood pressure (SBP) in the COAB and CO compared to the COI and control groups. However, significance among group differences were observed for urine aldosterone and AVP, and serum sodium, calcium, magnesium, potassium, and catecholamine (p≤0.05), but not for ACTH, renin, and 6-Keto-PGF-1α levels. Capsaicin treatment was non-significantly associated with decreased SBP in SHR. These findings suggested that the effects of Capsaicin on SPB may involve aldosterone and arteriolar vascular tone. Key words: Capsaicin oleoresin, amlodipine besylate, irbesartan, arginine vasopressin, spontaneously hypertensive rats.

Background Regular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences. Objective To identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption. Design We conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts. Results Heritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (Freq(A) = 0.015) was associated with 0.029 servings/day (similar to 1 serving/month) lower fish consumption (P = 1.96x10(-8)). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10(-7)). Post-hoc calculations demonstrated 95% statistical power to detect a genetic variant associated with effect size of 0.05% for fish and 0.08% for EPA+DHA. Conclusions These novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.

Insulin resistance syndrome (IRS), high blood pressure, elevated blood glucose and triacylglycerol-rich lipoproteins (TG-RL), as well as low levels of high-density lipoprotein (HDL-C) are disorders that combine to define metabolic syndrome (MetS). Metabolic syndrome is on the rise in the United States and is believed to be a powerful predictor of risk for diabetes and coronary events. Modulation of the activity of lipoprotein lipase (LPL) in MetS affects lipolysis of TG-RL, which has a direct correlation with the levels of plasma HDL-C. This study examined if increasing LPL activity by dietary means in a model for MetS leads to reduced IRS and increase in plasma HDL-C concentration. Ninety day-old Spontaneously Hypertensive Stroke-Prone male rats were originally fed lab chow diet for seven days. This was followed by feeding a fatty acid diet for 7 days containing one of the following: triolein (TO), trans fatty acids-rich (TFA, margarine) and 0 fatty acids (Control) with /without an LPL-rising drug (NO-1866) by gavages (5 mg or 25 mg/kg b. wt.). The results show that blood glucose and triacylglycerol levels were decreased with NO-1886. HDL-C levels increased with NO-1866 in the control and triolein group but not in the TFA group. Animals in the triolein group had higher levels of phospholipids and lower levels of insulin. Inclusion of NO-1866 lowered HOMA-IR by almost 40% in the control and the TFA group, but no further reduction was observed in the TO group. The control TFA groups had up to 45% higher HOMA-IR than the TO group. Overall the data suggest that raising the activity of lipoprotein lipase by dietary means, including the feeding of monounsaturated fat may increase HDL-C, reduce plasma triacylglycerol and other indices of MetS risk, and thus may decrease the incidence of vascular complications through the normalization of lipid metabolism in subjects with MetS.

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177 330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m2, P = 1.9 × 10-105), and all participants (0.30 [0.30, 0.35] kg/m2, P = 3.6 × 10-107). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10-16), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10-9) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 × 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 × 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery., The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (102318; 104781, 120315, 123885, 129619, 286284, 134309, 126925, 121584, 124282, 129378, 117787, 250207, 258753, 41071, 77299, 124243, 1114194, 24300796); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la Recherche; Agency for Health Care Policy Research (HS06516); Age UK Research into Ageing Fund; Åke Wiberg Foundation; ALF/LUA Research Grant in Gothenburg; ALFEDIAM; ALK-Abello´ A/S (Hørsholm, Denmark); American Heart Association (13POST16500011, 10SDG269004); Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; AstraZeneca; Australian Associated Brewers; Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498); Avera Research Institute; Bayer Diagnostics; Becton Dickinson; Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007); Biocentrum Helsinki; Boston Obesity Nutrition Research Center (DK46200); British Heart Foundation (RG/10/12/28456, SP/04/002); Canada Foundation for Innovation; Canadian Institutes of Health Research (FRN-CCT-83028); Cancer Research UK; Cardionics; Center for Medical Systems Biology; Center of Excellence in Complex Disease Genetics and SALVECenter of Excellence in Genomics (EXCEGEN); Chief Scientist Office of the Scottish Government; City of Kuopio; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF–1333-00124, DFF–1331-007308); Danish Diabetes Academy; Danish Medical Research Council; Department of Psychology and Education of the VU University Amsterdam; Diabetes Hilfs- und Forschungsfonds Deutschland; Dutch Brain Foundation; Dutch Ministry of Justice; Emil Aaltonen Foundation; Erasmus Medical Center; Erasmus University; Estonian Government (IUT20-60, IUT24-6); Estonian Ministry of Education and Research (3.2.0304.11-0312); European Commission (230374, 284167, 323195, 692145, FP7 EurHEALTHAgeing-277849, FP7 BBMRI-LPC 313010, nr 602633, HEALTH-F2-2008-201865-GEFOS, HEALTH-F4-2007-201413, FP6 LSHM-CT-2004-005272, FP5 QLG2-CT-2002-01254, FP6 LSHG-CT-2006-01947, FP7 HEALTH-F4-2007-201413, FP7 279143, FP7 201668, FP7 305739, FP6 LSHG-CT-2006-018947, HEALTH-F4-2007-201413, QLG1-CT-2001-01252); European Regional Development Fund; European Science Foundation (EuroSTRESS project FP-006, ESF, EU/QLRT-2001-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012, 03IS2061A); Federal State of Mecklenburg - West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Food Standards Agency; Fondation de France; Fonds Santé; Genetic Association Information Network of the Foundation for the National Institutes of Health; German Diabetes Association; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); German Research Council (SFB-1052, SPP 1629 TO 718/2-1); GlaxoSmithKline; Göran Gustafssons Foundation; Göteborg Medical Society; Health and Safety Executive; Heart Foundation of Northern Sweden; Icelandic Heart Association; Icelandic Parliament; Imperial College Healthcare NHS Trust; INSERM, Réseaux en Santé Publique, Interactions entre les déterminants de la santé; Interreg IV Oberrhein Program (A28); Italian Ministry of Economy and Finance; Italian Ministry of Health (ICS110.1/RF97.71); John D and Catherine T MacArthur Foundation; Juho Vainio Foundation; King's College London; Kjell och Märta Beijers Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); Leiden University Medical Center; Lilly; LMUinnovativ; Lundbeck Foundation; Lundberg Foundation; Medical Research Council of Canada; MEKOS Laboratories (Denmark); Merck Santé; Mid-Atlantic Nutrition Obesity Research Center (P30 DK72488); Ministère de l’Économie, de l’Innovation et des Exportations; Ministry for Health, Welfare and Sports of the Netherlands; Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science of the Netherlands; MRC Human Genetics Unit; MRC-GlaxoSmithKline Pilot Programme Grant (G0701863); Municipality of Rotterdam; Netherlands Bioinformatics Centre (2008.024); Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Genomics Initiative; Netherlands Organisation for Health Research and Development (904-61-090, 985-10-002, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192); Netherlands Organisation for Health Research and Development (2010/31471/ZONMW); Netherlands Organisation for Scientific Research (10-000-1002, GB-MW 940-38-011, 100-001-004, 60-60600-97-118, 261-98-710, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MaGW 452-04-314, GB-MaGW 452-06-004, 175.010.2003.005, 175.010.2005.011, 481-08-013, 480-05-003, 911-03-012); Neuroscience Campus Amsterdam; NHS Foundation Trust; Novartis Pharmaceuticals; Novo Nordisk; Office National Interprofessionel des Vins; Paavo Nurmi Foundation; Påhlssons Foundation; Päivikki and Sakari Sohlberg Foundation; Pierre Fabre; Republic of Croatia Ministry of Science, Education and Sport (108-1080315-0302); Research Centre for Prevention and Health, the Capital Region for Denmark; Research Institute for Diseases in the Elderly (014-93-015, RIDE2); Roche; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Executive Health Department (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du Diabète; State of Bavaria; Stroke Association; Swedish Diabetes Association; Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20140543); Swedish Research Council (2015-03657); Swedish Medical Research Council (K2007-66X-20270-01-3, 2011-2354); Swedish Society for Medical Research; Swiss National Science Foundation (33CSCO-122661, 33CS30-139468, 33CS30-148401); Tampere Tuberculosis Foundation; The Marcus Borgström Foundation; The Royal Society; The Wellcome Trust (084723/Z/08/Z, 088869/B/09/Z); Timber Merchant Vilhelm Bangs Foundation; Topcon; Torsten and Ragnar Söderberg's Foundation; UK Department of Health; UK Diabetes Association; UK Medical Research Council (MC_U106179471, G0500539, G0600705, G0601966, G0700931, G1002319, K013351, MC_UU_12019/1); UK National Institute for Health Research BioResource Clinical Research Facility and Biomedical Research Centre; UK National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre; UK National Institute for Health Research (RP-PG-0407-10371); Umeå University Career Development Award; United States – Israel Binational Science Foundation Grant (2011036); University Hospital Oulu (75617); University Medical Center Groningen; University of Tartu (SP1GVARENG); National Institutes of Health (AG13196, CA047988, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSC271201100004C, HHSN268200900041C, HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, HHSN268201500001I, HL36310, HG002651, HL034594, HL054457, HL054481, HL071981, HL084729, HL119443, HL126024, N01-AG12100, N01-AG12109, N01-HC25195, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-HC55021, N01-HC55022, N01-HD95159, N01-HD95160, N01-HD95161, N01-HD95162, N01-HD95163, N01-HD95164, N01-HD95165, N01-HD95166, N01-HD95167, N01-HD95168, N01-HD95169, N01-HG65403, N02-HL64278, R01-HD057194, R01-HL087641, R01-HL59367, R01HL-086694, R01-HL088451, R24-HD050924, U01-HG-004402, HHSN268200625226C, UL1-RR025005, UL1-RR025005, UL1-TR-001079, UL1-TR-00040, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, DA12854, MH081802, MH66206, R01-D004215701A, R01-DK075787, R01-DK089256, R01-DK8925601, R01-HL088451, R01-HL117078, R01-DK062370, R01-DK072193, DK091718, DK100383, DK078616, 1Z01-HG000024, HL087660, HL100245, R01DK089256, 2T32HL007055-36, U01-HL072515-06, U01-HL84756, NIA-U01AG009740, RC2-AG036495, RC4-AG039029, R03 AG046389, 263-MA-410953, 263-MD-9164, 263-MD-821336, U01-HG004802, R37CA54281, R01CA63, P01CA33619, U01-CA136792, U01-CA98758, RC2-MH089951, MH085520, R01-D0042157-01A, MH081802, 1RC2-MH089951, 1RC2-MH089995, 1RL1MH08326801, U01-HG007376, 5R01-HL08767902, 5R01MH63706:02, HG004790, N01-WH22110, U01-HG007033, UM1CA182913, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, 44221); USDA National Institute of Food and Agriculture (2007-35205-17883); Västra Götaland Foundation; Velux Foundation; Veterans Affairs (1 IK2 BX001823); Vleugels Foundation; VU University’s Institute for Health and Care Research (EMGO+, HEALTH-F4-2007-201413) and Neuroscience Campus Amsterdam; Wellcome Trust (090532, 091551, 098051, 098381); Wissenschaftsoffensive TMO; and Yrjö Jahnsson Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution., A full list of acknowledgments appears in the Supplementary Note 4. Co-author A.J.M.d.C. recently passed away while this work was in process. This work was performed under the auspices of the Genetic Investigation of ANthropometric Traits (GIANT) consortium. We acknowledge the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium for encouraging CHARGE studies to participate in this effort and for the contributions of CHARGE members to the analyses conducted for this research. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (41071, 77299, 102318, 110413, 117787, 121584, 123885, 124243, 124282, 126925, 129378, 134309, 286284); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la 359 Recherche; Agency for Health Care Policy Research (HS06516); ALF/LUA research grant in Gothenburg; ALFEDIAM; ALK-Abelló A/S; Althingi; American Heart Association (13POST16500011); Amgen; Andrea and Charles Bronfman Philanthropies; Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; Australian National Health and Medical Research Council (241944, 339462, 389875, 389891, 389892, 389927, 389938, 442915, 442981, 496739, 552485, 552498); Avera Institute; Bayer Diagnostics; Becton Dickinson; BHF (RG/14/5/30893); Boston Obesity Nutrition Research Center (DK46200), Bristol-Myers Squibb; British Heart Foundation (RG/10/12/28456, RG2008/08, RG2008/014, SP/04/002); Medical Research Council of Canada; Canadian Institutes for Health Research (FRCN-CCT-83028); Cancer Research UK; Cardionics; Cavadis B.V., Center for Medical Systems Biology; Center of Excellence in Genomics; CFI; CIHR; City of Kuopio; CNAMTS; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF-1333-00124, DFF-1331-00730B); County Council of Dalarna; Dalarna University; Danish Council for Strategic Research; Danish Diabetes Academy; Danish Medical Research Council; Department of Health, UK; Development Fund from the University of Tartu (SP1GVARENG); Diabetes Hilfs- und Forschungsfonds Deutschland; Diabetes UK; Diabetes Research and Wellness Foundation Fellowship; Donald W. Reynolds Foundation; Dr Robert Pfleger-Stiftung; Dutch Brain Foundation; Dutch Diabetes Research Foundation; Dutch Inter University Cardiology Institute; Dutch Kidney Foundation (E033); Dutch Ministry of Justice; the DynaHEALTH action No. 633595, Economic Structure Enhancing Fund of the Dutch Government; Else Kröner-Fresenius-Stiftung (2012_A147, P48/08//A11/08); Emil Aaltonen Foundation; Erasmus University Medical Center Rotterdam; Erasmus MC and Erasmus University Rotterdam; the Municipality of Rotterdam; Estonian Government (IUT20-60, IUT24-6); Estonian Research Roadmap through the Estonian Ministry of Education and Research (3.2.0304.11-0312); European Research Council (ERC Starting Grant and 323195:SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC); European Regional Development Fund; European Science Foundation (EU/QLRT-2001-01254); European Commission (018947, 018996, 201668, 223004, 230374, 279143, 284167, 305739, BBMRI-LPC-313010, HEALTH-2011.2.4.2-2-EU-MASCARA, HEALTH-2011-278913, HEALTH-2011-294713-EPLORE, HEALTH-F2-2008-201865-GEFOS, HEALTH-F2-2013-601456, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550-HYPERGENES, HEALTH-F7-305507 HOMAGE, IMI/115006, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHM-CT-2004-005272, LSHM-CT-2006-037697, LSHM-CT-2007-037273, QLG1-CT-2002-00896, QLG2-CT-2002-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A, 03ZIK012); Federal State of Mecklenburg-West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Fondation Leducq; Food Standards Agency; Foundation for Strategic Research; French Ministry of Research; FRSQ; Genetic Association Information Network (GAIN) of the Foundation for the NIH; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); GlaxoSmithKline; Greek General Secretary of Research and Technology; Göteborg Medical Society; Health and Safety Executive; Healthcare NHS Trust; Healthway; Western Australia; Heart Foundation of Northern Sweden; Helmholtz Zentrum München—German Research Center for Environmental Health; Hjartavernd; Ingrid Thurings Foundation; INSERM; InterOmics (PB05 MIUR-CNR); INTERREG IV Oberrhein Program (A28); Interuniversity Cardiology Institute of the Netherlands (ICIN, 09.001); Italian Ministry of Health (ICS110.1/RF97.71); Italian Ministry of Economy and Finance (FaReBio di Qualità); Marianne and Marcus Wallenberg Foundation; the Ministry of Health, Welfare and Sports, the Netherlands; J.D.E. and Catherine T, MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International; KfH Stiftung Präventivmedizin e.V.; King's College London; Knut and Alice Wallenberg Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); La Fondation de France; Leenaards Foundation; Lilly; LMUinnovativ; Lundberg Foundation; Magnus Bergvall Foundation; MDEIE; Medical Research Council UK (G0000934, G0601966, G0700931, MC_U106179471, MC_UU_12019/1); MEKOS Laboratories; Merck Santé; Ministry for Health, Welfare and Sports, The Netherlands; Ministry of Cultural Affairs of Mecklenburg-West Pomerania; Ministry of Economic Affairs, The Netherlands; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science, The Netherlands; Ministry of Science, Education and Sport in the Republic of Croatia (108-1080315-0302); MRC centre for Causal Analyses in Translational Epidemiology; MRC Human Genetics Unit; MRC-GlaxoSmithKline pilot programme (G0701863); MSD Stipend Diabetes; National Institute for Health Research; Netherlands Brain Foundation (F2013(1)-28); Netherlands CardioVascular Research Initiative (CVON2011-19); Netherlands Genomics Initiative (050-060-810); Netherlands Heart Foundation (2001 D 032, NHS2010B280); Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) (56-464-14192, 60-60600-97-118, 100-001-004, 261-98-710, 400-05-717, 480-04-004, 480-05-003, 481-08-013, 904-61-090, 904-61-193, 911-11-025, 985-10-002, Addiction-31160008, BBMRI–NL 184.021.007, GB-MaGW 452-04-314, GB-MaGW 452-06-004, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MW 940-38-011, Middelgroot-911-09-032, NBIC/BioAssist/RK 2008.024, Spinozapremie 175.010.2003.005, 175.010.2007.006); Neuroscience Campus Amsterdam; NHS Foundation Trust; National Institutes of Health (1RC2MH089951, 1Z01HG000024, 24152, 263MD9164, 263MD821336, 2R01LM010098, 32100-2, 32122, 32108, 5K99HL130580-02, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, AG13196, CA047988, DA12854, DK56350, DK063491, DK078150, DK091718, DK100383, DK078616, ES10126, HG004790, HHSN268200625226C, HHSN268200800007C, HHSN268201200036C, HHSN268201500001I, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C, HL043851, HL45670, HL080467, HL085144, HL087660, HL054457, HL119443, HL118305, HL071981, HL034594, HL126024, HL130114, KL2TR001109, MH66206, MH081802, N01AG12100, N01HC55015, N01HC55016, N01C55018, N01HC55019, N01HC55020, N01HC55021, N01HC55022, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC95159, N01HC95160, N01HC95161, N01HC95162, N01HC95163, N01HC95164, N01HC95165, N01HC95166, N01HC95167, N01HC95168, N01HC95169, N01HG65403, N01WH22110, N02HL6‐4278, N01-HC-25195, P01CA33619, R01HD057194, R01HD057194, R01AG023629, R01CA63, R01D004215701A, R01DK075787, R01DK062370, R01DK072193, R01DK075787, R01DK089256, R01HL53353, R01HL59367, R01HL086694, R01HL087641, R01HL087652, R01HL103612, R01HL105756, R01HL117078, R01HL120393, R03 AG046389, R37CA54281, RC2AG036495, RC4AG039029, RPPG040710371, RR20649, TW008288, TW05596, U01AG009740, U01CA98758, U01CA136792, U01DK062418, U01HG004402, U01HG004802, U01HG007376, U01HL080295, UL1RR025005, UL1TR000040, UL1TR000124, UL1TR001079, 2T32HL007055-36, T32GM074905, HG002651, HL084729, N01-HC-25195, UM1CA182913); NIH, National Institute on Aging (Intramural funding, NO1-AG-1-2109); Northern Netherlands Collaboration of Provinces; Novartis Pharma; Novo Nordisk; Novo Nordisk Foundation; Nutricia Research Foundation (2016-T1); ONIVINS; Parnassia Bavo group; Pierre Fabre; Province of Groningen; Päivikki and Sakari Sohlberg Foundation; Påhlssons Foundation; Paavo Nurmi Foundation; Radboud Medical Center Nijmegen; Research Centre for Prevention and Health, the Capital Region of Denmark; the Research Institute for Diseases in the Elderly; Research into Ageing; Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center; Roche; Royal Society; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Government Health Directorates, Chief Scientist Office (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du 358 Diabète; State of Bavaria; Stiftelsen för Gamla Tjänarinnor; Stockholm County Council (560183, 592229); Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council; Stroke Association; Swedish Diabetes Association; Swedish Diabetes Foundation (2013-024); Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20120197, 20150711); Swedish Research Council (0593, 8691, 2012-1397, 2012-1727, and 2012-2215); Swedish Society for Medical Research; Swiss Institute of Bioinformatics; Swiss National Science Foundation (3100AO-116323/1, 31003A-143914, 33CSCO-122661, 33CS30-139468, 33CS30-148401, 51RTP0_151019); Tampere Tuberculosis Foundation; Technology Foundation STW (11679); The Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Ministry of the Flemish Community (G.0880.13, G.0881.13); The Great Wine Estates of the Margaret River Region of Western Australia; Timber Merchant Vilhelm Bangs Foundation; Topcon; Tore Nilsson Foundation; Torsten and Ragnar Söderberg's Foundation; United States – Israel Binational Science Foundation (Grant 2011036), Umeå University; University Hospital of Regensburg; University of Groningen; University Medical Center Groningen; University of Michigan; University of Utrecht; Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) (b2011036); Velux Foundation; VU University’s Institute for Health and Care Research; Västra Götaland Foundation; Wellcome Trust (068545, 076113, 079895, 084723, 088869, WT064890, WT086596, WT098017, WT090532, WT098051, 098381); Wissenschaftsoffensive TMO; Yrjö Jahnsson Foundation; and Åke Wiberg Foundation. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI); the National Institutes of Health (NIH); or the U.S. Department of Health and Human Services.

OBJECTIVES: The aims of this study were to develop and implement a “Food as Medicine” intervention using quantitative methods supported by health literacy and food choice and behavioral models to target chronic diseases under free-living conditions in adults diagnosed with chronic diseases. The hypothesis of this study is that “Food as Medicine” nutrition and lifestyle intervention sessions will result in significant improvements in food choices, as well as shopping and cooking behaviors. METHODS: The Food as Medicine (FAM) study is a community-based pilot study that measured the effectiveness of a nutrition intervention to improve chronic disease risk factors and outcomes among African Americans with either pre-diabetes, type 2 diabetes mellitus, hypertension, elevated total blood cholesterol levels, obesity or a combination of these disorders. The study enrolled patients who were attending both Howard University Family Medicine and Internal Medicine Practices, and were residents of wards seven or eight in the District of Columbia. Fifty-four participants were enrolled and assigned to five cohorts, which consisted of five group sessions over three months, and focused on improving diet and health literacy through nutritionist-led, culturally-tailored, nutrition education classes that included health literacy, mindfulness exercises, and cooking demonstrations. RESULTS: After program completion, FAM participants demonstrated significant improvements in all outcome measures of interest: healthy dietary patterns (P

Pump thrombosis remains the catastrophic complication of Left ventricular assist device (LVAD) therapy. There is limited data available on the trend of in-hospital outcomes associated with pump thrombosis in LVAD patients. Patients with LVAD placement in the Healthcare Cost and Utilization Project

The HEART score was designed to aid in disposition of patients presenting to the emergency department (ED) with chest pain (CP). Using prospectively collected data from the local and regional electronic health record systems, CP presentations to the ED of an academic institution were investigated.

Circulating blood CD34(+) cells consist of hematopoietic stem/progenitor cells, angiogenic cells, and endothelial cells. In addition to their clinical use in hematopoietic stem cell transplantation, CD34(+) cells may also promote therapeutic neovascularization. Therefore, understanding the factors that influence circulating CD34(+) cell frequency has wide implications for vascular biology in addition to stem cell transplantation. In the present study, we examined the clinical and genetic characteristics associated with circulating CD34(+) cell frequency in a large, community-based sample of 1786 Framingham Heart Study participants.Among subjects without cardiovascular disease (n = 1595), CD34(+) frequency was inversely related to older age, female sex, and smoking. CD34(+) frequency was positively related to weight, serum total cholesterol, and statin therapy. Clinical covariates accounted for 6.3% of CD34(+) variability. CD34(+) frequency was highly heritable (h(2) = 54%; P < .0001). Genome-wide association analysis of CD34(+) frequency identified suggestive associations at several loci, including OR4C12 (chromosome 11; P = 6.7 × 10(-7)) and ENO1 and RERE (chromosome 1; P = 8.8 × 10(-7)). CD34(+) cell frequency is reduced in older subjects and is influenced by environmental factors including smoking and statin use. CD34(+) frequency is highly heritable. The results of the present study have implications for therapies that use CD34(+) cell populations and support efforts to better understand the genetic mechanisms that underlie CD34(+) frequency.

Background: Macronutrient intake varies substantially between individuals, and there is evidence that this variation is partly accounted for by genetic variants. Objective: The objective of the study was to identify common genetic variants that are associated with macronutrient intake. Design: We performed 2-stage genome-wide association (GWA) meta-analysis of macronutrient intake in populations of European descent. Macronutrients were assessed by using food-frequency questionnaires and analyzed as percentages of total energy consumption from total fat, protein, and carbohydrate. From the discovery GWA (n = 38,360), 35 independent loci associated with macronutrient intake at P < 5 x 10(-6) were identified and taken forward to replication in 3 additional cohorts (n = 33,533) from the DietGen Consortium. For one locus, fat mass obesity-associated protein (FTO), cohorts with Illumina MetaboChip genotype data (n 7724) provided additional replication data. Results: A variant in the chromosome 19 locus (rs838145) was associated with higher carbohydrate (beta +/- SE: 0.25 +/- 0.04%; P = 1.68 x 10(-8)) and lower fat (beta = SE: -0.21 +/- 0.04%; P = 1.57 x 10(-9)) consumption. A candidate gene in this region, fibroblast growth factor 21 (FGF21), encodes a fibroblast growth factor involved in glucose and lipid metabolism. The variants in this locus were associated with circulating FGF21 protein concentrations (P < 0.05) but not mRNA concentrations in blood or brain. The body mass index (BMI) increasing allele of the FTO variant (rs1421085) was associated with higher protein intake (beta +/- SE: 0.10 +/- 0.02%; P = 9.96 x 10(-10)), independent of BMI (after adjustment for BMI, beta +/- SE: 0.08 +/- 0.02%; P = 3.15 x 10(-7)). Conclusion: Our results indicate that variants in genes involved in nutrient metabolism and obesity are associated with macronutrient consumption in humans. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetic and Environmental Determinants of Triglycerides), NCT01331512 (InCHIANTI Study), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

Possession of the Apolipoprotein E (APOE) gene ε4 allele is the most prevalent genetic risk factor for late onset Alzheimer’s disease (AD). Recent evidence suggests that APOE genotype differentially affects the expression of brain-derived neurotrophic factor (BDNF). Notably, aerobic exercise-induced upregulation of BDNF is well documented; and exercise has been shown to improve cognitive function. As BDNF is known for its role in neuroplasticity and survival, its upregulation is a proposed mechanism for the neuroprotective effects of physical exercise. In this pilot study designed to analyze exercise-induced BDNF upregulation in an understudied population, we examined the effects of ApoEε4 (ε4) carrier status on changes in BDNF expression after a standardized exercise program. African Americans, age 55 years and older, diagnosed with mild cognitive impairment participated in a six-month, supervised program of either stretch (control treatment) or aerobic (experimental treatment) exercise. An exercise-induced increase in VO2Max was detected only in male participants. BDNF levels in serum were measured using ELISA. Age, screening MMSE scores and baseline measures of BMI, VO2Max, and BDNF did not differ between ε4 carriers and non-ε4 carriers. A significant association between ε4 status and serum BDNF levels was detected. Non-ε4 carriers showed a significant increase in BDNF levels at the 6 month time point while ε4 carriers did not. We believe we have identified a relationship between the ε4 allele and BDNF response to physiologic adaptation which likely impacts the extent of neuroprotective benefit gained from engagement in physical exercise. Replication of our results with inclusion of diverse racial cohorts, and a no-exercise control group will be necessary to determine the scope of this association in the general population.

Background: Current guidelines do not support the routine use of computed tomography (CT) scan of the head in the diagnostic workup of syncope. There is a lack of research to support whether these guidelines apply to the Black population. Aims: This study aims to evaluate the yield of neuroimaging in the evaluation of Syncope in a predominantly Black patient population and to test whether current guidelines based on studies conducted in other populations hold true in this group. Material and Methods: A retrospective review of records of 151 patients admitted to a University Hospital with Syncope from 2011 to 2014 was performed. Data collected include CT head, magnetic resonance imaging of the brain, magnetic resonance angiogram, electroencephalogram, and orthostatic vital signs. Demographic data, admitting service, and comorbid conditions were identified. Syncope was classified as cardiogenic, orthostatic, vasovagal, situational, or undetermined. Statistical analysis was performed to determine which diagnostic tools were useful in identifying the potential causes of syncope. Data analysis was conducted using the Statistical Analysis System software 9.3 (SAS Institute, Cary, NC) and Statistical Analysis and Graphics (NCSS 9.0.7, Kaysville, UT). Results: One hundred and twenty eight (84.8%) of the patients were Black. The average age was 56.62 ± 18.78 standard deviation and 68.2% (103) were female. One hundred and fourteen patients (75.5%) had a CT brain. Five out of 114 patients had an acute abnormality on CT (4.4%). Only 1 of these 5 patients had an abnormality that was related to syncope. CT brain (P = 0.978) was not found to be predictive of underlying etiology of syncope despite high frequency of use. Conclusions: CT head was not useful in determining the etiology of syncope in a predominantly Black population. Current guidelines and studies conducted in other populations have detected similar findings.

Background: Obesity predisposes patients to pulmonary complication: however, the association in critically ill Black populations is poorly understood. Objectives: To determine the association of body mass index (BMI) on pulmonary complications in Black patients in the ICU. Methods: A retrospective chart review of adult patients (n=605) aged 18 years and older with a medical ICU stay of greater than 24 hours during 2012 was performed. Patients were categorized based on BMI as under weight (BMI≤18.4kg/m2), normal weight (18.5-24.9kg/m2), overweight (25-29.9kg/m2), class I (30-34.99kg/m2), class II (35-39.99kg/m2) or, class III obesity (≥40kg/m2). Primary outcomes were need for and duration of mechanical ventilation and rate of ARDS. Secondary outcomes included ICU and hospital length of stay (LOS) and ICU mortality. Results: The overall rate of obesity was 35%. Patients with obesity (BMI≥30kg/m2) were more likely to be female (p Conclusion: Obesity was neither associated with increased pulmonary complications or mortality in critically ill Black patients. However patients with class II and III obesity require longer ICU and hospital stay.

Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10⁻⁹. We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10⁻⁴⁰) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10⁻⁴). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure., National Heart, Lung, and Blood Institute (HHSN268201100005C), National Heart, Lung, and Blood Institute (HHSN268201100006C), National Heart, Lung, and Blood Institute (HHSN268201100007C), National Heart, Lung, and Blood Institute (HHSN268201100008C), National Heart, Lung, and Blood Institute (HHSN268201100009C), National Heart, Lung, and Blood Institute (HHSN268201100010C), National Heart, Lung, and Blood Institute (HHSN268201100011C), National Heart, Lung, and Blood Institute (HHSN268201100012C), National Heart, Lung, and Blood Institute (N01-HC-55015), National Heart, Lung, and Blood Institute (N01-HC-55016), National Heart, Lung, and Blood Institute (N01-HC-55018), National Heart, Lung, and Blood Institute (N01-HC-55019), National Heart, Lung, and Blood Institute (N01-HC-55020), National Heart, Lung, and Blood Institute (N01-HC-55021), National Heart, Lung, and Blood Institute (N01-HC-55022), National Heart, Lung, and Blood Institute (R01HL087641), National Heart, Lung, and Blood Institute (R01HL59367), National Heart, Lung, and Blood Institute (R01HL086694), National Human Genome Research Institute (U.S.) (U01HG004402), United States. National Institutes of Health (HHSN268200625226C), United States. National Institutes of Health (UL1RR025005), National Heart, Lung, and Blood Institute (HHSN268201200036C), National Heart, Lung, and Blood Institute (N01HC55222), National Heart, Lung, and Blood Institute (HHSN268200800007C), National Heart, Lung, and Blood Institute (N01HC85079), National Heart, Lung, and Blood Institute (N01HC85080), National Heart, Lung, and Blood Institute (N01HC85081), National Heart, Lung, and Blood Institute (N01HC85082), National Heart, Lung, and Blood Institute (N01HC85083), National Heart, Lung, and Blood Institute (N01HC85086), National Heart, Lung, and Blood Institute (U01HL080295), National Science Foundation (U.S.) (R01HL087652), National Heart, Lung, and Blood Institute (R01HL105756), National Heart, Lung, and Blood Institute (R01HL103612), National Heart, Lung, and Blood Institute (R01HL120393), National Institute on Aging (R01AG023629), National Center for Advancing Translational Sciences (U.S.) (UL1TR000124), National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (DK063491), National Heart, Lung, and Blood Institute (N01-HC-25195), National Heart, Lung, and Blood Institute (2K24HL04334), National Heart, Lung, and Blood Institute (R01HL077477), National Heart, Lung, and Blood Institute (R01HL093328), National Heart, Lung, and Blood Institute (NIH R01HL105993), National Institute on Aging (N01AG62101), National Heart, Lung, and Blood Institute (N01AG62103), National Heart, Lung, and Blood Institute (N01AG62106), National Institute on Aging (1R01AG032098-01A1), United States. National Institutes of Health (HHSN268200782096C), National Cancer Institute (U.S.) (CA-34944), National Cancer Institute (U.S.) (CA-40360), National Cancer Institute (U.S.) (CA-097193), National Heart, Lung, and Blood Institute (HL-26490), National Heart, Lung, and Blood Institute (HL-34595)

Purpose: New onset supraventricular arrhythmias (SVA) are commonly reported in mixed intensive care settings. We sought to determine the incidence, risk factors and outcomes of new onset SVA in African American (AA) patients with severe sepsis admitted to medical intensive care unit (MICU).Methods: Patients admitted to MICU between January 2012 through December 2012 were studied. Patients with a previous history of arrhythmia or with new onset of ventricular arrhythmia were excluded. Data on risk factors, critical care interventions and outcomes were obtained.Results: One hundred and thirty-one patients were identified. New onset SVA occurred in 34 (26%) patients. Of those 34, 20 (59%) had atrial fibrillation (AF), 6 (18%) had atrial flutter and 8 (24%) had other forms of SVA. Compared with patients without SVA, patients with new onset SVA were older (69 ± 12 yrs vs 59 ± 13 yrs, P=.003), had congestive heart failure (47% vs 24%, P=.015) and dyslipidemia (41% vs 15%, P=.002). Additionally, they had a higher mean mortality prediction model (MPM II) score (65 ± 25 vs 49 ± 26, P=.001) and an increased incidence of respiratory failure (85% vs 55%, P=.001). Hospital mortality in patients with new onset SVA was 18 (53%) vs 30 (31%); P=.024; however, in a multivariate analysis, new onset SVA was associated with nonsignificantly increased odds (OR 2.58, 95% CI 0.86-8.05) for in-hospital mortality.Conclusion: New onset SVA was prevalent in AA patients with severe sepsis and occurred more frequently with advanced age, increased severity of illness, congestive heart failure, and acute respiratory failure; it was associated with higher unadjusted in hospital mortality. However, after multiple adjustments, new onset SVA did not remain an independent predictor of mortality. Ethn Dis.2016;26(2):205-212; doi:10.18865/ ed.26.2.205

Background Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. Methods In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. Results In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. Conclusions We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years. Electronic supplementary material The online version of this article (doi:10.1186/s12874-016-0248-6) contains supplementary material, which is available to authorized users.

Clustered data, multiple observations collected on the same experimental unit, is common in epidemiological studies. Bivariate outcome data is often the result of interest in two correlated response variables. An efficient method is presented for dealing with bivariate outcomes when one outcome is continuous and the other is a count using a simple transformation to handle over-dispersed Poisson data. A multilevel analysis was performed on data from the National Health Interview Survey (NHIS) with body mass index (BMI) and the number of cigarettes smoked per day (NCS) as responses. Results show that these random effects models yield misleading results in cases where the data is not transformed.

Objective Multiple studies have identified single-nucleotide polymorphisms (SNPs) that are associated with coronary heart disease (CHD). We examined whether SNPs selected based on predefined criteria will improve CHD risk prediction when added to traditional risk factors (TRFs). Methods SNPs were selected from the literature based on association with CHD, lack of association with a known CHD risk factor, and successful replication. A genetic risk score (GRS) was constructed based on these SNPs. Cox proportional hazards model was used to calculate CHD risk based on the Atherosclerosis Risk in Communities (ARIC) and Framingham CHD risk scores with and without the GRS. Results The GRS was associated with risk for CHD (hazard ratio [HR] = 1.10; 95% confidence interval [CI]: 1.07–1.13). Addition of the GRS to the ARIC risk score significantly improved discrimination, reclassification, and calibration beyond that afforded by TRFs alone in non-Hispanic whites in the ARIC study. The area under the receiver operating characteristic curve (AUC) increased from 0.742 to 0.749 ( Δ = 0.007; 95% CI, 0.004–0.013), and the net reclassification index (NRI) was 6.3%. Although the risk estimates for CHD in the Framingham Offspring (HR = 1.12; 95% CI: 1.10–1.14) and Rotterdam (HR = 1.08; 95% CI: 1.02–1.14) Studies were significantly improved by adding the GRS to TRFs, improvements in AUC and NRI were modest. Conclusion Addition of a GRS based on direct associations with CHD to TRFs significantly improved discrimination and reclassification in white participants of the ARIC Study, with no significant improvement in the Rotterdam and Framingham Offspring Studies.

Introduction: Cancer affects people of all races in the U.S.; however, the burden is greater for minority populations. This is influenced by factors such as demographics, behavioral factors, and access to medical services. The disparity is well documented in some solid tumors. However, little information exists on a racial disparity in survival among minority populations diagnosed with chronic myeloid leukemia (CML) especially in the era of targeted therapies. A better understanding of the population impact of CML will drive further research into approaches to improve overall outcomes. Methods: All cases of CML diagnosed between 1973 and 2017 and with available follow-up data reported in the Surveillance, Epidemiology, and End Results database were reviewed. We performed a population-based study of CML to evaluate survival by race and calendar year of diagnosis: 1975-1989 (the era of cytotoxic therapy; busulfan and hydroxyurea), 1990-2000 (the era of Allogeneic hematopoietic stem cell transplantation or interferon-alfa ± cytarabine), and 2001-2015 (the era of targeted therapy; Tyrosine Kinase Inhibitors). Results: A total of 14572 (56.4% were females) patients diagnosed with CML were included in our analysis. The racial distribution was 83.4% white, 10% black, 0.8% American Indian/Alaska Native and 5.9% Asian or Pacific Islander. 5314 (36.5%), 3725 (25.5%) and 5544 (38%) cases of CML were diagnosed during the 1975-1989, 1990-2000 and 2001-2015 eras respectively. The median 5-year survival improved by race with each era, with the greatest improvement observed among patients diagnosed during the 2001-2015 era. Although a trend of improvement in the median 5-year survival is seen across all age categories, patients 65 years and older continue to experience only a modest survival benefit in the era of targeted therapies (Table 1). No significant differences in survival by race was observed in the era of targeted therapies (Figure 1). Conclusions: This survival analysis includes a longer follow-up duration (Diagnosis Years: 1973-2015) for patients with CML compared to prior studies. Although an overall improvement in median survival is seen across all age groups and by each era, survival benefits are lagging in certain populations (black and American Indian/ Alaska Native) older than 65. SEER database captures information from metropolitan counties in the United States and this may not truly reflect the health of the entire US population. Disclosures No relevant conflicts of interest to declare.