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PDF file - 96KB, Supplementary Figure S4. Wnt/beta-catenin signaling proteins in LRP6 KD cells. Western blot for expression of Wnt/beta-catenin signaling proteins in adherent 2LMP shRNA LRP6 knockdown cells compared to shRNA control cells.

PDF file - 59KB, Supplementary Figure S5. In vivo toxicity study. Athymic nude mice were treated with niclosamide (20 mg/kg), TRA-8 (200 ng) or the combination for two weeks. Mice were weighed twice a week for two weeks.

Basal-like breast cancers (BLBC) are poorly differentiated and display aggressive clinical behavior. These tumors become resistant to cytotoxic agents, and tumor relapse has been attributed to the presence of cancer stem cells (CSC). One of the pathways involved in CSC regulation is the Wnt/β-catenin signaling pathway. LRP6, a Wnt ligand receptor, is one of the critical elements of this pathway and could potentially be an excellent therapeutic target. Niclosamide has been shown to inhibit the Wnt/β-catenin signaling pathway by causing degradation of LRP6. TRA-8, a monoclonal antibody specific to TRAIL death receptor 5, is cytotoxic to BLBC cell lines and their CSC-enriched populations. The goal of this study was to examine whether niclosamide is cytotoxic to BLBCs, specifically the CSC population, and if in combination with TRA-8 could produce increased cytotoxicity. Aldehyde dehydrogenase (ALDH) is a known marker of CSCs. By testing BLBC cells for ALDH expression by flow cytometry, we were able to isolate a nonadherent population of cells that have high ALDH expression. Niclosamide showed cytotoxicity against these nonadherent ALDH-expressing cells in addition to adherent cells from four BLBC cell lines: 2LMP, SUM159, HCC1187, and HCC1143. Niclosamide treatment produced reduced levels of LRP6 and β-catenin, which is a downstream Wnt/β-catenin signaling protein. The combination of TRA-8 and niclosamide produced additive cytotoxicity and a reduction in Wnt/β-catenin activity. Niclosamide in combination with TRA-8 suppressed growth of 2LMP orthotopic tumor xenografts. These results suggest that niclosamide or congeners of this agent may be useful for the treatment of BLBC. Mol Cancer Ther; 13(4); 800–11. ©2014 AACR.

PDF file - 128KB, Supplementary Figure S1. Sensitivity of MF10A mammary epithelial cells to niclosamide and TRA-8 mediated cytotoxicity. (A) Attached MCF10A cell line was treated for 48 hours with niclosamide (0.08, 0.1, 0.25 and 0.5 microM) and analyzed for cell viability using ATPlite assay.

PDF file - 89KB, Supplementary Table S1. Sensitivity of BLBC cell lines to niclosamide mediated cytotoxicity. SUM159, HCC1187, HCC1143 and 2LMP BLBC NAAE and adherent cell lines were treated with niclosamide for 48 hours and analyzed for viability using the ATPlite assay. Individual experiments were assayed in quadruplicate and values represent the mean and SD of three independent experiments. SUM159 P < 0.05, HCC1187 P < 0.05, HCC1143 P < 0.0005, 2LMP P > 0.05.

PDF file - 72KB, Supplementary Table S2. Immunohistochemistry of mouse tumors treated for two weeks with vehicle control, niclosamide (20 mg/kg), TRA-8 (200 micro-g) or the combination of both agents. Tumors were stained and analyzed (H-score) for p-STAT3, cytosolic beta-catenin, nuclear beta-catenin and cleaved caspase 3. The values represent an average of three tumors per group (plus-minus SE).

PDF file - 209KB, Supplementary Figure S2. Niclosamide effect on Wnt/beta-catenin signaling. Activity of TCF/LEF plasmid reporter in the TOPflash assay was evaluated on adherent 2LMP, SUM159, HCC143 and HCC1187 cells. Cell lines were treated with 1 microM niclosamide for 24 hours (SUM159, HCC1143 and HCC1187) or with 0.25 microM niclosamide (2LMP). Each cell line was also treated with Wnt3A ligand (5 ng/mL) alone and in combination with niclosamide. The experiment was performed in triplicate. The bars represent means plus-minus SE. Treatment was compared to control or Wnt3A. Niclosamide vs. control (*P < 0.05), niclosamide vs. control (**P < 0.01), niclosamide vs. Wnt3A (#P < 0.05), niclosamide + Wnt3A vs. Wnt3A (##P < 0.01).

PDF file - 99KB, Supplementary Figure S3. ATPlite viability of cells treated with niclosamide. 2LMP, SUM159, HCC1143 and HCC1187 adherent (A) or NAAE (B) cells were treated with niclosamide for 24 hours and analyzed for viability using ATPlite assay. Individual experiments were assayed in triplicate and values represent the mean plus-minus SE.

Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Chronic kidney disease (CKD) is a risk factor for cardiovascular disease and early death. Genetic factors contribute to CKD, and recently, polygenic scores (PGS) have been developed to quantify risk for complex diseases, such as CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PGS development overall; moreover, European-ancestry derived PGSs demonstrate diminished predictive performance in African ancestry populations. This study aimed to develop a PGS for CKD using genotype and phenotype data from African American (AA) participants of observational cohort studies. We obtained score weights from a meta-analysis of genome-wide association studies (GWAS) for estimated glomerular filtration rate (eGFR) in the Million Veteran Program (MVP) and Reasons for Geographical and Racial Differences in Stroke (REGARDS) Study (total n~66,000). We then optimized the PGS in a cohort of AAs from the Hypertension Genetic Epidemiology Network (HyperGEN) Study (n~1,900) using the PRS-CS software and evaluated the predictive performance of the PGS at multiple global shrinkage parameters. We further adjusted the PGS for APOL1 risk status. In HyperGEN, the eGFR-based PGS was significantly associated with the odds of prevalent CKD—defined as baseline eGFR 2 — in logistic regression models adjusted for age, sex, and population structure. Further, accounting for APOL1 risk status—a putative variant for CKD unique to those of sub-Saharan African descent—improved the score’s accuracy, with the APOL1 -adjusted PGS explaining 1.9% (1.1% without APOL1 ) of the variance in CKD and an area under the curve (AUC) of 58.9% [95% CI: 53.0%-64.9%] (without APOL1 , 58.2% [95% CI: 52.3%-64.1%]). Sensitivity analyses and validation in external cohorts, as well as comparisons to previously published PGS, are ongoing. In this study, we developed a PGS that was significantly associated with CKD with improved predictive accuracy in AAs over previously published PGS.

BackgroundHeterogeneity in the long-term metabolic response to dietary macronutrient composition can be partially explained by genetic factors. However, few studies have demonstrated reproducible gene-diet interactions (GDIs), likely due in part to measurement error in dietary intake estimation as well as insufficient capture of rare genetic variation. Discovery analyses in ancestry-diverse cohorts that include rare genetic variants from whole-genome sequencing (WGS) could help identify genetic variants modifying the effects of dietary macronutrient composition on glycemic phenotypes.ObjectiveWe aimed to identify macronutrient GDIs across the genetic frequency spectrum associated with continuous glycemic traits in genetically and culturally diverse cohorts.MethodsWe analyzed N=33,187 diabetes-free participants from 10 cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program with WGS, self-reported diet, and glycemic traits (fasting glucose [FG], insulin [FI], and hemoglobin A1c [HbA1c]). We fit multivariable-adjusted linear mixed models for the main effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and for its interactions with genetic variants genome-wide. Tests were performed for both common variants and gene-based rare variant sets in each cohort followed by a combined cohort meta-analysis.ResultsIn main effect models, participants consuming more calories from carbohydrate at the expense of fat had modestly lower glycemic trait values (β per 250 kcal substitution for FG: −0.030 mmol/L, p=2.7×10−6; lnFI: −0.008 log(pmol/L), p=0.17; HbA1c: −0.013 %, p=0.025). In GDI analyses, a common African ancestry-enriched variant (rs79762542; 78 kb upstream of the FRAS1 gene) reached study-wide significance (p = 1.14×10−8) indicating a higher HbA1c with greater proportion of calories from carbohydrate vs. fat among minor allele carriers only. This interaction was replicated in the UK Biobank cohort. Simulations revealed that there is (1) a substantial impact of measurement error on statistical power for GDI discovery at these sample sizes, especially for rare genetic variants, and (2) over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error.ConclusionsOur analysis identified a potential genetic interaction modifying the dietary macronutrient-HbA1c association while highlighting the importance of precise exposure measurement and significantly increased sample size to identify additional similar effects.

How race, ethnicity, and ancestry are used in genomic research has wide-ranging implications for how research is translated into clinical care and incorporated into public understanding. Correlation between race and genetic ancestry contributes to unresolved complexity for the scientific community, as illustrated by heterogeneous definitions and applications of these variables. Here, we offer commentary and recommendations on the use of race, ethnicity, and ancestry across the arc of genetic research, including data harmonization, analysis, and reporting. While informed by our experiences as researchers affiliated with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, these recommendations are applicable to basic and translational genomic research in diverse populations with genome-wide data. Moving forward, considerable collaborative effort will be required to ensure that race, ethnicity, and ancestry are described and used appropriately to generate scientific knowledge that yields broad and equitable benefit.

Heart failure (HF) is a multifactorial syndrome that remains a leading cause of worldwide morbidity. Despite its high prevalence, only half of patients with HF respond to guideline-directed medical management, prompting therapeutic efforts to confront the molecular underpinnings of its heterogeneity. In the current study, we examined epigenetics as a yet unexplored source of heterogeneity among patients with end-stage HF. Specifically, a multicohort-based study was designed to quantify cardiac genome-wide cytosine-p-guanine (CpG) methylation of cardiac biopsies from male patients undergoing left ventricular assist device (LVAD) implantation. In both pilot (n = 11) and testing (n = 31) cohorts, unsupervised multidimensional scaling of genome-wide myocardial DNA methylation exhibited a bimodal distribution of CpG methylation found largely to occur in the promoter regions of metabolic genes. Among the available patient attributes, only categorical self-identified patient race could delineate this methylation signature, with African American (AA) and Caucasian American (CA) samples clustering separately. Because race is a social construct, and thus a poor proxy of human physiology, extensive review of medical records was conducted, but ultimately failed to identify covariates of race at the time of LVAD surgery. By contrast, retrospective analysis exposed a higher all-cause mortality among AA (56.3%) relative to CA (16.7%) patients at 2 yr following LVAD placement (P = 0.03). Geocoding-based approximation of patient demographics uncovered disparities in income levels among AA relative to CA patients. Although additional studies are needed, the current analysis implicates cardiac DNA methylation as a previously unrecognized indicator of socioeconomic disparity in human heart failure outcomes. NEW & NOTEWORTHY A bimodal signature of cardiac DNA methylation in heart failure corresponds with racial differences in all-cause mortality following mechanical circulatory support. Racial differences in promoter methylation disproportionately affect metabolic signaling pathways. Socioeconomic factors are associated with racial differences in the cardiac methylome among men with end-stage heart failure. Listen to this article’s corresponding podcast at https://ajpheart.podbean.com/e/racial-socioeconomic-determinants-of-the-cardiac-epigenome/.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

BACKGROUND: Epigenome-wide association studies identified the cg00574958 DNA methylation site at the carnitine palmitoyltransferase-1A (CPT1A) gene to be associated with reduced risk of metabolic diseases (hypertriglyceridemia, obesity, type 2 diabetes, hypertension, metabolic syndrome), but the mechanism underlying these associations is unknown. OBJECTIVES: We aimed to elucidate whether carbohydrate and fat intakes modulate cg00574958 methylation and the risk of metabolic diseases. METHODS: We examined associations between carbohydrate (CHO) and fat (FAT) intake, as percentages of total diet energy, and the CHO/FAT ratio with CPT1A-cg00574958, and the risk of metabolic diseases in 3 populations (Genetics of Lipid Lowering Drugs and Diet Network, n = 978; Framingham Heart Study, n = 2331; and REgistre GIroní del COR study, n = 645) while adjusting for confounding factors. To understand possible causal effects of dietary intake on the risk of metabolic diseases, we performed meta-analysis, CPT1A transcription analysis, and mediation analysis with CHO and FAT intakes as exposures and cg00574958 methylation as the mediator. RESULTS: We confirmed strong associations of cg00574958 methylation with metabolic phenotypes (BMI, triglyceride, glucose) and diseases in all 3 populations. Our results showed that CHO intake and CHO/FAT ratio were positively associated with cg00574958 methylation, whereas FAT intake was negatively correlated with cg00574958 methylation. Meta-analysis further confirmed this strong correlation, with β = 58.4 ± 7.27, P = 8.98 x 10(-16) for CHO intake; β = −36.4 ± 5.95, P = 9.96 x 10(-10) for FAT intake; and β = 3.30 ± 0.49, P = 1.48 x 10(-11) for the CHO/FAT ratio. Furthermore, CPT1A mRNA expression was negatively associated with CHO intake, and positively associated with FAT intake, and metabolic phenotypes. Mediation analysis supports the hypothesis that CHO intake induces CPT1A methylation, hence reducing the risk of metabolic diseases, whereas FAT intake inhibits CPT1A methylation, thereby increasing the risk of metabolic diseases. CONCLUSIONS: Our results suggest that the proportion of total energy supplied by CHO and FAT can have a causal effect on the risk of metabolic diseases via the epigenetic status of CPT1A. Study registration at https://www.clinicaltrials.gov/: the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN)—NCT01023750; and the Framingham Heart Study (FHS)—NCT00005121.

Rationale: Cardiovascular disease remains the leading cause of death in women. To address its determinants including persisting cardiovascular risk factors amplified by sex and race inequities, novel personalized approaches are needed grounded in the engagement of participants in research and prevention. Objective: To report on a participant-centric and personalized dynamic registry designed to address persistent gaps in understanding and managing cardiovascular disease in women. Methods and Results: The American Heart Association and Verily launched the Research Goes Red registry (RGR) in 2019, as an online research platform available to consenting individuals over the age of 18 years in the United States. RGR aims to bring participants and researchers together to expand knowledge by collecting data and providing an open-source longitudinal dynamic registry for conducting research studies. As of July 2021, 15 350 individuals have engaged with RGR. Mean age of participants was 48.0 48.0±0.2 years with a majority identifying as female and either non-Hispanic White (75.7%) or Black (10.5%). In addition to 6 targeted health surveys, RGR has deployed 2 American Heart Association-sponsored prospective clinical studies based on participants’ areas of interest. The first study focuses on perimenopausal weight gain, developed in response to a health concerns survey. The second study is designed to test the use of social media campaigns to increase awareness and participation in cardiovascular disease research among underrepresented millennial women. Conclusions: RGR is a novel online participant-centric platform that has successfully engaged women and provided critical data on women’s heart health to guide research. Priorities for the growth of RGR are centered on increasing reach and diversity of participants, and engaging researchers to work within their communities to leverage the platform to address knowledge gaps and improve women’s health.

BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician’s diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV(1)/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10(−8) in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10(−6)). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV(1) (P = 3.3 × 10(−3)), postbronchodilator (PB) FEV(1) (7.3 × 10(−3)), and PB FEV(1)/FVC ratio (P = 2.7 × 10(−3)). The identified baseline FEV(1)/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rareLDLRintronic variants associated with markedly increased LDL-C, similar to rareLDLRexonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

To identify trends in racial and ethnic diversity of epidemiology graduate degree recipients in the United States between academic years 2008 and 2018.National-level data from the National Center for Education Statistics was analyzed to assess the change in proportions of epidemiology degrees conferred to each racial/ethnic group - American Indian or Alaska Native; Asian, Native Hawaiian or Other Pacific Islander; Black or African American; Hispanic or Latino; White; and two or more races- over two time periods, Fall 2007- Spring 2012 (Period 1) and Fall 2012 - Spring 2018 (Period 2).During Period 13,837 epidemiology graduate degrees were conferred, and 6960 in Period 2. Within race/ethnicity groups, there was a statistically significant increase in graduate epidemiology degrees awarded over the two time periods to students of Hispanic or Latino ethnicity, and to students reporting two or more races. The proportion of degrees awarded to non-White students in aggregate increased by 4.7 percentage points, from 33.5% to 38.2%, while awards to White students decreased by the same amount.Overall, the racial/ethnic diversity of epidemiology graduates in the United States increased between 2008 and 2018, however, further efforts are needed to increase awards within some racial minority subgroups.

AIMS/HYPOTHESIS: Ideal cardiovascular health (CVH) is associated with lower diabetes risk. However, it is unclear whether this association is similar across glycaemic levels (normal [

Five sequence variants in SLC16A11 (rs117767867, rs13342692, rs13342232, rs75418188, and rs75493593), which occur in two non-reference haplotypes, were recently shown to be associated with diabetes in Mexicans from the SIGMA consortium. We aimed to determine whether these previous findings would replicate in the HCHS/SOL Mexican origin group and whether genotypic effects were similar in other HCHS/SOL groups. We analyzed these five variants in 2492 diabetes cases and 5236 controls from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), which includes U.S. participants from six diverse background groups (Mainland groups: Mexican, Central American, and South American; and Caribbean groups: Puerto Rican, Cuban, and Dominican). We estimated the SNP-diabetes association in the six groups and in the combined sample. We found that the risk alleles occur in two non-reference haplotypes in HCHS/SOL, as in the SIGMA Mexicans. The haplotype frequencies were very similar between SIGMA Mexicans and the HCHS/SOL Mainland groups, but different in the Caribbean groups. The SLC16A11 sequence variants were significantly associated with risk for diabetes in the Mexican origin group (P = 0.025), replicating the SIGMA findings. However, these variants were not significantly associated with diabetes in a combined analysis of all groups, although the power to detect such effects was 85% (assuming homogeneity of effects among the groups). Additional analyses performed separately in each of the five non-Mexican origin groups were not significant. We also analyzed (1) exclusion of young controls and, (2) SNP by BMI interactions, but neither was significant in the HCHS/SOL data. The previously reported effects of SLC16A11 variants on diabetes in Mexican samples was replicated in a large Mexican-American sample, but these effects were not significant in five non-Mexican Hispanic/Latino groups sampled from U.S. populations. Lack of replication in the HCHS/SOL non-Mexicans, and in the entire HCHS/SOL sample combined may represent underlying genetic heterogeneity. These results indicate a need for future genetic research to consider heterogeneity of the Hispanic/Latino population in the assessment of disease risk, but add to the evidence suggesting SLC16A11 as a potential therapeutic target for type 2 diabetes.

Background: Heart failure (HF) mortality rates have been increasing since 2011. Individual-level education and occupation have been inversely associated with HF mortality among those with diabetes mellitus (DM) but not among those without DM. However, less is known about the association between neighborhood social and economic environment (NSEE) and HF risk and whether this association varies by DM status. Methods: This study included 21,244 Black and White adults age >=45 years at baseline (2003-07) from the REGARDS Study. NSEE quartiles were created using z-scores based on 6 census tract variables from year 2000 (% =126 mg/dL or random glucose >=200 mg/dL or use of diabetes medications. Cox proportional hazards regression was used to obtain hazard ratios (95% CI) with HF follow-up through 2016. Results: Mean age was 65 years, 54% were women, 61% were White and 18% had prevalent DM at baseline. During a median 10.1 years, 829 incident HF events occurred. Among adults with DM, neighborhood disadvantage was associated with an increased HF risk , but this association was not statistically significant (Table). Among adults without DM, the risk of HF was higher for participants living in any neighborhood that was not the most advantaged, and the magnitude of association was smiliar across NSEE quartiles. Conclusion: Adults living in disadvantaged neighborhoods had a higher risk of HF, particularly among those without DM. Addressing neighborhood social and economic conditions may be important for HF prevention.

The digital world in which we live is changing rapidly. The evolving media environment is having a direct impact on traditional forms of communication and knowledge translation in public health and epidemiology. Openly accessible digital media can be used to reach a broader and more diverse audience of trainees, scientists, and the lay public than can traditional forms of scientific communication. The new digital landscape for delivering content is vast, and new platforms are continuously being added. In this article, we focus on several, including Twitter and podcasting, and discuss their relevance to epidemiology and science communication. We highlight 3 key reasons why we think epidemiologists should be engaging with these mediums: 1) science communication, 2) career advancement, and 3) development of a community and public service. Other positive and negative consequences of engaging in these forms of new media are also discussed. The authors of this commentary are all engaged in social media and podcasting for scientific communication, and we reflect on our experiences with these mediums as tools to advance the field of epidemiology.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome and exome arrays, resulting in over 100 associated variants. We extended this work with a high-coverage whole genome sequencing (WGS) analysis from fifteen cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. More than 23,000 non-diabetic individuals from five self-reported race/ethnicities (African, Asian, European, Hispanic and Samoan) were included for each trait. We analyzed 60M variants in race/ethnicity-specific and pooled single variant and rare variant aggregate tests. Twenty-two variants across sixteen gene regions were found significantly associated with FG or FI, eight of which were rare (Minor Allele Frequency, MAFG6PC2locus we identified a distinct FG signal at rare variant rs2232326 (MAF=0.01) after conditioning on known common variants. Functional annotations show rs2232326 to be disruptive and likely damaging while being weakly transcribed in islets. A pair of FG-associated variants were identified near theSLC30A8locus. These variants, one of which was rare (MAF=0.001) and Asian race/ethnicity-specific, were shown to be in islet-specific active enhancer regions. Other associated regions include rare variants nearROBO1andPTPRT, and common variants nearMTNR1B, GCK, GCKR, FOXA2, APOB, TCF7L2, andADCY5. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions down to a minor allele count of 20, creating a foundation for future sequencing-based investigation of glycemic traits.

There has been great interest in genetic risk prediction using risk scores in recent years, however, the utility of scores developed in European populations and later applied to non-European populations has not been successful. The goal of this study was to create a methylation risk score (MRS) for metabolic syndrome (MetS), demonstrating the utility of MRS across race groups using cross-sectional data from the Hypertension Genetic Epidemiology Network (HyperGEN, N = 614 African Americans (AA)) and the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, N = 995 European Americans (EA)). To demonstrate this, we first selected cytosine-guanine dinucleotides (CpG) sites measured on Illumina Methyl450 arrays previously reported to be significantly associated with MetS and/or component conditions in more than one race/ethnic group (CPT1A cg00574958, PHOSPHO1 cg02650017, ABCG1 cg06500161, SREBF1 cg11024682, SOCS3 cg18181703, TXNIP cg19693031). Second, we calculated the parameter estimates for the 6 CpGs in the HyperGEN data (AA) and used the beta estimates as weights to construct a MRS in HyperGEN (AA), which was validated in GOLDN (EA). We performed association analyses using logistic mixed models to test the association between the MRS and MetS, adjusting for covariates. Results showed the MRS was significantly associated with MetS in both populations. In summary, a MRS for MetS was a strong predictor for the condition across two race groups, suggesting MRS may be useful to examine metabolic disease risk or related complications across race/ethnic groups.

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10−8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD., Drugs targeting cardiovascular disease (CVD) can have negative consequences for liver function. Here, the authors combine genome wide analyses on 69,479 individuals to identify loss-of-function variants with beneficial effects on CVD-related traits without negative impacts on liver function.

Type 2 diabetes is increasing in all ancestry groups1. Part of its genetic basis may reside among the rare (minor allele frequency 2. We analyzed high-coverage (mean depth 38.2x) whole genome sequencing from 9,639 individuals with T2D and 34,994 controls in the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program2 to show that rare, non-coding variants that are poorly captured by genotyping arrays or imputation panels contribute h2=53% (P=4.2×10−5) to the genetic component of risk in the largest (European) ancestry subset. We coupled sequence variation with islet epigenomic signatures3 to annotate and group rare variants with respect to gene expression4, chromatin state5 and three-dimensional chromatin architecture6, and show that pancreatic islet regulatory elements contribute to T2D genetic risk (h2=8%, P=2.4×10−3). We used islet annotation to create a non-coding framework for rare variant aggregation testing. This approach identified five loci containing rare alleles in islet regulatory elements that suggest novel biological mechanisms readily linked to hypotheses about variant-to-function. Large scale whole genome sequence analysis reveals the substantial contribution of rare, non-coding variation to the genetic architecture of T2D and highlights the value of tissue-specific regulatory annotation for variant-to-function discovery.

Metabolic Syndrome (MetS) is a cluster of conditions that can increase the risk of chronic diseases like, type 2 diabetes. Multiple reports show alterations in DNA methylation patterns are associated with the condition. Using cross-sectional data from Hypertension Genetic Epidemiology Network (HyperGEN) (N=614 African Americans (AA)) and Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) (N=995 European Americans (EA)), we created a risk score model for MetS. First, we selected specific cytosine-guanine dinucleotides (CpG) sites measured on Illumina Methyl450 arrays previously found to be significantly associated to MetS (CPT1A cg00574958, PHOSPHO1 cg02650017, ABCG1 cg06500161, SREBF1 cg11024682, SOCS3 cg18181703, TXNIP cg19693031). Using the HyperGEN parameter estimates for the single CpGs as weights, we calculated a methylation risk score in HyperGEN which was validated in GOLDN. We then conducted an association analysis using a generalized linear mixed model to test the association between the methylation risk score (MetS-score) and MetS, adjusting for age, sex, study site, 4 ancestry principal components, and family structure. In HyperGEN we performed a 100,000-permutation test to determine statistical significance of the score with MetS. Results showed the risk score was significantly associated with MetS in both populations (HyperGEN p=6.36x-10; GOLDN p=2x-16), with a 1 standard deviation increase in the score being associated with 2.25 (95% CI, 1.79 - 2.86) higher odds of having MetS in HyperGEN and a 2.45 (CI 95%, 2.02 - 3.00) higher odds of having MetS in GOLDN. Lastly, we compared the model fitness of the GOLDN MetS model with and without the MetS-score. AIC criterion suggested the model including the score was a better fit (no score = 1173.99; with score = 1112.61). In conclusion, we created a methylation risk score strongly associated with MetS which should be further investigated for its ability to predict MetS risk in both AAs and EAs. Disclosure B.A. Minniefield: None. M. Irvin: None. B. Hidalgo: None. H.K. Tiwari: None. A. Patki: None. N. Chaudhary: None. Funding National Heart, Lung, and Blood Institute (3R01HL123782-02S1)

Background: Diabetes (DM) is more prevalent among rural adults in the U.S. However, it is unclear if coronary heart disease (CHD) risk among adults with DM varies by urbanicity. Methods: This study included 3,578 of the REGARDS Study participants with DM at baseline (2003-07). Each participant’s address was categorized as urban, large rural, small rural or isolated small rural tract using Rural and Urban Commuting Areas categories. Incident CHD events through 12/31/2016 were adjudicated. Cox regression with robust standard errors accounting for geographic clustering was used to obtain hazard ratios and 95% CIs. Results: Mean age of participants was 64.8 years, 56% were women, and 60% were Black adults. At baseline, 82.5% lived in urban, 10.7% in large rural, 5.3% in small rural and 1.5% in isolated small rural tracts. During a median 9.4 years, 358 incident CHD events occurred. CHD incidence among those in isolated small rural tracts was doubled compared to urban tracts, while those in large or small rural tracts had similar rates as those in urban tracts (Table). Controlling for individual-level and community-level factors minimally attenuated the association. Conclusion: Differences in the environmental attributes of these communities may contribute to the increased risk of CHD in isolated rural areas. Disclosure G. Malla: None. D. Long: None. A. Cherrington: None. M. Safford: None. D.M. Cummings: Research Support; Self; Novo Nordisk Inc. T.P. McAlexander: None. S. De Silva: None. S. Judd: None. B. Hidalgo: None. E.B. Levitan: Advisory Panel; Self; Amgen. Consultant; Self; Novartis Pharmaceuticals Corporation. Research Support; Self; Amgen. A.P. Carson: Research Support; Self; Amgen. Funding National Institute of Neurological Disorders and Stroke; National Institutes of Health; Department of Health and Human Services (U01NS041588); Centers for Disease Control and Prevention (U01DP006302)