Faculty Bibliography

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

BACKGROUND:Greater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms. OBJECTIVES:Using untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study. METHODS:We calculated 4 PDIs based on participants' responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model. RESULTS:We identified 82 significant PDI-metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026-0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006). CONCLUSIONS:In a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.

We assessed mortality risks associated with source-specific fine particles (PM_2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM_2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM_2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m³ increase) across five identified sources. On a 1 μg/m³ basis, the residual oil-related PM_2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM_2.5 mass, suggesting that past estimates using the generic PM_2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

BACKGROUND/OBJECTIVES/OBJECTIVE:Little is known about trajectories of recovery 12-months after hospitalization for severe COVID. METHODS:We conducted a prospective, longitudinal cohort study of patients with and without neurological complications during index hospitalization for COVID-19 from March 10, 2020-May 20, 2020. Phone follow-up batteries were performed at 6- and 12-months post-COVID symptom onset. The primary 12-month outcome was the modified Rankin Scale (mRS) comparing patients with or without neurological complications using multivariable ordinal analysis. Secondary outcomes included: activities of daily living (Barthel Index), telephone Montreal Cognitive Assessment (t-MoCA) and Neuro-QoL batteries for anxiety, depression, fatigue and sleep. Changes in outcome scores from 6 to 12-months were compared using non-parametric paired-samples sign test. RESULTS:Twelve-month follow-up was completed in N=242 patients (median age 65, 64% male, 34% intubated during hospitalization) and N=174 completed both 6- and 12-month follow-up. At 12-months 197/227 (87%) had ≥1 abnormal metric: mRS>0 (75%), Barthel<100 (64%), t-MoCA≤18 (50%), high anxiety (7%), depression (4%), fatigue (9%) and poor sleep (10%). 12-month mRS scores did not differ significantly among those with (N=113) or without (N=129) neurological complications during hospitalization after adjusting for age, sex, race, pre-COVID mRS and intubation status (adjusted OR 1.4, 95% CI0.8-2.5), though those with neurological complications had higher fatigue scores (T-score 47 vs 44, P=0.037). Significant improvements in outcome trajectories from 6- to 12-months were observed in t-MoCA scores (56% improved, median difference 1 point, P=0.002), and Neuro-QoL anxiety scores (45% improved, P=0.003). Non-significant improvements occurred in fatigue, sleep and depression scores in 48%, 48% and 38% of patients, respectively. Barthel and mRS scores remained unchanged between 6 and 12-months in >50% of patients. DISCUSSION/CONCLUSIONS:At 12-months post-hospitalization for severe COVID, 87% of patients had ongoing abnormalities in functional, cognitive or Neuro-QoL metrics and abnormal cognition persisted in 50% of patients without a prior history of dementia/cognitive abnormality. Only fatigue severity differed significantly between patients with or without neurological complications during index hospitalization. However, significant improvements in cognitive (t-MoCA) and anxiety (Neuro-QoL) scores occurred in 56% and 45% of patients, respectively, between 6- to 12-months. These results may not be generalizable to those with mild/moderate COVID.

BACKGROUND:More than 80% of adult patients diagnosed with cancer survive long term. Long-term complications of cancer and its therapies may increase the risk of cardiovascular disease (CVD), but prospective studies using adjudicated cancer and CVD events are lacking. OBJECTIVES:The aim of this study was to assess the risk of CVD in cancer survivors in a prospective community-based study. METHODS:We included 12,414 ARIC (Atherosclerosis Risk In Communities) study participants. Cancer diagnoses were ascertained via linkage with state registries supplemented with medical records. Incident CVD outcomes were coronary heart disease (CHD), heart failure (HF), stroke, and a composite of these. We used multivariable Poisson and Cox regressions to estimate the association of cancer with incident CVD. RESULTS:Mean age was 54 years, 55% were female, and 25% were Black. A total of 3,250 participants (25%) had incident cancer over a median 13.6 years of follow-up. Age-adjusted incidence rates of CVD (per 1,000 person-years) were 23.1 (95% CI: 24.7-29.1) for cancer survivors and 12.0 (95% CI: 11.5-12.4) for subjects without cancer. After adjustment for cardiovascular risk factors, cancer survivors had significantly higher risks of CVD (HR: 1.37; 95% CI: 1.26-1.50), HF (HR: 1.52; 95% CI: 1.38-1.68), and stroke (HR: 1.22; 95% CI: 1.03-1.44), but not CHD (HR: 1.11; 95% CI: 0.97-1.28). Breast, lung, colorectal, and hematologic/lymphatic cancers, but not prostate cancer, were significantly associated with CVD risk. CONCLUSIONS:Compared with persons without cancer, adult cancer survivors have significantly higher risk of CVD, especially HF, independent of traditional cardiovascular risk factors. There is an unmet need to define strategies for CVD prevention in this high-risk population.

Despite considerable progress, smoking remains the leading preventable cause of death in the United States. To address the considerable health and economic burden of tobacco use, the development of improved tobacco control and treatment interventions is critical. By combining elements of economics and psychology, behavioral economics provides a framework for novel solutions to treat smokers who have failed to quit with traditional smoking cessation interventions. The full range of behavioral economic principles, however, have not been widely utilized in the realm of tobacco control and treatment. Given the need for improved tobacco control and treatment, the limited use of other behavioral economic principles represents a substantial missed opportunity. For this reason, we sought to describe the principles of behavioral economics as they relate to tobacco control, highlight potential gaps in the behavioral economics tobacco research literature, and provide examples of potential interventions that use each principle.

Prediabetes affects at least 1 in 3 adults in the U.S. and 1 in 5 in Europe. Although guidelines advocate aggressive management of lipid parameters in diabetes, most guidelines do not address treatment of dyslipidemia in prediabetes despite the increased atherosclerotic cardiovascular disease (ASCVD) risk. Several criteria are used to diagnose prediabetes: impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and HbA1c of 5.7-6.4%. Individuals with prediabetes have a greater risk of diabetes, a higher prevalence of dyslipidemia with a more atherogenic lipid profile and an increased risk of ASCVD. In addition to calculating ASCVD risk using traditional methods, an OGTT may further stratify risk. Those with 1-hour plasma glucose ≥8.6 mmol/L (155 mg/dL) and/or 2-hour ≥7.8 mmol/L (140 mg/dL) (IGT) have a greater risk of ASCVD. Diet and lifestyle modification are fundamental in prediabetes. Statins, ezetimibe and PCSK9 inhibitors are recommended in people requiring pharmacotherapy. Although high-intensity statins may increase risk of diabetes, this is acceptable because of the greater reduction of ASCVD. The LDL-C goal in prediabetes should be individualized. In those with IGT and/or elevated 1-hour plasma glucose, the same intensive approach to dyslipidemia as recommended for diabetes should be considered, particularly if other ASCVD risk factors are present.

The destruction of the World Trade Center (WTC) on September 11, 2001 (9/11) released large amounts of toxic dusts and fumes into the air that exposed many community members who lived and/or worked in the local area. Many community members, defined as WTC survivors by the federal government, developed lower respiratory symptoms (LRS). We previously reported the persistence of these symptoms in patients with normal spirometry despite treatment with inhaled corticosteroids and/or long-acting bronchodilators. This report expands upon our study of this group with the goal to identify molecular markers associated with exposure and heterogeneity in WTC survivors with LRS using a selected plasma biomarker approach. Samples from WTC survivors with LRS (n = 73, WTCS) and samples from healthy control participants of the NYU Bellevue Asthma Registry (NYUBAR, n = 55) were compared. WTCS provided information regarding WTC dust exposure intensity. Hierarchical clustering of the linear biomarker data identified two clusters within WTCS and two clusters within NYUBAR controls. Comparison of the WTCS clusters showed that one cluster had significantly increased levels of circulating matrix metalloproteinases (MMP1, 2, 3, 8, 12, 13), soluble inflammatory receptors (receptor for advanced glycation end-products-RAGE, Interleukin-1 receptor antagonist (IL-1RA), suppression of tumorigenicity (ST)2, triggering receptor expressed on myeloid cells (TREM)1, IL-6Ra, tumor necrosis factor (TNF)RI, TNFRII), and chemokines (IL-8, CC chemokine ligand- CCL17). Furthermore, this WTCS cluster was associated with WTC exposure variables, ash at work, and the participant category workers; but not with the exposure variable WTC dust cloud at 9/11. A comparison of WTC exposure categorial variables identified that chemokines (CCL17, CCL11), circulating receptors (RAGE, TREM1), MMPs (MMP3, MMP12), and vascular markers (Angiogenin, vascular cell adhesion molecule-VCAM1) significantly increased in the more exposed groups. Circulating biomarkers of remodeling and inflammation identified clusters within WTCS and were associated with WTC exposure.