Faculty Bibliography

BACKGROUND:Growth differentiation factor 15 (GDF-15) is a stress-responsive biomarker associated with several types of cardiovascular diseases. However, conflicting results have been reported regarding its association with incident atrial fibrillation (AF) in the general population. METHODS:In 10 234 White and Black Atherosclerosis Risk in Communities (ARIC) Study participants (mean age 60 years, 20.5% Blacks) free of AF at baseline (1993 to 1995), we quantified the association of GDF-15 with incident AF using Cox regression models. GDF-15 concentration was measured by an aptamer-based proteomic method. AF was defined as AF diagnosis by electrocardiogram at subsequent ARIC visits or AF diagnosis in hospitalization records or death certificates. Harrell's c-statistic and categorical net reclassification improvement were computed for risk discrimination and reclassification. RESULTS:There were 2217 cases of incident AF over a median follow-up of 20.6 years (incidence rate 12.3 cases/1000 person-years). After adjusting for potential confounders, GDF-15 was independently associated with incident AF, with a hazard ratio (HR) of 1.42 (95% CI, 1.24-1.62) for the top vs bottom quartile. The result remained consistent (HR 1.23 [95% CI, 1.07-1.41]) even after further adjusting for 2 cardiac biomarkers, cardiac troponin T and natriuretic peptide. The results were largely consistent across demographic subgroups. The addition of GDF-15 modestly improved the c-statistic by 0.003 (95% CI, 0.001-0.006) beyond known risk factors of AF. CONCLUSIONS:In this community-based biracial cohort, higher concentrations of GDF-15 were independently associated with incident AF, supporting its potential value as a clinical marker of AF risk.

BACKGROUND:Genome-wide association studies have identified six genetic variants associated with severe COVID-19, yet the mechanisms through which they may affect disease remains unclear. We investigated proteomic signatures related to COVID-19 risk variants rs657152 (ABO), rs10735079 (OAS1/OAS2/OAS3), rs2109069 (DPP9), rs74956615 (TYK2), rs2236757 (IFNAR2) and rs11385942 (SLC6A20/LZTFL1/CCR9/FYCO1/CXCR6/XCR1) as well as their corresponding downstream pathways that may promote severe COVID-19 in risk allele carriers and their potential relevancies to other infection outcomes. METHODS:A DNA aptamer-based array measured 4870 plasma proteins among 11 471 participants. Linear regression estimated associations between the COVID-19 risk variants and proteins with correction for multiple comparisons, and canonical pathway analysis was conducted. Cox regression assessed associations between proteins identified in the main analysis and risk of incident hospitalized respiratory infections (2570 events) over a 20.7-year follow-up. RESULTS:The ABO variant rs657152 was associated with 84 proteins in 7241 white participants with 24 replicated in 1671 Black participants. The TYK2 variant rs74956615 was associated with ICAM-1 and -5 in white participants with ICAM-5 replicated in Black participants. Of the 84 proteins identified in the main analysis, seven were significantly associated with incident hospitalized respiratory infections including Ephrin type-A receptor 4 (hazard ratio (HR): 0.87; P = 2.3 × 10-11) and von Willebrand factor type A (HR: 1.17; P = 1.6x10-13). CONCLUSIONS:Novel proteomics signatures and pathways for COVID-19-related risk variants TYK2 and ABO were identified. A subset of these proteins predicted greater risk of incident hospitalized pneumonia and respiratory infections. Further studies to examine these proteins in COVID-19 patients are warranted.

Introduction The COVID-19 pandemic is forcing changes to clinical practice within traditional addiction treatment programmes, including the increased use of telehealth, reduced restrictions on methadone administration (eg, increased availability of take-home doses and decreased requirements for in-person visits), reduced reliance on group counselling and less urine drug screening. This paper describes the protocol for a mixed-methods study analysing organisational-level factors that are associated with changes in clinic-level practice changes and treatment retention. Methods and analysis We will employ an explanatory sequential mixed-methods design to study the treatment practices for opioid use disorder (OUD) patients in New York State (NYS). For the quantitative aim, we will use the Client Data System and Medicaid claims data to examine the variation in clinical practices (ie, changes in telehealth, pharmacotherapy, group vs individual counselling and urine drug screening) and retention in treatment for OUD patients across 580 outpatient clinics in NYS during the pandemic. Clinics will be categorised into quartiles based on composite rankings by calculating cross-clinic Z scores for the clinical practice change and treatment retention variables. We will apply the random-effects modelling to estimate change by clinic by introducing a fixed-effect variable for each clinic, adjusting for key individual and geographic characteristics and estimate the changes in the clinical practice changes and treatment retention. We will then employ qualitative methods and interview 200 key informants (ie, programme director, clinical supervisor, counsellor and medical director) to develop an understanding of the quantitative findings by examining organisational characteristics of programmes (n=25) representative of those that rank in the top quartile of clinical practice measures as well as programmes that performed worst on these measures (n=25). Ethics and dissemination The study has been approved by the Institutional Review Board of NYU Langone Health (#i21-00573). Study findings will be disseminated through national and international conferences, reports and peer-reviewed publications.

Stroke severity is the most important predictor of post-stroke outcome. Most longitudinal cohort studies do not include direct and validated measures of stroke severity, yet these indicators may provide valuable information about post-stroke outcomes, as well as risk factor associations. In the Atherosclerosis Risk in Communities (ARIC) study, stroke severity data were retrospectively collected, and this paper outlines the procedures used and shares them as a model for assessment of stroke severity in other large epidemiologic studies. Trained physician abstractors, who were blinded to other clinical events, reviewed hospital charts of all definite/probable stroke events occurring in ARIC. In this analysis we included 1,198 ischemic stroke events occurring from ARIC baseline (1987-1989) through December 31, 2009. Stroke severity was categorized according to the National Institutes of Health Stroke Scale (NIHSS) score and classified into 5 levels: NIHSS ≤ 5 (minor), NIHSS 6-10 (mild), NIHSS 11-15 (moderate), NIHSS 16-20 (severe), and NIHSS > 20 (very severe). We assessed interrater reliability in a subgroup of 180 stroke events, reviewed independently by the lead abstraction physician and one of the four secondary physician abstractors. Interrater correlation coefficients for continuous NIHSS score as well as percentage of absolute agreement and Cohen Kappa Statistic for NIHSS categories were presented. Determination of stroke severity by the NIHSS, based on data abstracted from hospital charts, was possible for 97% of all ischemic stroke events. Median (25%-75%) NIHSS score was 5 (2-8). The distribution of NIHSS category was NIHSS ≤ 5 = 58.3%, NIHSS 6-10 = 24.5%, NIHSS 11-15 = 8.9%, NIHSS 16-20 = 4.7%, NIHSS > 20 = 3.6%. Overall agreement in the classification of severity by NIHSS category was present in 145/180 events (80.56%). Cohen's simple Kappa statistic (95% CI) was 0.64 (0.55-0.74) and weighted Kappa was 0.79 (0.72-0.86). Mean (SD) NIHSS score was 5.84 (5.88), with a median score of 4 and range 0-31 for the lead reviewer (rater 1) and mean (SD) 6.16 (6.10), median 4.5 and range 0-36 in the second independent assessment (rater 2). There was a very high correlation between the scores reported in both assessments (Pearson r = 0.90). Based on our findings, we conclude that hospital chart-based retrospective assessment of stroke severity using the NIHSS is feasible and reliable.

Patient health literacy is vital to clinical trial engagement. Knowledge and beliefs about clinical trials may contribute to patient literacy of clinical trials, influencing engagement, enrollment and retention. We developed and assessed a survey that measures clinical trial health knowledge and beliefs, known as the Clinical trial HEalth Knowledge and belief Scale (CHEKS). The 31 survey items in CHEKS represent knowledge and beliefs about clinical trial research (n = 409) in 2017. We examined item-scale correlations for the 31 items, eliminated items with item-scale correlations less than 0.30, and then estimated internal consistency reliability for the remaining 25 items. We used the comparative fit index (CFI) and the root mean squared error of approximation (RMSEA) to evaluate model fit. The average age of the sample was 34 (SD = 15.7) and 48% female. We identified 6 of the 31 items that had item-scale correlations (corrected for overlap) lower than 0.30. Coefficient alpha for the remaining 25 items was 0.93 A one-factor categorical confirmatory factor analytic model with 16 correlated errors was not statistically significant (chi-square = 10011.994, df = 300, p < 0.001) but fit the data well (CFI = 0.95 and RMSEA = 0.07). CHEKS can assess clinical trial knowledge and beliefs.

Increasing reports of long-term symptoms following COVID-19 infection, even among mild cases, necessitate systematic investigation into the prevalence and type of lasting illness. Notably, there is limited data regarding the influence of social determinants of health, like perceived discrimination and economic stress, that may exacerbate COVID-19 health risks. Here, 1,584 recovered COVID-19 patients that experienced mild to severe forms of disease provided detailed medical and psychosocial information. Path analyses examined hypothesized associations between discrimination, illness severity, and lasting symptoms. Secondary analyses evaluated sex differences, timing of infection, and impact of prior mental health problems. Post hoc logistic regressions tested social determinants hypothesized to predict neurological, cognitive, or mood symptoms. 70.6% of patients reported presence of one or more lasting symptom after recovery. 19.4% and 25.1% of patients reported lasting mood or cognitive/memory problems. Perceived discrimination predicted increased illness severity and increased lasting symptom count, even when adjusting for sociodemographic factors and mental/physical health comorbidities. This effect was specific to stress related to discrimination, not to general stress levels. Further, patient perceptions regarding quality of medical care influenced these relationships. Finally, illness early in the pandemic is associated with more severe illness and more frequent lasting complaints. Lasting symptoms after recovery from COVID-19 are highly prevalent and neural systems are significantly impacted. Importantly, psychosocial factors (perceived discrimination and perceived SES) can exacerbate individual health risk. This study provides actionable directions for improved health outcomes by establishing that sociodemographic risk and medical care influence near and long-ranging health outcomes. All data from this study have been made publicly available.

This series of six articles (five original articles and one review) is presented by international leaders in health disparities research [...].

Fetal exposure to environmental chemicals has been associated with adverse health outcomes in children and later into adulthood. While several studies have examined correlations and variability of non-persistent chemical exposures throughout pregnancy, many do not capture more recent exposures, particularly in New York City. Our goal was to characterize exposure to phthalates, bisphenols, polycyclic aromatic hydrocarbons, and organophosphate pesticides among pregnant women residing in New York City who enrolled in the New York University Children's Health and Environment Study (NYU CHES) between 2016 and 2018. We measured urinary chemical metabolite concentrations in 671 women at early, mid, and late pregnancy (median 10.8, 20.8, and 29.3 weeks, respectively). We calculated Spearman correlation coefficients among chemical concentrations at each measurement time point. We compared changes in population-level urinary metabolites at each stage using paired Wilcoxon signed-rank tests and calculated intraclass correlation coefficients (ICCs) to quantify intra-individual variability of metabolites across pregnancy. Geometric means and ICCs were compared to nine other pregnancy cohorts that recruited women in 2011 or later as well as nationally reported levels from women of child-bearing age. Compared with existing cohorts, women in NYU CHES had higher geometric means of organophosphate pesticides (Σdiethylphosphates = 28.7 nmol/g cr, Σdimethylphosphates = 57.3 nmol/g cr, Σdialkyl phosphates = 95.9 nmol/g cr), bisphenol S (0.56 μg/g cr), and 2-naphthalene (8.98 μg/g cr). Five PAH metabolites and two phthalate metabolites increased between early to mid and early to late pregnancy at the population level. Spearman correlation coefficients for chemical metabolites were generally below 0.50. Intra-individual exposures varied over time, as indicated by low ICCs (0.22-0.88, median = 0.38). However, these ICCs were often higher than those observed in other pregnancy cohorts. These results provide a general overview of the chemical metabolites measured in NYU CHES in comparison to other contemporary pregnancy cohorts and highlight directions for future studies.