Gail D'Eramo Melkus

Faculty

Gail D'Eramo Melkus headshot

Gail D'Eramo Melkus

Florence and William Downs Professor in Nursing Research

1 212 998 5356

Gail D'Eramo Melkus's additional information

Gail D’Eramo Melkus, EdD, ANP, FAAN, is the former vice dean for research and the Florence and William Downs Professor in Nursing Research at NYU Rory Meyers College of Nursing. Melkus’ sustained interest in eliminating health disparities among vulnerable populations earned her a reputation as a leader in the development and testing of culturally competent models of diabetes care. Her program of intervention research that focuses on physiological and behavioral outcomes of self-management interventions has served as an education and training ground for numerous multidisciplinary scientists. 

Melkus currently serves as co-PI and co-investigator or research mentor on numerous funded projects specific to biobehavioral interventions for prevention and management of chronic conditions and related co-morbidities, in mid-life and older adults, including national and international work. Melkus serves as sponsor of K-awards focused on health disparities among vulnerable populations (women with T2D and depression; elderly Blacks at-risk for depression, glycemic control and infection in oncology stem cell recipients, CHD in older adult workers, HIV in adolescents). She is PI for the NINR P20 Center for Precision Health in Diverse Populations.

Before joining the faculty at NYU Meyers, Melkus was the Independence Foundation Professor of Nursing at the Yale University School of Nursing, where, in collaboration with the Diabetes Research Center of Albert Einstein College of Medicine in NY, she developed and implemented the Diabetes Care Specialty for advanced practice nurses. 

In recognition of her mentorship, Melkus received the 1st Annual NYU CTSI Mentor Award in May 2011, and in 2015 was inducted into the STTI Nurse Researcher Hall of Fame.

Melkus earned her EdD from Columbia University, MS from Yale University, MS from Connecticut State University, and ASN/BS in Nursing from the University of Bridgeport.

MS - Yale University (2003)
EdD - Columbia University (1987)
MS - Connecticut State University (1978)
ASN/BS, Nursing - University of Bridgeport (1976)

Primary care
Non-communicable disease
Women's health
Immigrants
Adult health

American Academy of Nursing
American Diabetes Association
Council for the Advancement of Nursing Science
CT Nursing Association
Eastern Nursing Research Society
Society for Behavioral Medicine

Faculty Honors Awards

Eastern Nursing Research Award (2020)
International Nurse Researcher Hall of Fame, Sigma Theta Tau (2015)
STTI Nurse Researcher Hall of Fame Inductee (2015)
Fellow, New York Academy of Medicine (2014)
Affiliated Faculty Appointment, University of Georgia (2014)
Distinguished Alumni Award, University of Bridgeport (2014)
Faculty Scholar Appointment, Universita' Tor Vergata (2014)
Advisory Committee Member, Medicare Evidence Development & Coverage (2013)
1st annual Distinguished Mentor Award, NYU Clinical Translational Science Institute (2011)
Distinguished Scholar Award, New York University College of Nursing (2010)
Distinguished Nurse Researcher Award, New York State Nurse Foundation (2009)
Endowed Chair, New York University (2008)
Endowed Chair of the Independence Foundation, Yale University (2004)
Excellence in Nursing Research Award, Diamond Jubilee Virginia Henderson (2003)
Fellow, American Academy of Nursing (2003)

Publications

Addressing Challenges in Recruiting Diverse Populations for Research: Practical Experience from a P20 Center

Wright, F., Malone, S. K., Wong, A., Melkus, G. D., & Dickson, V. V. (2022). Nursing Research, 71(3), 218-226. 10.1097/NNR.0000000000000577
Abstract
Abstract
Background Improving the recruitment and retention of underrepresented groups in all research areas is essential for health equity. However, achieving and retaining diverse samples is challenging. Barriers to recruitment and retention of diverse participants include socioeconomic and cultural factors and practical challenges (e.g., time and travel commitments). Objectives The purpose of this article is to describe the successful recruitment and retention strategies used by two related studies within a P20 center funded by the National Institute of Nursing Research focused on precision health research in diverse populations with multiple chronic conditions, including metabolic syndrome. Methods To address the complexity, biodiversity, and effect of metabolic syndrome and multiple chronic conditions, we developed culturally appropriate, multipronged recruitment and retention strategies for a pilot intervention study and a longitudinal observational pilot study within our P20 center. The following are the underlying principles that guided the recruitment and retention strategies: (a) flexibility, (b) active listening and bidirectional conversations, and (c) innovative problem solving. Results The intervention study (Pilot 1) enrolled 49 participants. The longitudinal observational study (Pilot 2) enrolled 45 participants. Women and racial/ethnic minorities were significantly represented in both. In Pilot 1, most of the participants completed the intervention and all phases of data collection. In Pilot 2, most participants completed all phases of data collection and chose to provide biorepository specimens. Discussion We developed a recruitment and retention plan building on standard strategies for a general medical population. Our real-world experiences informed the adaption of these strategies to facilitate the participation of individuals who often do not participate in research - specifically, women and racial/ethnic populations. Our experience across two pilot studies suggests that recruiting diverse populations should build flexibility in the research plan at the outset.

Associations Between DNA Methylation Age Acceleration, Depressive Symptoms, and Cardiometabolic Traits in African American Mothers From the InterGEN Study

Perez, N. B., Vorderstrasse, A. A., Yu, G., Melkus, G. D., Wright, F., Ginsberg, S. D., Crusto, C. A., Sun, Y. V., & Taylor, J. Y. (2022). Epigenetics Insights, 15. 10.1177/25168657221109781
Abstract
Abstract
Background: African American women (AAW) have a high risk of both cardiometabolic (CM) illness and depressive symptoms. Depressive symptoms co-occur in individuals with CM illness at higher rates than the general population, and accelerated aging may explain this. In this secondary analysis, we examined associations between age acceleration; depressive symptoms; and CM traits (hypertension, diabetes mellitus [DM], and obesity) in a cohort of AAW. Methods: Genomic and clinical data from the InterGEN cohort (n = 227) were used. Age acceleration was based on the Horvath method of DNA methylation (DNAm) age estimation. Accordingly, DNAm age acceleration (DNAm AA) was defined as the residuals from a linear regression of DNAm age on chronological age. Spearman’s correlations, linear and logistic regression examined associations between DNAm AA, depressive symptoms, and CM traits. Results: DNAm AA did not associate with total depressive symptom scores. DNAm AA correlated with specific symptoms including self-disgust/self-hate (−0.13, 95% CI −0.26, −0.01); difficulty with making decisions (−0.15, 95% CI −0.28, −0.02); and worry over physical health (0.15, 95% CI 0.02, 0.28), but were not statistically significant after multiple comparison correction. DNAm AA associated with obesity (0.08, 95% CI 1.02, 1.16), hypertension (0.08, 95% CI 1.01, 1.17), and DM (0.20, 95% CI 1.09, 1.40), after adjustment for potential confounders. Conclusions: Associations between age acceleration and depressive symptoms may be highly nuanced and dependent on study design contexts. Factors other than age acceleration may explain the connection between depressive symptoms and CM traits. AAW with CM traits may be at increased risk of accelerated aging.

Cardiovascular Disease Prevention Education Using a Virtual Environment in Sexual-Minority Men of Color With HIV: Protocol for a Sequential, Mixed Method, Waitlist Randomized Controlled Trial

Ramos, S. R., Johnson, C., Melkus, G., Kershaw, T., Gwadz, M., Reynolds, H., & Vorderstrasse, A. (2022). JMIR Research Protocols, 11(5). 10.2196/38348
Abstract
Abstract
Background: It is estimated that 70% of all deaths each year in the United States are due to chronic conditions. Cardiovascular disease (CVD), a chronic condition, is the leading cause of death in ethnic and racial minority males. It has been identified as the second most common cause of death in persons with HIV. By the year 2030, it is estimated that 78% of persons with HIV will be diagnosed with CVD. Objective: We propose the first technology-based virtual environment intervention to address behavioral, modifiable risk factors associated with cardiovascular and metabolic comorbidities in sexual-minority men of color with HIV. Methods: This study will be guided using social cognitive theory and the Technology Acceptance Model. A sequential, mixed method, waitlist controlled randomized control feasibility trial will be conducted. Aim 1 is to qualitatively explore perceptions of cardiovascular risk in 15 participants. Aim 2 is to conduct a waitlist controlled comparison to test if a virtual environment is feasible and acceptable for CVD prevention, based on web-based, self-assessed, behavioral, and psychosocial outcomes in 80 sexual-minority men of color with HIV. Results: The study was approved by the New York University Institutional Review Board in 2019, University of Texas Health Science Center at Houston in 2020, and by the Yale University Institutional Review Board in February 2022. As of April 2022, aim 1 data collection is 87% completed. We expect to complete data collection for aim 1 by April 30, 2022. Recruitment for aim 2 will begin mid-May 2022. Conclusions: This study will be the first online virtual environment intervention for CVD prevention in sexual-minority men of color with HIV. We anticipate that the intervention will be beneficial for CVD prevention education and building peer social supports, resulting in change or modification over time in risk behaviors for CVD.

Factors Associated With Cognitive Impairment in Heart Failure With Preserved Ejection Fraction

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Feasibility and Acceptability of the Adherence Connection Counseling, Education, and Support (ACCESS) Proof of Concept: A Peer-Led, Mobile Health (mHealth) Cognitive Behavioral Antiretroviral Therapy (ART) Adherence Intervention for HIV-Infected (HIV+) Ad

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Identifying Patients with Hypoglycemia Using Natural Language Processing: Systematic Literature Review

Zheng, Y., Dickson, V. V., Blecker, S., Ng, J. M., Rice, B. C., Melkus, G. D., Shenkar, L., Mortejo, M. C. R., & Johnson, S. B. (2022). JMIR Diabetes, 7(2). 10.2196/34681
Abstract
Abstract
Background: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. Objective: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. Methods: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. Results: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. Conclusions: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.

Precision Health in Cardiovascular Conditions

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Changes in Gut Microbiome Associated With Co-Occurring Symptoms Development During Chemo-Radiation for Rectal Cancer: A Proof of Concept Study

González-Mercado, V. J., Henderson, W. A., Sarkar, A., Lim, J., Saligan, L. N., Berk, L., Dishaw, L., McMillan, S., Groer, M., Sepehri, F., & Melkus, G. D. (2021). Biological Research for Nursing, 23(1), 31-41. 10.1177/1099800420942830
Abstract
Abstract
Purpose: To examine a) whether there are significant differences in the severity of symptoms of fatigue, sleep disturbance, or depression between patients with rectal cancer who develop co-occurring symptoms and those with no symptoms before and at the end of chemotherapy and radiation therapy (CRT); b) differences in gut microbial diversity between those with co-occurring symptoms and those with no symptoms; and c) whether before-treatment diversity measurements and taxa abundances can predict co-occurrence of symptoms. Methods: Stool samples and symptom ratings were collected from 31 patients with rectal cancer prior to and at the end of (24–28 treatments) CRT. Descriptive statistics were computed and the Mann-Whitney U test was performed for symptoms. Gut microbiome data were analyzed using R’s vegan package software. Results: Participants with co-occurring symptoms reported greater severity of fatigue at the end of CRT than those with no symptoms. Bacteroides and Blautia2 abundances differed between participants with co-occurring symptoms and those with no symptoms. Our random forest classification (unsupervised learning algorithm) predicted participants who developed co-occurring symptoms with 74% accuracy, using specific phylum, family, and genera abundances as predictors. Conclusion: Our preliminary results point to an association between the gut microbiota and co-occurring symptoms in rectal cancer patients and serves as a first step in potential identification of a microbiota-based classifier.

Exploring the effects of genomic testing on fear of cancer recurrence among breast cancer survivors

Gormley, M., Knobf, M. T., Vorderstrasse, A., Aouizerat, B., Hammer, M., Fletcher, J., & D’Eramo Melkus, G. (2021). Psycho-Oncology, 30(8), 1322-1331. 10.1002/pon.5679
Abstract
Abstract
Objective: Fear of cancer recurrence (FCR) is the greatest unmet psychosocial need among breast cancer survivors (BCS). The Oncotype Dx® test predicts the 10-year risk of distant recurrence and benefit of adjuvant chemotherapy among women with early stage hormone receptor-positive breast cancer. Despite the test's clinical utility, psychosocial responses are poorly understood. Methods: A descriptive cross-sectional study was conducted to explore associations between Oncotype Dx® test results (Recurrence Score [RS]) and FCR, health-related quality of life (HRQOL), distress, anxiety, depression, illness representation and perceived risk. Bivariate analyses were used to examine the associations between variables followed by multiple linear regression to examine predictors of FCR. Results: Greater FCR was associated with higher distress, anxiety, depression, illness representation and poorer HRQOL. BCS's with a high Oncotype Dx® RS reported higher overall fear (p = 0.013) and greater perceived consequences of their cancer (p = 0.034) compared to BCS's with a low RS. Using multiple linear regression, anxiety ((Formula presented.) = 0.21, p = 0.016), greater emotional response (Formula presented.) = 0.45, p < 0.001) and perceived consequences ((Formula presented.) = 0.18, p = 0.039) of illness explained 58% of the variance (p < 0.001) in FCR. Conclusion: BCS's with higher risk of recurrence may experience higher FCR. However, for FCR, modifiable factors such as anxiety and illness representation (greater emotional response and perceived consequences of illness) may be more important than non-modifiable factors such as Oncotype Dx® test results and age. Further research is needed to develop personalized interventions to improve BCS's outcomes.

Habitual physical activity patterns in a nationally representative sample of U.S. adults

Malone, S. K., Patterson, F., Grunin, L., Melkus, G. D., Riegel, B., Punjabi, N., Yu, G., Urbanek, J., Crainiceanu, C., & Pack, A. (2021). Translational Behavioral Medicine, 11(2), 332-341. 10.1093/tbm/ibaa002
Abstract
Abstract
Physical inactivity is a leading determinant of noncommunicable diseases. Yet, many adults remain physically inactive. Physical activity guidelines do not account for the multidimensionality of physical activity, such as the type or variety of physical activity behaviors. This study identified patterns of physical activity across multiple dimensions (e.g., frequency, duration, and variety) using a nationally representative sample of adults. Sociodemographic characteristics, health behaviors, and clinical characteristics associated with each physical activity pattern were defined. Multivariate finite mixture modeling was used to identify patterns of physical activity among 2003-2004 and 2005-2006 adult National Health and Nutrition Examination Survey participants. Chi-square tests were used to identify sociodemographic differences within each physical activity cluster and test associations between the physical activity clusters with health behaviors and clinical characteristics. Five clusters of physical activity patterns were identified: (a) low frequency, short duration (n = 730, 13%); (b) low frequency, long duration (n = 392, 7%); (c) daily frequency, short duration (n = 3,011, 55%); (d) daily frequency, long duration (n = 373, 7%); and (e) high frequency, average duration (n = 964, 18%). Walking was the most common form of activity; highly active adults engaged in more varied types of activity. High-activity clusters were comprised of a greater proportion of younger, White, nonsmoking adult men reporting moderate alcohol use without mobility problems or chronic health conditions. Active females engaged in frequent short bouts of activity. Data-driven approaches are useful for identifying clusters of physical activity that encompass multiple dimensions of activity. These activity clusters vary across sociodemographic and clinical subgroups.