Gail D'Eramo Melkus

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.

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.

Abstract

BACKGROUND: Cognitive impairment is prevalent in heart failure and is associated with higher mortality rates. The mechanism behind cognitive impairment in heart failure with preserved ejection fraction (HFpEF) has not been established. OBJECTIVE: The aim of this study was to evaluate associations between abnormal cardiac hemodynamics and cognitive impairment in individuals with HFpEF. METHODS: A secondary analysis of Atherosclerosis Risk in Communities (Atherosclerosis Risk in Communities) study data was performed. Participants free of stroke or dementia who completed in-person assessments at visit 5 were included. Neurocognitive test scores among participants with HFpEF, heart failure with reduced ejection fraction (HFrEF), and no heart failure were compared. Sociodemographics, comorbid illnesses, medications, and echocardiographic measures of cardiac function that demonstrated significant (P < .10) bivariate associations with neurocognitive test scores were included in multivariate models to identify predictors of neurocognitive test scores among those with HFpEF. Multiple imputation by chained equations was used to account for missing values. RESULTS: Scores on tests of attention, language, executive function, and global cognitive function were worse among individuals with HFpEF than those with no heart failure. Neurocognitive test scores were not significantly different among participants with HFpEF and HFrEF. Worse diastolic function was weakly associated with worse performance in memory, attention, and language. Higher cardiac index was associated with worse performance on 1 test of attention. CONCLUSIONS: Cognitive impairment is prevalent in HFpEF and affects several cognitive domains. The current study supports the importance of cognitive screening in patients with heart failure. An association between abnormal cardiac hemodynamics and cognitive impairment was observed, but other factors are likely involved.

Abstract

Effective antiretroviral therapy (ART) adherence strategies for HIV+ adolescents and young adults (AYA) are needed to prevent HIV-related morbidity, mortality, and onward transmission. In the Adherence Connection for Counseling, Education, and Support (ACCESS) pilot, an exploratory sequential mixed-methods design was used to develop and test a peer-led, mobile health (mHealth) cognitive behavioral ART adherence intervention. HIV+ AYA (ages 16–29 years) with unsuppressed plasma HIV RNA (HIV viral load) were eligible for this five-session intervention directed to improving ART adherence and HIV viral load. A total of 78 peer-led remote videoconferencing sessions (via WebEx) were delivered to 16 participants. High completion rates (97.5%) and client satisfaction scores (mean = 29.13 of 32; SD = 2.45) were observed. Self-reported ART adherence improved (32% increase in doses taken; 95th CI 11.2–53.3) with an annualized average rate of 47.5% (0.28 log10) reduction in HIV viral load. We established proof of concept for the ACCESS peer-led, mHealth cognitive behavioral ART adherence intervention, with promising adherence and virologic outcome data.

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.

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.

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.

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.

Abstract

Background: Self-care is essential to cardiovascular disease (CVD) health outcomes, but may be challenging for older working adults. Objective: Describe self-care and the relationship of work-related characteristics to self-care among older workers with CVD. Methods: Convergent mixed methods design (n = 108) assessed self-care, organization of work, job-level and clinical factors; qualitative data (n = 40) explored self-care and working. Data integrated in the final analytic phase. Results: Sixty-eight percent reported adequate self-care maintenance (SC-CHDI maintenance ≥70); only 22% had adequate self-care management (SC-CHDI management ≥ 70). Controlling for physical capacity, work-related factors explained 22% variance in self-care maintenance; physical capacity was only significant determinant of self-care management. Individuals with poor self-care described low job control, job stress and work-life imbalance that interfered with routine self-care. Individuals with poor self-care management reported “feeling stressed out” and “extreme fatigue” attributed to their job. Conclusions: Interventions targeting self-care, stress management and work-life balance among older workers with CVD are needed.