Publications

Abstract

Since the completion of the Human Genome Project, there have been pitched debates about its implications and the research it enables. One prominent thread of concern focuses on the role of post-genomic science on technically enabling and generating interest in genetic ancestry testing (GAT). Critical analyses of GAT have pointed to multiple issues, raising the alarm on consumers’ experiences with such technologies. This paper describes the results of a pilot study in which we tracked women’s experiences receiving their genetic ancestry results, and their understandings of, reactions to, and valuing of this information over time. Overwhelmingly, our participants reported a curious combination of anticipation and satisfaction yet no discernable impact on their sense of self or racial identity. We elaborate on the effects and non-effects of GAT for the women in our study, and how we make sense of their simultaneous experiences of ‘knowing something’ but not ‘feeling different.’.

Abstract

Background: In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report.Methods: A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy.Results: Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%.Conclusions: A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

Abstract

Background: The effects of tobacco smoking on epigenome-wide methylation signatures in white blood cells (WBCs) collected from persons living with HIV may have important implications for their immune-related outcomes, including frailty and mortality. The application of a machine learning approach to the analysis of CpG methylation in the epigenome enables the selection of phenotypically relevant features from high-dimensional data. Using this approach, we now report that a set of smoking-associated DNA-methylated CpGs predicts HIV prognosis and mortality in an HIV-positive veteran population. Results: We first identified 137 epigenome-wide significant CpGs for smoking in WBCs from 1137 HIV-positive individuals (p < 1.70E-07). To examine whether smoking-associated CpGs were predictive of HIV frailty and mortality, we applied ensemble-based machine learning to build a model in a training sample employing 408,583 CpGs. A set of 698 CpGs was selected and predictive of high HIV frailty in a testing sample [(area under curve (AUC) = 0.73, 95%CI 0.63~0.83)] and was replicated in an independent sample [(AUC = 0.78, 95%CI 0.73~0.83)]. We further found an association of a DNA methylation index constructed from the 698 CpGs that were associated with a 5-year survival rate [HR = 1.46; 95%CI 1.06~2.02, p = 0.02]. Interestingly, the 698 CpGs located on 445 genes were enriched on the integrin signaling pathway (p = 9.55E-05, false discovery rate = 0.036), which is responsible for the regulation of the cell cycle, differentiation, and adhesion. Conclusion: We demonstrated that smoking-associated DNA methylation features in white blood cells predict HIV infection-related clinical outcomes in a population living with HIV.

Abstract

Sexual minority (i.e., non-heterosexual) individuals experience poorer mental and physical health, accounted for in part by the additional burden of sexual minority stress occurring from being situated in a culture favoring heteronormativity. Informed by previous research, the purpose of this study was to identify the relationship between sexual minority stress and leukocyte gene expression related to inflammation, cancer, immune function, and cardiovascular function. Sexual minority men living with HIV who were on anti-retroviral medication, had viral load < 200 copies/mL, and had biologically confirmed, recent methamphetamine use completed minority stress measures and submitted blood samples for RNA sequencing on leukocytes. Differential gene expression and pathway analyses were conducted comparing those with clinically elevated minority stress (n = 18) and those who did not meet the clinical cutoff (n = 20), covarying reactive urine toxicology results for very recent stimulant use. In total, 90 differentially expressed genes and 138 gene set pathways evidencing 2-directional perturbation were observed at false discovery rate (FDR) < 0.10. Of these, 41 of the differentially expressed genes and 35 of the 2-directionally perturbed pathways were identified as functionally related to hypothesized mechanisms of inflammation, cancer, immune function, and cardiovascular function. The neuroactive-ligand receptor pathway (implicated in cancer development) was identified using signaling pathway impact analysis. Our results suggest several potential biological pathways for future work investigating the relationship between sexual minority stress and health.