Browsing by Author "Cheng, Feixiong"

Now showing 1 - 7 of 7
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    Alcohol use disorder is associated with higher risks of Alzheimer's and Parkinson's diseases: A study of US insurance claims data
    (Wiley, 2022-11-21) Zhang, Pengyue; Edenberg, Howard J.; Nurnberger, John; Lai, Dongbing; Cheng, Feixiong; Liu, Yunlong; Biostatistics, School of Public Health
    Introduction: Alcohol use disorder (AUD) is on the ascendancy in the US older adult population. The association between AUD and adverse brain outcomes remains inconclusive. Method: In a retrospective cohort design using US insurance claim data (2007-2020), 129,182 individuals with AUD were matched with 129,182 controls by age, sex, race, and clinical characteristics. We investigated the association between AUD and adverse brain outcomes using Cox analysis, Kaplan-Meier analysis, and log-rank test. Results: After adjusting for covariates, AUD was associated with a higher risk of Alzheimer's disease (female adjusted hazard ratio [HR] = 1.78, 95% confidence interval [CI]: 1.68-1.90, p < 0.001; male adjusted HR = 1.80, 95% CI: 1.71-1.91, p < 0.001) and a higher risk of Parkinson's disease (female adjusted HR = 1.49, 95% CI: 1.32-1.68, p < 0.001; male adjusted HR = 1.42, 95% CI: 1.32-1.52, p < 0.001) in the overall sample. In separate analyses of Black, White, and Hispanic individuals, those with AUD had higher risk of Alzheimer's disease (adjusted HRs ≥1.58; Ps ≤ 0.001). A significantly elevated risk for Parkinson's disease was found only in the White subpopulation (female adjusted HR = 1.55, 95% CI: 1.36-1.77, p < 0.001; male adjusted HR = 1.45, 95% CI: 1.33-1.57, p < 0.001). Conclusions: AUD is associated with Alzheimer's disease. AUD is associated with Parkinson's disease in White people. Cognitive screening and neurological examination among older adults with AUD hold the promise for early detection of Alzheimer's disease and Parkinson's disease. Highlights: Alcohol use disorder is associated with Alzheimer's disease and dementia. Alcohol use disorder is associated with Parkinson's disease in White people.
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    Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease
    (BMC, 2022-01-10) Fang, Jiansong; Zhang, Pengyue; Wang, Quan; Chiang, Chien‑Wei; Zhou, Yadi; Hou, Yuan; Xu, Jielin; Chen, Rui; Zhang, Bin; Lewis, Stephen J.; Leverenz, James B.; Pieper, Andrew A.; Li, Bingshan; Li, Lang; Cummings, Jeffrey; Cheng, Feixiong; Biostatistics and Health Data Science, School of Medicine
    Background: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. Results: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. Conclusions: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.
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    The Concurrent Initiation of Medications Is Associated with Discontinuation of Buprenorphine Treatment for Opioid Use Disorder
    (medRxiv, 2020) Zhang, Pengyue; Chiang, Chien-Wei; Quinney, Sara; Donneyong, Macarius; Lu, Bo; Huang, Lei Frank; Cheng, Feixiong; Obstetrics and Gynecology, School of Medicine
    Introduction Retention in buprenorphine treatment for opioid use disorder (OUD) yields better opioid abstinence and reduces all-cause mortality for patients with OUD. Despite significant efforts have been made to expand the availability and use of buprenorphine in the United States, its retention rates remain on a low level. The current study examines discontinuation of buprenorphine with respect to concurrent initiation of other medications using real-world evidence. Methods Case-crossover study was conducted to examine discontinuation of buprenorphine using a large-scale longitudinal health dataset including 148,306 commercially-insured individuals initiated on medications for opioid use disorder (MOUD). Odds ratios and Bonferroni adjusted p-values were calculated for medications and therapeutic classes of medications. Results Clonidine was associated with increased discontinuation risk of buprenorphine both using the buprenorphine dataset alone (OR = 1.583 and adjusted p-value = 1.22 × 10−6) and using naltrexone as a comparison drug (OR = 2.706 and adjusted p-value = 4.11 × 10−5). Opioid medications (oxycodone, morphine and fentanyl) and methocarbamol were associated with increased discontinuation risk of buprenorphine using the buprenorphine dataset alone (adjusted p-value < 0.05), but not significant using naltrexone as a comparison drug. 6 drug therapeutic classes were associated with increased discontinuation risk of buprenorphine both using the buprenorphine dataset alone and using naltrexone as a comparison drug (adjusted p-value < 0.05). Conclusion Concurrent initiation of medications is associated with increased discontinuation risk of buprenorphine. Opioid medications are prescribed among patients on MOUD and associated with increased discontinuation risk of buprenorphine. Analgesics is associated with increased discontinuation risk of buprenorphine for patients without previous exposure of pain medications.
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    Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease
    (Springer Nature, 2021) Fang, Jiansong; Zhang, Pengyue; Zhou, Yadi; Chiang, Chien-Wei; Tan, Juan; Hou, Yuan; Stauffer, Shaun; Li, Lang; Pieper, Andrew A.; Cummings, Jeffrey; Cheng, Feixiong; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    We developed an endophenotype disease module-based methodology for Alzheimer's disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on retrospective case-control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25-0.39, P<1.0×10-8). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho-tau expression in AD patient-induced pluripotent stem cells-derived neuron models, supporting mechanistically its potential beneficial effect in Alzheimer's disease. The association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.
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    Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease
    (Cold Spring Harbor Laboratory, 2021) Xu, Jielin; Zhang, Pengyue; Huang, Yin; Zhou, Yadi; Hou, Yuan; Bekris, Lynn M.; Lathia, Justin; Chiang, Chien-Wei; Li, Lang; Pieper, Andrew A.; Leverenz, James B.; Cummings, Jeffrey; Cheng, Feixiong; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.
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    Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment
    (BMC, 2021-06) Zhou, Yadi; Xu, Jielin; Hou, Yuan; Leverenz, James B.; Kallianpur, Asha; Mehra, Reena; Liu, Yunlong; Yu, Haiyuan; Pieper, Andrew A.; Jehi, Lara; Cheng, Feixiong; Medical and Molecular Genetics, School of Medicine
    Background Dementia-like cognitive impairment is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of causative processes by which COVID-19 may lead to cognitive impairment is essential for developing preventive and therapeutic interventions. Methods In this study, we conducted a network-based, multimodal omics comparison of COVID-19 and neurologic complications. We constructed the SARS-CoV-2 virus-host interactome from protein-protein interaction assay and CRISPR-Cas9-based genetic assay results and compared network-based relationships therein with those of known neurological manifestations using network proximity measures. We also investigated the transcriptomic profiles (including single-cell/nuclei RNA-sequencing) of Alzheimer’s disease (AD) marker genes from patients infected with COVID-19, as well as the prevalence of SARS-CoV-2 entry factors in the brains of AD patients not infected with SARS-CoV-2. Results We found significant network-based relationships between COVID-19 and neuroinflammation and brain microvascular injury pathways and processes which are implicated in AD. We also detected aberrant expression of AD biomarkers in the cerebrospinal fluid and blood of patients with COVID-19. While transcriptomic analyses showed relatively low expression of SARS-CoV-2 entry factors in human brain, neuroinflammatory changes were pronounced. In addition, single-nucleus transcriptomic analyses showed that expression of SARS-CoV-2 host factors (BSG and FURIN) and antiviral defense genes (LY6E, IFITM2, IFITM3, and IFNAR1) was elevated in brain endothelial cells of AD patients and healthy controls relative to neurons and other cell types, suggesting a possible role for brain microvascular injury in COVID-19-mediated cognitive impairment. Overall, individuals with the AD risk allele APOE E4/E4 displayed reduced expression of antiviral defense genes compared to APOE E3/E3 individuals. Conclusion Our results suggest significant mechanistic overlap between AD and COVID-19, centered on neuroinflammation and microvascular injury. These results help improve our understanding of COVID-19-associated neurological manifestations and provide guidance for future development of preventive or treatment interventions, although causal relationship and mechanistic pathways between COVID-19 and AD need future investigations.
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    NHLBI-CMREF Workshop Report on Pulmonary Vascular Disease Classification: JACC State-of-the-Art Review
    (Elsevier, 2021) Oldham, William M.; Hemnes, Anna R.; Aldred, Micheala A.; Barnard, John; Brittain, Evan L.; Chan, Stephen Y.; Cheng, Feixiong; Cho, Michael H.; Desai, Ankit A.; Garcia, Joe G.N.; Geraci, Mark W.; Ghiassian, Susan D.; Hall, Kathryn T.; Horn, Evelyn M.; Jain, Mohit; Kelly, Rachel S.; Leopold, Jane A.; Lindstrom, Sara; Modena, Brian D.; Nichols, William C.; Rhodes, Christopher J.; Sun, Wei; Sweatt, Andrew J.; Vanderpool, Rebecca R.; Wilkins, Martin R.; Wilmot, Beth; Zamanian, Roham T.; Fessel, Joshua P.; Aggarwal, Neil R.; Loscalzo, Joseph; Xiao, Lei; Medicine, School of Medicine
    The National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes. The committee suggested support for interdisciplinary research teams to develop and validate analytic methods, a national effort to coordinate biosamples and data, a consortium of preclinical investigators to expedite target evaluation and drug development, longitudinal assessment of molecular biomarkers in clinical trials, and a task force to develop a master clinical trials protocol for pulmonary vascular disease.