Bradley E. Aouizerat


Bradley E. Aouizerat headshot

Bradley E. Aouizerat


Professor, College of Dentistry

Bradley E. Aouizerat's additional information

BS, Microbiology/ Molecular Genetics - University of California at Los Angeles
PhD, Microbiology/ Molecular Genetics/lmmunology - University of California at Los Angeles
MAS, Master of Advance Science Research in Clinical - University of California at San Francisco

Oral-systemic health

American Heart Association
American Liver Foundation
American Pain Society
American Society for Human Genetics
International Association for the Study of Pain

Faculty Honors Awards

Excellence in Research Mentoring Faculty Teaching Award (2013)
Excellence in Research Mentoring Faculty Teaching Award (Nominee) (2012)
Excellence in Research Mentoring Faculty Teaching Award (Nominee) (2011)
Most Dedicated Mentor Award, PMCTR Fellowship Program (2009)
Early Career Investigator Award, Bayer Healthcare International (2006)
Multidisciplinary Clinical Research Scholar, Roadmap K12 (2006)
Early Career Faculty Award, Hellman Family (2005)
Faculty Mentorship Award Nominee (2005)
Young Investigator Award, National Hemophilia Foundation (2005)
National Liver Scholar Award, American Liver Foundation (2004)
Irvine H. Page Young Investigator Award (Finalist), American Heart Association (2004)
Faculty Mentorship Award Nominee (2004)
Sam and Rose Gilbert Fellowship, UCLA (1998)
Warsaw Fellowship (1998)


Co-occurrence of injection drug use and hepatitis C increases epigenetic age acceleration that contributes to all-cause mortality among people living with HIV

Liang, X., Justice, A. C., Marconi, V. C., Aouizerat, B. E., & Xu, K. (2023). Epigenetics, 18(1). 10.1080/15592294.2023.2212235
Co-occurrence of injection drug use (IDU) and hepatitis C virus infection (HCV) is common in people living with HIV (PLWH) and leads to significantly increased mortality. Epigenetic clocks derived from DNA methylation (DNAm) are associated with disease progression and all-cause mortality. In this study, we hypothesized that epigenetic age mediates the relationships between the co-occurrence of IDU and HCV with mortality risk among PLWH. We tested this hypothesis in the Veterans Aging Cohort Study (n = 927) by using four established epigenetic clocks of DNAm age (i.e., Horvath, Hannum, Pheno, Grim). Compared to individuals without IDU and HCV (IDU-HCV-), participants with IDU and HCV (IDU+HCV+) showed a 2.23-fold greater risk of mortality estimated using a Cox proportional hazards model (hazard ratio: 2.23; 95% confidence interval: 1.62–3.09; p = 1.09E–06). IDU+HCV+ was associated with a significantly increased epigenetic age acceleration (EAA) measured by 3 out of 4 epigenetic clocks, adjusting for demographic and clinical variables (Hannum: p = 8.90E–04, Pheno: p = 2.34E–03, Grim: p = 3.33E–11). Furthermore, we found that epigenetic age partially mediated the relationship between IDU+HCV+ and all-cause mortality, up to a 13.67% mediation proportion. Our results suggest that comorbid IDU with HCV increases EAA in PLWH that partially mediates the increased mortality risk.

Insights from Bacterial 16S rRNA Gene into Bacterial Genera and Predicted Metabolic Pathways Associated with Stool Consistency in Rectal Cancer Patients: A Proof of Concept

Gonzalez-Mercado, V. J., Lim, J., & Aouizerat, B. (2023). Biological Research for Nursing, 25(3), 491-500. 10.1177/10998004231159623
Purpose: To examine if gut microbial taxa abundances and predicted functional pathways correlate with Bristol Stool Form Scale (BSFS) classification at the end of neoadjuvant chemotherapy and radiation therapy (CRT) for rectal cancer. Methods: Rectal cancer patients (n = 39) provided stool samples for 16S rRNA gene sequencing. Stool consistency was evaluated using the BSFS. Gut microbiome data were analyzed using QIIME2. Correlation analysis were performed in R. Results: At the genus level, Staphylococcus positively correlates (Spearman’s rho = 0.26), while Anaerofustis, Roseburia, Peptostreptococcaceae unclassified, Ruminococcaceae UBA1819, Shuttleworthia, Ca. Soleaferrea, Anaerostignum, Oscillibacter, and Akkermansia negatively correlate with BSFS scores (Spearman’s rho −0.20 to −0.42). Predicted pathways, including mycothiol biosynthesis and sucrose degradation III (sucrose invertase), were positively correlated with BSFS (Spearman’s rho = 0.03–0.21). Conclusion: The data support that in rectal cancer patients, stool consistency is an important factor to include in microbiome studies. Loose/liquid stools may be linked to Staphylococcus abundance and to mycothiol biosynthesis and sucrose degradation pathways.

MicroRNA biomarkers target genes and pathways associated with type 2 diabetes

Kariuki, D., Aouizerat, B. E., Asam, K., Kanaya, A. M., Zhang, L., Florez, J. C., & Flowers, E. (2023). Diabetes Research and Clinical Practice, 203. 10.1016/j.diabres.2023.110868
Aims/Hypothesis: Our prior analysis of the Diabetes Prevention Program study identified a subset of five miRNAs that predict incident type 2 diabetes. The purpose of this study was to identify mRNAs and biological pathways targeted by these five miRNAs to elucidate potential mechanisms of risk and responses to the tested interventions. Methods: Using experimentally validated data from miRTarBase version 8.0 and R (2021), we identified mRNAs with strong evidence to be regulated by individual or combinations of the five predictor miRNAs. Overrepresentation of the mRNA targets was assessed in pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation database. Results: The five miRNAs targeted 167 pathways and 122 mRNAs. Nine of the pathways have known associations with type 2 diabetes: Insulin signaling, Insulin resistance, Diabetic cardiomyopathy, Type 2 diabetes, AGE-RAGE signaling in diabetic complications, HIF-1 signaling, TGF-beta signaling, PI3K/Akt signaling, and Adipocytokine signaling pathways. Vascular endothelial growth factor A (VEGFA) has prior genetic associations with risk for type 2 diabetes and was the most commonly targeted mRNA for this set of miRNAs. Conclusions/Interpretation: These findings show that miRNA predictors of incident type 2 diabetes target mRNAs and pathways known to underlie risk for type 2 diabetes. Future studies should evaluate miRNAs as potential therapeutic targets for preventing and treating type 2 diabetes.

MicroRNAs Associated With Incident Diabetes in the Diabetes Prevention Program

Flowers, E., Aouizerat, B. E., Kanaya, A. M., Florez, J. C., Gong, X., & Zhang, L. (2023). Journal of Clinical Endocrinology and Metabolism, 108(6), e306-e312. 10.1210/clinem/dgac714
CONTEXT: MicroRNAs (miRs) are short (ie, 18-26 nucleotide) regulatory elements of messenger RNA translation to amino acids. OBJECTIVE: The purpose of this study was to assess whether miRs are predictive of incident type 2 diabetes (T2D) in the Diabetes Prevention Program (DPP) trial. METHODS: This was a secondary analysis (n = 1000) of a subset of the DPP cohort that leveraged banked biospecimens to measure miRs. We used random survival forest and Lasso methods to identify the optimal miR predictors and the Cox proportional hazards to model time to T2D overall and within intervention arms. RESULTS: We identified 5 miRs (miR-144, miR-186, miR-203a, miR-205, miR-206) that constituted the optimal predictors of incident T2D after adjustment for covariates (hazard ratio [HR] 2.81, 95% CI 2.05, 3.87; P < .001). Predictive risk scores following cross-validation showed the HR for the highest quartile risk group compared with the lowest quartile risk group was 5.91 (95% CI 2.02, 17.3; P < .001). There was significant interaction between the intensive lifestyle (HR 3.60, 95% CI 2.50, 5.18; P < .001) and the metformin (HR 2.72; 95% CI 1.47, 5.00; P = .001) groups compared with placebo. Of the 5 miRs identified, 1 targets a gene with prior known associations with risk for T2D. CONCLUSION: We identified 5 miRs that are optimal predictors of incident T2D in the DPP cohort. Future directions include validation of this finding in an independent sample in order to determine whether this risk score may have potential clinical utility for risk stratification of individuals with prediabetes, and functional analysis of the potential genes and pathways targeted by the miRs that were included in the risk score.

Multi-Tiered Assessment of Gene Expression Provides Evidence for Mechanisms That Underlie Risk for Type 2 Diabetes

Asam, K., Lewis, K. A., Kober, K., Gong, X., Kanaya, A. M., Aouizerat, B. E., & Flowers, E. (2023). Diabetes, Metabolic Syndrome and Obesity, 16, 3445-3457. 10.2147/DMSO.S428572
Introduction: Integrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways. Methods: Participants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed >6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed. Results: No genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D. Discussion: We validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D.

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes

Chang, L., Fukuoka, Y., Aouizerat, B. E., Zhang, L., & Flowers, E. (2023). Biological Research for Nursing, 25(3), 393-403. 10.1177/10998004221147513
Background: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases. Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics. Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov–Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types. Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Chang, L., Fukuoka, Y., Aouizerat, B. E., Zhang, L., & Flowers, E. (2023). JMIR Diabetes, 8. 10.2196/44018
Background: Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. Objective: The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. Methods: The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. Results: Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). Conclusions: Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity.

Reply to: Genetic differentiation at probe SNPs leads to spurious results in meQTL discovery

Cheng, Y., Li, B., Zhang, X., Aouizerat, B. E., Zhao, H., & Xu, K. (2023, December 1). In Communications Biology (Vols. 6, Issue 1). 10.1038/s42003-023-05646-9

Review of databases for experimentally validated human microRNA-mRNA interactions

Kariuki, D., Asam, K., Aouizerat, B. E., Lewis, K. A., Florez, J. C., & Flowers, E. (2023). Database, 2023. 10.1093/database/baad014
MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL

Study Protocol Using Cohort Data and Latent Variable Modeling to Guide Sampling Women with Type 2 Diabetes and Depressive Symptoms

Perez, N. B., D’Eramo Melkus, G., Yu, G., Brown-Friday, J., Anastos, K., & Aouizerat, B. (2023). Nursing Research, 72(5), 409-415. 10.1097/NNR.0000000000000669
Background Depression affects one in three women with Type 2 diabetes, and this concurrence significantly increases the risks of diabetes complications, disability, and early mortality. Depression is underrecognized because of wide variation in presentation and the lack of diagnostic biomarkers. Converging evidence suggests inflammation is a shared biological pathway in diabetes and depression. Overlapping epigenetic associations and social determinants of diabetes and depression implicate inflammatory pathways as a common thread. Objectives This article describes the protocol and methods for a pilot study aimed to examine associations between depressive symptoms, inflammation, and social determinants of health among women with Type 2 diabetes. Methods This is an observational correlational study that leverages existing longitudinal data from the Women's Interagency HIV Study (WIHS), a multicenter cohort of HIV seropositive (66%) and HIV seronegative (33%) women, to inform purposive sampling of members from latent subgroups emergent from a prior retrospective cohort-wide analysis. Local active cohort participants from the Bronx study site are then selected for the study. The WIHS recently merged with the Multicenter Aids Cohort Study (MACS) to form the MACS/WIHS Combined Cohort Study. Latent subgroups represent distinct symptom trajectories resultant from a growth mixture model analysis of biannually collected depressive symptom data. Participants complete surveys (symptom and social determinants) and provide blood samples to analyze plasma levels and DNA methylation of genes that encode for inflammatory markers (CRP, IL-6, TNF-). Correlation and regression analysis will be used to estimate the effect sizes between depressive symptoms and inflammatory markers, clinical indices (body mass index, hemoglobin A1C, comorbidities), and social determinants of health. Results The study began in January 2022, and completed data collection is estimated by early 2023. We hypothesize that depressive symptom severity will associate with higher levels of inflammation, clinical indices (e.g., higher hemoglobin A1C), and exposure to specific social determinants of health (e.g., lower income, nutritional insecurity). Discussion Study findings will provide the basis for future studies aimed at improving outcomes for women with Type 2 diabetes by informing the development and testing of precision health strategies to address and prevent depression in populations most at risk.