Browsing by Author "Zhang, Fan"
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Item A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials(IOS Press, 2021-05-18) O'Bryant, Sid E.; Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; Rissman, Robert A.Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFɑ, FABP-3, and PPY) correctly classified treatment response with 100% accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.Item A proteomic signature for dementia with Lewy bodies(Elsevier Inc., 2019-03-15) O'Bryant, Sid E.; Ferman, Tanis J.; Zhang, Fan; Hall, James R.; Pedraza, Otto; Wszolek, Zbigniew K.; Como, Tori; Julovich, David A.; Mattevada, Sravan; Johnson, Leigh A.; Edwards, Melissa; Graff-Radford, Neill R.Introduction: We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease would distinguish patients with Lewy body disease from normal controls, and if it would distinguish dementia with Lewy bodies (DLB) from Parkinson's disease (PD). Methods: Stored plasma samples were obtained from 145 patients (DLB n = 57, PD without dementia n = 32, normal controls n = 56) enrolled from patients seen in the Behavioral Neurology or Movement Disorders clinics at the Mayo Clinic, Florida. Proteomic assays were conducted and analyzed as per our previously published protocols. Results: In the first step, the proteomic profile distinguished the DLB-PD group from controls with a diagnostic accuracy of 0.97, sensitivity of 0.91, and specificity of 0.86. In the second step, the proteomic profile distinguished the DLB from PD groups with a diagnostic accuracy of 0.92, sensitivity of 0.94, and specificity of 0.88. Discussion: These data provide evidence of the potential utility of a multitiered blood-based proteomic screening method for detecting DLB and distinguishing DLB from PD.Item Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing(Frontiers Media S.A., 2021-12-08) Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; O'Bryant, Sid E.Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer's disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.Item Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting(2023) Nyalakonda, Ramyashree; Petersen, Melissa; Zhang, Fan; Johnson, Leigh; Tolman, Alex; Gutierrez, Alejandra; O'Bryant, Sid; Mozdbar, SimaIntroduction Alzheimer’s Disease (AD) is the most prevalent form of dementia and a leading cause of death in the elderly. The detection of AD remains poor in primary care despite the advancement of neurodiagnostic procedures. There are no rapid and cost-effective tools available to primary care providers to conduct cognitive examinations to diagnose AD. The goal of this study is to determine the predictive ability of structural retinal biomarkers to identify cognitive impairment in a primary care setting. Methods Participants were recruited from Alzheimer’s Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging and fundus imaging was performed. Exclusion criteria included high intraocular pressure defines as greater than or equal to 30 mmHg in either eye, history of adverse effects with pupillary relation, known hypersensitivity to tropicamide or any ingredient in the formulation, active ocular infection or inflammation, history of angle closure glaucoma, or having undergone ocular surgery within the last 6 months. Cognitive diagnoses were assigned algorithmically and verified at consensus review by an expert in the field of dementia. Results Data were examined on a total of 91 participants (59 cognitively unimpaired, 32 cognitively impaired (26 mild cognitive impairment (MCI), 6 AD)). The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer (pRNFL), and the inferior quadrant of the macular retinal nerve fiber layer. While all four quadrants of the pRNFL are highly important biomarkers for identifying those with cognitive impairment, the inferior and superior quadrants displayed higher relative importance compared to the temporal and nasal quadrants. Conclusion This study was the first to examine the utility of retinal biomarkers in diagnosis cognitive impairment in a primary care setting with models reflecting how it could be employed as a screening tool in practice. The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD. In prior studies, preferential thinning of the inner retinal layers is found in AD compared to healthy controls. This study can help distinguish those with cognitive impairment from those cognitively unimpaired. The availability of such a biomarker could increase access to disease modifying treatments once available.Item Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting(IOS Press, 2023-02-02) Mozdbar, Sima; Petersen, Melissa E.; Zhang, Fan; Johnson, Leigh A.; Tolman, Alex; Nyalakonda, Ramyashree; Gutierrez, Alejandra; O'Bryant, Sid E.BACKGROUND: Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer's disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD. OBJECTIVE: This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting. METHODS: Participants were recruited from Alzheimer's Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed. RESULTS: Data were examined on n = 91 participants. The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer, and the inferior quadrant of the macular retinal nerve fiber layer. CONCLUSION: The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD.Item Comparison of support vector machine, random forest, extreme gradient boosting and lasso and elastic-net regularized generalized linear model for Alzheimer's Disease prediction(2021) Zhang, Fan; Petersen, Melissa; Johnson, Leigh; Hall, James; O'Bryant, SidPurpose: Machine learning based blood test shows promise in detecting Alzheimer's disease (AD) and pinpointing mechanisms underlying the process of neurodegeneration. Model selection plays a crucial role in building good machine learning models for AD prediction. Methods: The paper presents a comparison of four machine learning algorithms: support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost )and lasso and elastic-net regularized generalized linear model (GLMNET) for Alzheimer's disease prediction using blood test data from serum. First, we implemented 10 times repeated 5-fold cross-validation to split the data into training set and testing set randomly 50 times to select the best hyperparameters for each selected machine learning method. Then we selected the best learning model based on the performance metrics in the testing set. Results: Of all compared prediction results in the training set, RF and XGBoost achieved the highest negative predictive value (100%) followed by SVM with 99.40% and GLMNET with 94.45%. Of all compared prediction results in the testing set, SVM achieved the highest negative predictive value (96.96%) followed by XGBoost with 95.94%, RF with 95.59%, and GLMNET with 94.27%. With 28-cores high performance computing, RF took 1.35 hours CPU usage, SVM 1.10 hours, XGBoost 48 seconds, and GLMNET 47 seconds, respectively. Conclusions: SVM, RF, and XGBoost are the top three best models for AD prediction. SVM performs better in handling overfitting problem in the training set with small size than RF and XGBoost and also achieved best performance in the testing set.Item The Correlation Between Menopause and Quiet Standing Variable Changes(2023) Hurt, Paige; Zhang, Fan; Meza, Sebastian; Campbell, Blake; Kennedy, Shawn; Buxton, Natasha; Patterson, RitaPurpose: During menopause, individuals experience hormonal changes that can affect body systems contributing to balance. It has been established that balance declines with increasing age, but cohort studies have shown that there is a significant increase in falls during the perimenopausal phase of an individual’s life. A previous study has also been able to relate kyphosis and osteoporosis, postmenopausal changes often associated with estrogen deficiency, to altered standing balance specifically an increase in velocity of the center of pressure (COP) displacement compared to premenopausal females, though this study had a relatively small sample size. The purpose of our study is to measure sway to determine if there are meaningful balance changes that occur during the menopausal transition. This could indicate intervention strategies and decrease risk for falls in postmenopausal females. Methods: Data was obtained from ongoing quiet standing project at the University of North Texas Health Science Center (UNTHSC). Patients visiting the osteopathic manipulative medicine (OMM) and geriatric clinic were asked to quietly stand on a Bertec force plate (Bertec, Columbus, Ohio) for 30 seconds with their eyes open and eyes closed. A total of 475 females were stratified into two groups those less than 48 years old were considered premenopausal (total = 188) and those 48 and older were considered perimenopausal or postmenopausal (total = 287). Results: When comparing the menopausal group to the pre-menopausal group with eyes open, 13 out of 20 sway variables showed a statistically significant difference, similarly with eyes closed, 17 out of 20 variables showed statistical significance. The most significant changes in variables in participants with their eyes open were found for the range of the COP in the anterior posterior direction (AP) and velocity. For the premenopausal group, the average range of COP AP direction was 13.09 mm, while the average range of COP AP in the postmenopausal group was 16.45 mm. The velocity showed a similar change in that the premenopausal group showed an average velocity of 7.84 mm/s2, while the menopausal group had an average velocity of 10.76 mm/s2. Conclusion: The increase in the average range of COP AP and average velocity between the two groups leads us to hypothesize that the menopausal group may have a decline in postural control due to hormonal changes. With the significant difference in the majority of the SWAY variables, it appears the menopausal group has increased movement during quiet standing at an increased velocity. Suggesting that this group is having increased difficulty in modulating the position of their body, which could lead to increased likelihood of fall. We also hypothesize that the menopausal group likely relies on vision more for stability because of age related changes in proprioception and strength. Further studies would be required to determine if these changes are related to age or menopause. In the future, identifying balance changes associated with menopause should be studied. This will lead to better monitoring and early preventative measures to prevent falls.Item Evaluation of Neighborhood-Level Disadvantage and Cognition in Mexican American and Non-Hispanic White Adults 50 Years and Older in the US(American Medical Association, 2023-08-30) Wong, Christina G.; Miller, Justin B.; Zhang, Fan; Rissman, Robert A.; Raman, Rema; Hall, James R.; Petersen, Melissa E.; Yaffe, Kristine; Kind, Amy J.; O'Bryant, Sid E.; Team, HABS-HD StudyIMPORTANCE: Understanding how socioeconomic factors are associated with cognitive aging is important for addressing health disparities in Alzheimer disease. OBJECTIVE: To examine the association of neighborhood disadvantage with cognition among a multiethnic cohort of older adults. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, data were collected between September 1, 2017, and May 31, 2022. Participants were from the Health and Aging Brain Study-Health Disparities, which is a community-based single-center study in the Dallas/Fort Worth area of Texas. A total of 1614 Mexican American and non-Hispanic White adults 50 years and older were included. EXPOSURE: Neighborhood disadvantage for participants' current residence was measured by the validated Area Deprivation Index (ADI); ADI Texas state deciles were converted to quintiles, with quintile 1 representing the least disadvantaged area and quintile 5 the most disadvantaged area. Covariates included age, sex, and educational level. MAIN OUTCOMES AND MEASURES: Performance on cognitive tests assessing memory, language, attention, processing speed, and executive functioning; measures included the Spanish-English Verbal Learning Test (SEVLT) Learning and Delayed Recall subscales; Wechsler Memory Scale, third edition (WMS-III) Digit Span Forward, Digit Span Backward, and Logical Memory 1 and 2 subscales; Trail Making Test (TMT) parts A and B; Digit Symbol Substitution Test (DSST); Letter Fluency; and Animal Naming. Raw scores were used for analyses. Associations between neighborhood disadvantage and neuropsychological performance were examined via demographically adjusted linear regression models stratified by ethnic group. RESULTS: Among 1614 older adults (mean [SD] age, 66.3 [8.7] years; 980 women [60.7%]), 853 were Mexican American (mean [SD] age, 63.9 [7.9] years; 566 women [66.4%]), and 761 were non-Hispanic White (mean [SD] age, 69.1 [8.7] years; 414 women [54.4%]). Older Mexican American adults were more likely to reside in the most disadvantaged areas (ADI quintiles 3-5), with 280 individuals (32.8%) living in ADI quintile 5, whereas a large proportion of older non-Hispanic White adults resided in ADI quintile 1 (296 individuals [38.9%]). Mexican American individuals living in more disadvantaged areas had worse performance than those living in ADI quintile 1 on 7 of 11 cognitive tests, including SEVLT Learning (ADI quintile 5: beta = -2.50; 95% CI, -4.46 to -0.54), SEVLT Delayed Recall (eg, ADI quintile 3: beta = -1.11; 95% CI, -1.97 to -0.24), WMS-III Digit Span Forward (eg, ADI quintile 4: beta = -1.14; 95% CI, -1.60 to -0.67), TMT part A (ADI quintile 5: beta = 7.85; 95% CI, 1.28-14.42), TMT part B (eg, ADI quintile 5: beta = 31.5; 95% CI, 12.16-51.35), Letter Fluency (ADI quintile 4: beta = -2.91; 95% CI, -5.39 to -0.43), and DSST (eg, ADI quintile 5: beta = -4.45; 95% CI, -6.77 to -2.14). In contrast, only non-Hispanic White individuals living in ADI quintile 4 had worse performance than those living in ADI quintile 1 on 4 of 11 cognitive tests, including SEVLT Learning (beta = -2.35; 95% CI, -4.40 to -0.30), SEVLT Delayed Recall (beta = -0.95; 95% CI, -1.73 to -0.17), TMT part B (beta = 15.95; 95% CI, 2.47-29.44), and DSST (beta = -3.96; 95% CI, -6.49 to -1.43). CONCLUSIONS AND RELEVANCE: In this cross-sectional study, aging in a disadvantaged area was associated with worse cognitive functioning, particularly for older Mexican American adults. Future studies examining the implications of exposure to neighborhood disadvantage across the life span will be important for improving cognitive outcomes in diverse populations.Item Hyperemesis Cannabinoid Syndrome Chart Review(2023) Patterson, Tyler; Porter, Cardon; Birky, Jaxton; Judd, Dallin; Zhang, Fan; Espinoza, Anna; Galke, Curtis; Petersen, JamesPurpose: The purpose of this research study is to determine if the combination of Compazine and Benadryl is a superior method of treatment for patients who present with nausea and vomiting symptoms due to a diagnosis of Hyperemesis Cannabinoid Syndrome. Based on patient charts from a regional hospital emergency department in Oklahoma, an analysis was performed to determine if this combination of medications is more effective in quickly reducing the nausea, vomiting, and other symptoms associated with cannabis use.Methods: An agreement was created between a regional hospital in Duncan Oklahoma and The University of North Texas Health Science Center which allowed students at the Texas College of Osteopathic Medicine to review and analyze a total of 75 patients’ charts from the regional hospital. IRB approval was obtained for this project. The chart review process consisted of evaluating the patient's age, chief complaint, abnormal lab values, history of present illness (HPI), and medications administered in the emergency department. The important variables that assisted in this study were drug screening values, and the medications administered. The main outcome for which statistical efficacy of drug treatment was measured by duration of stay in the emergency department and whether or not an additional dose of medication was given. Results: This chart review showed that the 12 patients that used the Benadryl and Compazine combination had a decreased time spent in the emergency department by an average of 56 minutes when compared to 38 patients who received alternative medications. The average time spent in the emergency department for those who received Benadryl and Compazine was 127 minutes vs the average time spent for those using an alternative medication was 183 minutes. The typical dose was 50 mg of Benadryl and 10 mg of Compazine. While using an ANOVA statistical analysis these doses showed a significantly statistical relationship by decreasing provider-to-discharge time with a p value of 0.012. It was also found while using a logistic regression analysis that those patients who received this combination as their initial dose were less likely to receive a second dose. This relationship also was statistically significant with a p value of 0.005. It was also noted in the logistic analysis that females were more likely to receive a second dose when compared to men. This relationship also showed a relationship with significance and a p value of 0.037. Conclusions:The findings from this study recommend and encourage providers who are in a setting where Hyperemesis Cannabinoid Syndrome is prevalent to consider the medication combination of 50 mg of Benadryl and 10 mg of Compazine when providing treatment. There may be multiple ways to alleviate the discomfort and symptoms that patients may present with, however the combination stated above appears most effective to reduce provider-to-discharge time 56 minutes and eliminate second doses of medication based off of the data reviewed from the charts provided.Item Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data(MDPI, 2022-11-17) Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; O'Bryant, Sid E.Accurate detection is still a challenge in machine learning (ML) for Alzheimer's disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly distributed within classes. Here, we present a hyperparameter tuning workflow with high-performance computing (HPC) for imbalanced data related to prevalent mild cognitive impairment (MCI) and AD in the Health and Aging Brain Study-Health Disparities (HABS-HD) project. We applied a single-node multicore parallel mode to hyperparameter tuning of gamma, cost, and class weight using a support vector machine (SVM) model with 10 times repeated fivefold cross-validation. We executed the hyperparameter tuning workflow with R's bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)'s Lonestar6 system. The computational time was dramatically reduced by up to 98.2% for the high-performance SVM hyperparameter tuning model, and the performance of cross-validation was also improved (the positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%). Our results show that a single-node multicore parallel structure and high-performance SVM hyperparameter tuning model can deliver efficient and fast computation and achieve outstanding agility, simplicity, and productivity for imbalanced data in AD applications.Item Identification of long non-coding RNA-related and -coexpressed mRNA biomarkers for hepatocellular carcinoma(BioMed Central Ltd., 2019-01-31) Zhang, Fan; Ding, Linda; Cui, Li; Barber, Robert C.; Deng, BinBackground: While changes in mRNA expression during tumorigenesis have been used widely as molecular biomarkers for the diagnosis of a number of cancers, the approach has limitations. For example, traditional methods do not consider the regulatory and positional relationship between mRNA and lncRNA. The latter has been largely shown to possess tumor suppressive or oncogenic properties. The combined analysis of mRNA and lncRNA is likely to facilitate the identification of biomarkers with higher confidence. Results: Therefore, we have developed an lncRNA-related method to identify traditional mRNA biomarkers. First we identified mRNAs that are differentially expressed in Hepatocellular Carcinoma (HCC) by comparing cancer and matched adjacent non-tumorous liver tissues. Then, we performed mRNA-lncRNA relationship and coexpression analysis and obtained 41 lncRNA-related and -coexpressed mRNA biomarkers. Next, we performed network analysis, gene ontology analysis and pathway analysis to unravel the functional roles and molecular mechanisms of these lncRNA-related and -coexpressed mRNA biomarkers. Finally, we validated the prediction and performance of the 41 lncRNA-related and -coexpressed mRNA biomarkers using Support Vector Machine model with five-fold cross-validation in an independent HCC dataset from RNA-seq. Conclusions: Our results suggested that mRNAs expression profiles coexpressed with positionally related lncRNAs can provide important insights into early diagnosis and specific targeted gene therapy of HCC.Item Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq(BioMed Central Ltd., 2020-12-03) Zhang, Fan; Deng, Chris K.; Wang, Mu; Deng, Bin; Barber, Robert C.; Huang, GangBackground: Alternative splicing isoforms have been reported as a new and robust class of diagnostic biomarkers. Over 95% of human genes are estimated to be alternatively spliced as a powerful means of producing functionally diverse proteins from a single gene. The emergence of next-generation sequencing technologies, especially RNA-seq, provides novel insights into large-scale detection and analysis of alternative splicing at the transcriptional level. Advances in Proteomic Technologies such as liquid chromatography coupled tandem mass spectrometry (LC-MS/MS), have shown tremendous power for the parallel characterization of large amount of proteins in biological samples. Although poor correspondence has been generally found from previous qualitative comparative analysis between proteomics and microarray data, significantly higher degrees of correlation have been observed at the level of exon. Combining protein and RNA data by searching LC-MS/MS data against a customized protein database from RNA-Seq may produce a subset of alternatively spliced protein isoform candidates that have higher confidence. Results: We developed a bioinformatics workflow to discover alternative splicing biomarkers from LC-MS/MS using RNA-Seq. First, we retrieved high confident, novel alternative splicing biomarkers from the breast cancer RNA-Seq database. Then, we translated these sequences into in silico Isoform Junction Peptides, and created a customized alternative splicing database for MS searching. Lastly, we ran the Open Mass spectrometry Search Algorithm against the customized alternative splicing database with breast cancer plasma proteome. Twenty six alternative splicing biomarker peptides with one single intron event and one exon skipping event were identified. Further interpretation of biological pathways with our Integrated Pathway Analysis Database showed that these 26 peptides are associated with Cancer, Signaling, Metabolism, Regulation, Immune System and Hemostasis pathways, which are consistent with the 256 alternative splicing biomarkers from the RNA-Seq. Conclusions: This paper presents a bioinformatics workflow for using RNA-seq data to discover novel alternative splicing biomarkers from the breast cancer proteome. As a complement to synthetic alternative splicing database technique for alternative splicing identification, this method combines the advantages of two platforms: mass spectrometry and next generation sequencing and can help identify potentially highly sample-specific alternative splicing isoform biomarkers at early-stage of cancer.Item IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis(Springer Nature, 2012) Zhang, Fan; Drabier, ReneeBackground: Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics). Results: We designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources. IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ. Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study. Conclusions: IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies.Item MaCHTools: Additional functionality for the imputation software MaCH(2016-12-01) Mitchel, Jeffrey S.; Barber, Robert C.; Zhang, FanImputation of unknown genotypes is becoming a standard procedure in exploratory genetic association studies. Imputation is accomplished by comparing observed data from the study population to reference panels of individuals who are from a genetically similar population and genotyped at a dense set of polymorphic sites. Linkage disequilibrium within the reference panels is used to construct haplotypes and extrapolate allelic correlations in the test sample. Imputation has been shown to be accurate for the inference of genotypes at unobserved SNPs, as well as for quality control measures at genotyped locations. Imputing genotypes also allows cohorts that were genotyped on different platforms to be combined in a joint or meta-analysis. One of the most widely used imputation software packages is MaCH (http://csg.sph.umich.edu//abecasis/mach/). MaCH uses a powerful and accurate Markov chain-based algorithm, however its usability is lacking. MaCHTools allows the user to streamline their workflow with MaCH through input file specification, error checking, and QC measures, MaCHTools began as a series of Java scripts used to check input files and QC raw data as an initial step before imputing additional genotypes in MaCH. This set of scripts became invaluable to the GWAS workflow, but they were unpolished and ill-suited for public release to benefit the scientific community. This project aimed to bundle the scripts into a single executable program that provides a graphical user interface (GUI) to facilitate use by students and researchers to aid in streamlining the GWAS workflow. Additional functionalities include more efficient launching of jobs to compute clusters and compatibility with different Linux job handlers, the ability to easily switch between different GWAS projects including switching between different genotype data and reference datasets, more simplistic specification of parameters and thresholds, and several other usability improvements. The GWAS workflow that includes dataset preparation with MaCHTools coupled with haplotype estimation and imputation with MaCH was validated by replicating results from a published study of the genetic basis of Alzheimer’s endophenotypes in the Texas Alzheimer’s Research and Care Consortium. A similar analysis was then performed to determine the genetic basis of D, a latent variable that represents the dementing process.Item Neurodegeneration from the AT(N) framework is different among Mexican Americans compared to non-Hispanic Whites: A Health & Aging Brain among Latino Elders (HABLE) Study(Wiley Periodicals, LLC, 2022-02-09) O'Bryant, Sid E.; Zhang, Fan; Petersen, Melissa E.; Hall, James R.; Johnson, Leigh A.; Yaffe, Kristine; Braskie, Meredith N.; Rissman, Robert A.; Vig, Rocky; Toga, Arthur W.Introduction: We sought to examine a magnetic resonance imaging (MRI)-based marker of neurodegeneration from the AT(N) (amyloid/tau/neurodegeneration) framework among a multi-ethnic, community-dwelling cohort. Methods: Community-dwelling Mexican Americans and non-Hispanic White adults and elders were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing and 3T MRI of the brain. A neurodegeneration MRI meta-region of interest (ROI) biomarker for the AT(N) framework was calculated. Results: Data were examined from n = 1305 participants. Mexican Americans experienced N at significantly younger ages. The N biomarker was significantly associated with cognitive outcomes. N was significantly impacted by cardiovascular factors (e.g., total cholesterol, low-density lipoprotein) among non-Hispanic Whites whereas diabetes (glucose, HbA1c, duration of diabetes) and sociocultural (household income, acculturation) factors were strongly associated with N among Mexican Americans. Discussion: The prevalence, progression, timing, and sequence of the AT(N) biomarkers must be examined across diverse populations.Item Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study(Elsevier Inc., 2019-05-02) O'Bryant, Sid E.; Edwards, Melissa; Zhang, Fan; Johnson, Leigh A.; Hall, James R.; Kuras, Yuliya; Scherzer, Clemens R.Introduction: We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods: Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results: The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion: These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.Item Proteomic profiles for Alzheimer's disease and mild cognitive impairment among adults with Down syndrome spanning serum and plasma: An Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study(Wiley Periodicals, Inc., 2020-06-30) Petersen, Melissa E.; Zhang, Fan; Schupf, Nicole; Krinsky-McHale, Sharon J.; Hall, James R.; Mapstone, Mark; Cheema, Amrita; Silverman, Wayne; Lott, Ira; Rafii, Michael S.; Handen, Benjamin; Klunk, William; Head, Elizabeth; Christian, Bradley; Foroud, Tatiana; Lai, Florence; Rosas, H. Diana; Zaman, Shahid; Ances, Beau M.; Wang, Mei-Cheng; Tycko, Benjamin; Lee, Joseph H.; O'Bryant, Sid E.Introduction: Previously generated serum and plasma proteomic profiles were examined among adults with Down syndrome (DS) to determine whether these profiles could discriminate those with mild cognitive impairment (MCI-DS) and Alzheimer's disease (DS-AD) from those cognitively stable (CS). Methods: Data were analyzed on n = 305 (n = 225 CS; n = 44 MCI-DS; n = 36 DS-AD) enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS). Results: Distinguishing MCI-DS from CS, the serum profile produced an area under the curve (AUC) = 0.95 (sensitivity [SN] = 0.91; specificity [SP] = 0.99) and an AUC = 0.98 (SN = 0.96; SP = 0.97) for plasma when using an optimized cut-off score. Distinguishing DS-AD from CS, the serum profile produced an AUC = 0.93 (SN = 0.81; SP = 0.99) and an AUC = 0.95 (SN = 0.86; SP = 1.0) for plasma when using an optimized cut-off score. AUC remained unchanged to slightly improved when age and sex were included. Eotaxin3, interleukin (IL)-10, C-reactive protein, IL-18, serum amyloid A , and FABP3 correlated fractions at r2 > = 0.90. Discussion: Proteomic profiles showed excellent detection accuracy for MCI-DS and DS-AD.Item Proteomic profiles of incident mild cognitive impairment and Alzheimer's disease among adults with Down syndrome(Wiley Periodicals, Inc., 2020-05-21) O'Bryant, Sid E.; Zhang, Fan; Silverman, Wayne; Lee, Joseph H.; Krinsky-McHale, Sharon J.; Pang, Deborah; Hall, James R.; Schupf, NicoleIntroduction: We sought to determine if proteomic profiles could predict risk for incident mild cognitive impairment (MCI) and Alzheimer's disease (AD) among adults with Down syndrome (DS). Methods: In a cohort of 398 adults with DS, a total of n = 186 participants were determined to be non-demented and without MCI or AD at baseline and throughout follow-up; n = 103 had incident MCI and n = 81 had incident AD. Proteomics were conducted on banked plasma samples from a previously generated algorithm. Results: The proteomic profile was highly accurate in predicting incident MCI (area under the curve [AUC] = 0.92) and incident AD (AUC = 0.88). For MCI risk, the support vector machine (SVM)-based high/low cut-point yielded an adjusted hazard ratio (HR) = 6.46 (P < .001). For AD risk, the SVM-based high/low cut-point score yielded an adjusted HR = 8.4 (P < .001). Discussion: The current results provide support for our blood-based proteomic profile for predicting risk for MCI and AD among adults with DS.Item Proteomic profiles of prevalent mild cognitive impairment and Alzheimer's disease among adults with Down syndrome(Wiley Periodicals, Inc., 2020-04-17) Petersen, Melissa E.; Zhang, Fan; Krinsky-McHale, Sharon J.; Silverman, Wayne; Lee, Joseph H.; Pang, Deborah; Hall, James R.; Schupf, Nicole; O'Bryant, Sid E.Introduction: We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease (AD) in the general population would apply to adults with Down syndrome (DS). Methods: Plasma samples were obtained from 398 members of a community-based cohort of adults with DS. A total of n = 186 participants were determined to be non-demented and without mild cognitive impairment (MCI) at baseline and throughout follow-up; n = 50 had prevalent MCI; n = 42 had prevalent AD. Results: The proteomic profile yielded an area under the curve (AUC) of 0.92, sensitivity (SN) = 0.80, and specificity (SP) = 0.98 detecting prevalent MCI. For detecting prevalent AD, the proteomic profile yielded an AUC of 0.89, SN = 0.81, and SP = 0.97. The overall profile closely resembled our previously published profile of AD in the general population. Discussion: These data provide evidence of the applicability of our blood-based algorithm for detecting MCI/AD among adults with DS.Item Proteomic Profiles of Tau Positivity among an Ethnically Diverse Cohort: An HABS-HD Study(2023) Do, Tony; Petersen, Melissa; Zhang, Fan; Hall, James; O'Bryant, SidPurpose: PET tau has been well documented to precede Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). In the pursuit of increasing the accessibility of AD diagnosis, studies have shown that blood biomarkers including ptau181 trend with PET tau in Non-Hispanic Whites (NHW). Current literature shows limited studies on Mexican Americans (MA) who have a higher risk of AD at earlier ages. MA populations have shown to have significantly higher burden of blood biomarkers/metabolic markers that are associated with MCI including ptau181, insulin, and glucagon, but lower in plasma amyloid. Our aim is to look at the utility of AT(N) (amyloid, tau, neurodegeneration) biomarkers in the detection of PET Tau positivity status among MA and NHWs Methods: Data were analyzed from n=401 participants (Total sample [n=21 Tau positive, n=380 Tau negative]; Black [n=11 Tau positive, n=216 Tau negative]; Hispanic [n=5 Tau positive, n=50 Tau negative]; Non-Hispanic whites [n=5 Tau positive, n=114 Tau negative]) from a community-based study of brain aging the Health and Aging Brain Study- Health Disparities (HABS-HD). HABS-HD participants underwent a clinical interview, neuropsychological testing, blood draw, functional medical exam and neuroimaging as a part of the study’s protocol. Plasma blood biomarkers used in this study consisted of Amyloid Beta 40, 42, Total Tau, Ptau181 and NFL derived using Single Molecule Array Technology (SIMOA) on an HDX platform. PET Tau positivity status was determined based on a clinical read. Support Vector Machine (SVM) models were used with plasma ATN biomarkers as predictors of PET Tau positivity status (Positive; Negative). SVM models were run with 10 times, five-fold repeated cross--validation and included models with and without demographics. Results: In the total sample, ATN biomarkers produced an area under the curve (AUC) of 98% with a sensitivity [SN] of 100% and Specificity [SP] of 73% for distinguishing PET Tau Positive cases from PET Tau Negative. The same ATN biomarkers produced for Black participants an AUC of 98% (SN=100%, SP=80%), for Hispanic participants an AUC of 100% (SN=100%, SP=100%), and for Non-Hispanic White participants an AUC of 98% (SN=100%, SP=70%). The addition of demographic variables of age, gender, and education produced a slight increase in the AUC for both black and non-Hispanic white participants by 1%. The top biomarkers were shown to vary by race and ethnic group. Conclusions: The results further support the AT(N) biomarkers as a viable method in predicting AD/PET tau positivity, as well as confirming that ptau181 has heavy influence in MA. The varying results of top biomarkers between groups confirm that ethnic background plays a strong role in biomarker profiles contributing to AD. An interesting finding was that demographic factors were ranked higher in black participants in distinguishing PET Tau positivity as compared to NHW and MA. Future work should expand on ptau181 value in relation to MCI state/AD progression in MA.