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4 Neurophenotypes and recovery trajectories following laboratory-confirmed SARS-CoV-2 infection
- Divya Prabhakaran, Gregory S Day, Bala Munipalli, Beth Rush, Lauren Pudalov, Shehzad Niazi, Emily Brennan, Harry R Powers, Arjun Athreya, Karen Blackmon
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 877-879
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Objective:
Cognitive sequelae are reported in 20-25% of patients following SARS-CoV-2 infection. It remains unclear whether post-infection sequelae cluster into a uniform cognitive syndrome. In this cohort study, we characterized post-COVID neuropsychological outcome clusters, identified factors associated with cluster membership, and examined 6-month recovery trajectories by cluster.
Participants and Methods:The Mayo Clinic Institutional Review Board approved study protocols. Informed consent was obtained from all participants. Participants (> 18 years old) were recruited from a hospital-wide registry of Mayo Clinic Florida patients who tested positive for SARS-CoV-2 infection from July 2020 to Feb 2022. We abstracted participant health history and COVID-19 disease severity (NIAID score) from the electronic health record and retrieved Area Deprivation Index (ADI) scores as a measure of neighborhood socioeconomic disadvantage. We assessed objective cognitive performance with the CNS Vital-Signs (CNSVS) and subjective neuropsychological symptoms with the Neuropsych Questionnaire-45 (NPQ-45). Results were used as input features in a K-means clustering analysis to derive neurophenotypes. Chi-square and analysis of variance (AnOvA) tests were used to identify clinical and sociodemographic factors associated with cluster membership. Participants repeated the CNS Vital Signs, NPQ-45, as well as the Medical Outcomes Survey (MOS SF-36) and a posttraumatic stress disorder (PTSD) checklist (PCL-C 17) 6 months following initial testing. Repeated-measures ANOVA was used to assess change in neurocognitive performance over time by cluster. Significance was set at P < 0.05.
Results:Our cohort consisted of 205 participants (171 ambulatory, 34 hospitalized) who completed post-acute outcome assessment a mean of 5.7 (± 3.8) weeks following testing positive for SARS-CoV-2. K-means clustering with elbow method fitting identified three subgroups (see figure). The first cluster (N = 31) is characterized by executive dysfunction, greater socioeconomic disadvantage, and higher rates of obesity. The second cluster (N = 32) is characterized by memory and speed impairment, higher COVID severity, prevalent anosmia (70%), and greater severity of memory complaints, depression, anxiety, and fatigue. The third and largest cluster (N = 142) is absent cognitive impairment. Approximately 39% of participants completed the 6-month outcome assessment (N=79). Regardless of cluster membership, verbal memory, psychomotor speed, and reaction time scores improved over time. Regardless of timepoint, cluster 1 (dysexecutive) showed lower scores on cognitive flexibility and complex attention and cluster 2 (memory-speed impaired) showed lower scores on verbal memory, psychomotor speed, and reaction time. Modeling of cluster by timepoint interactions showed a steeper slope of improvement in complex attention and cognitive flexibility in cluster 1 (dysexecutive). Cluster 3 (normal) showed significant improvement in fatigue while cluster 2 (memory-speed impaired) continued to report moderate-severe fatigue, worse medical outcomes, and higher PTSD symptom severity scores at six months.
Conclusions:Most participants were cognitively normal or experienced cognitive recovery following SARS-CoV-2 infection. The 25-30% of participants who showed cognitive impairment cluster into two different neurophenotypes. The dysexecutive phenotype was associated with socioeconomic factors and medical comorbidities that are non-specific to COVID-19, while the amnestic phenotype was associated with COVID-19 severity and anosmia. These results suggest that cognitive sequelae following SARS-CoV-2 infection are not uniform. Deficits may be influenced by distinct patient- and disease-specific factors, necessitating differentiated treatment approaches.
310 Transcriptomics for gallbladder cancer prognosis
- Linsey Jackson, Loretta K. Allotey, Kenneth Valles, Gavin R. Oliver, Asha Nair, Daniel R. Obrien, Rondell P. Graham, Mitesh J Borad, Arjun Athreya, Lewis R. Roberts
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- Journal:
- Journal of Clinical and Translational Science / Volume 6 / Issue s1 / April 2022
- Published online by Cambridge University Press:
- 19 April 2022, p. 54
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OBJECTIVES/GOALS: Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients prognosis. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. METHODS/STUDY POPULATION: Current predictive models for GBC outcomes often utilize clinical data only. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Rochester, Minnesota, Daegu, Korea, Vilnius, Lithuania, and Calgary, Canada. We will perform next-generation RNA sequencing on these tissue samples. The patients clinical, pathologic and survival data will be abstracted from the medical record. Random forests, support vector machines, and gradient boosting machines will be applied to train the data. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. RESULTS/ANTICIPATED RESULTS: Our preliminary analysis of next generation RNA sequencing from 18 GBC tissue samples identified recurrent mutations in genes enriched in pathways in cytoskeletal signaling, cell organization, cell movement, extracellular matrix interaction, growth, and proliferation. The top three most significantly altered pathways, actin cytoskeleton signaling, hepatic fibrosis/hepatic stellate cell activation, and epithelial adherens junction signaling, emphasized a molecular metastatic and invasive fingerprint in our patient cohort. This molecular fingerprint is consistent with the previous knowledge of the highly metastatic nature of gallbladder tumors and is also manifested physiologically in the patient cohort. DISCUSSION/SIGNIFICANCE: Integrative analysis of molecular and clinical characterization of GBC has not been fully established, and minimal improvement has been made to the survival of these patients. If overall survival can be better predicted, we can gain a greater understanding of key biomarkers driving the tumor phenotype.
54443 Pharmacogenomic Profiling of East and West African Populations
- Linsey Jackson, Ruben Bonilla Guerrero, Arjun Athreya, Lewis R. Roberts
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- Journal:
- Journal of Clinical and Translational Science / Volume 5 / Issue s1 / March 2021
- Published online by Cambridge University Press:
- 30 March 2021, pp. 102-103
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ABSTRACT IMPACT: Genomic variation likely plays a role in differences in rates of adverse reactions and efficacy for African populations, and this research will add to the understudied field of pharmacogenomics in African populations. OBJECTIVES/GOALS: We aim to characterize the frequency of variants in clinically relevant genes in East and West African populations to assess the prevalence of potential drug-gene interactions. METHODS/STUDY POPULATION: Our pilot study will consist of 100 Somali patients enrolled at Mayo Clinic Rochester and 100 Ghanaian patients recruited at Teaching Hospitals in Ghana. Germline DNA will be extracted from pre-existing blood samples. Sequencing will be performed using Admera Health’s PGxOne Plus test, interrogating a panel of 62 genes. Variants will be reported along with the predicted response for a list of drugs. Differences between frequencies of variants in East and West African populations will be analyzed. We will look for correlations with reported adverse reaction rates. We will then compare our findings with allele frequency data from publicly available data bases. Additionally, we will analyze the flanking regions of the panel for novel variants, using machine learning to predict gene-drug interactions. RESULTS/ANTICIPATED RESULTS: African populations are known to have more genetic diversity than any other population. Additionally, only African-Americans, African-Caribbeans from Barbados, Esan and Yoruba Nigerians, Gambians, Kenyans, and Sierra Leoneans are accounted for within the publicly available data bases most often used for variant studies. Thus, it is anticipated that we will find differences in the variant allele frequencies of our populations compared to European allele frequencies, and differences in frequencies between the East and West African populations. In the 200 base pair flanking regions that are sequenced in the assay along with the known variant regions, we may find novel previously unreported variants. DISCUSSION/SIGNIFICANCE OF FINDINGS: The lack of knowledge of pharmacogenomic variation in African populations contributes to ethnic disparities in patient outcomes. This study addresses this gap by adding to our comprehension of variants in clinically relevant genes, giving insight into underlying mechanisms of ethnicity-based drug responses.