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Exploring the gut-brain axis in Parkinson’s disease and the influence of physical fitness to restore gut health

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Exploring the gut-brain axis in Parkinson’s disease and the influence of physical fitness to restore gut health

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Content EXPLORING THE GUT-BRAIN AXIS IN PARKINSON’S DISEASE AND THE INFLUENCE OF PHYSICAL FITNESS TO RESTORE GUT HEALTH by Kaylie Rebekah-Marsh Zapanta A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (BIOKINESIOLOGY) August 2023 Copyright 2023 Kaylie Zapanta ii Dedication This dissertation project is dedicated to two people who have played critical roles in my journey to becoming a scientist and educator. First, this dissertation is dedicated to my husband, Kevin Zapanta. You were the first person to believe in me when I initially had the idea to become a scientist. I would not be here today without your unconditional love and support- it is my greatest gift. You continually put my needs above yours and motivate me daily to follow my dreams. I love you so much! Second, this dissertation is dedicated to my grandfather, Dr. Warren Marsh, who is my inspiration for obtaining a Ph.D. in the first place. You have taught me to fearlessly work towards the call God has placed on my heart to follow and positively contribute my gifts to the world. I am honored to follow in your footsteps as a fellow USC doctoral alum. iii Acknowledgments My time at USC has been a journey full of obstacles and opportunity, adversity and growth, failure, and success. The experiences I have acquired in the last six years have helped me to develop into the scientist and educator that I have always dreamt of being. Ultimately, I give glory to God for guiding me and protecting me as I walk this unknown road and forge a new research path that can hopefully impact the lives of people living with Parkinson’s disease and beyond. However, I would not be at the proverbial finish line of my Ph.D. without the support, expertise, and intelligence of my advisor, committee, past mentors and classmates, family, and loved ones. First and foremost, I would like to thank my advisor, Dr. Beth Fisher. Words cannot express how grateful I am that you agreed to work with me and welcomed me into your lab with open arms. Particularly at a time when I thought my dreams of a Ph.D. and career in science had come to an end, you took a chance on me and this project. I have no doubt that, without your willingness to work with me, I would not be where I am today. From the moment we began working together, you have believed in me and have seen the potential scientist in me- even if, at times, I couldn’t see it myself. Your clinical and research expertise in the Parkinson’s disease research space and beyond is unmatched, and I have learned so much from your mentorship. You have challenged me and were earnest in providing feedback and guidance, which ultimately made me a better scientist, writer, educator, and overall person today. Most importantly, you supported me through unforeseen and very drastic life events during my time in the Ph.D. program and saw me as a human being first and foremost. Thank you! I am so blessed that you are my mentor but even more so, that you have become a great friend. iv I would also like to thank all of my committee members. Each of you played an invaluable role in this dissertation project, as well as in developing my scientific identity. Notably, Dr. E. Todd Schroeder, I am immensely grateful for your unending support during my time as a Master’s student in the exercise physiology lab, your guidance and encouragement as I transitioned to working with Dr. Fisher, and your continually making me feel welcome in the exercise lab through my Ph.D. program. Thank you for providing your expert perspective on exercise physiology that has helped shape my dissertation project and future research. Dr. Giselle Petzinger, thank you for generously bestowing your knowledge and expertise of Parkinson’s disease research, for our inspiring and thought-provoking conversations about my research, and for inviting me into your lab and to experience the Neuroplasticity retreat to collaborate with other scientists and experts in the field. Dr. Kristan Leech, thank you for providing me with your expertise in neuroscience and the insightful feedback you continually provided that helped me to shape my research study. Dr. Elizabeth Bess, I am grateful to have had you on my committee to provide your expertise in the gut microbiota, your willingness to support this project, and your constant, invaluable guidance on the complexities of the gut microbiota that allowed for me to grow as a gut microbiota scientist. I would like to thank Drs. Jim Gordon and Chris Powers, and the all the faculty and staff within the Division. In particular, I am thankful for Drs. Kulig, Winstein, Salem, Schweighofer, and Finley for the knowledge you all imparted to me in coursework and the support you provided to me during my time in the program. To Drs. Erceg and Matthews, I am fortunate to have been your teaching assistant. You both exemplified passion and excellence in your teaching, and it is inspiring to me as a future professor. v I am grateful to all the past and current Neuroplasticity and Imaging Lab members and physical therapy students who helped me with my dissertation. Alex, Mac, Pooja, Gina, Natalie, Karen, Jack, Domeryne, Maggie, and Ava—thank you for all your help and effort in assisting in my dissertation project, brainstorming research ideas and general support as fellow students. I am particularly grateful for my time spent with Juliet Moore and Malcolm Jones, working on our dissertation projects together and supporting one another through the Master’s and Ph.D. programs at USC. I would also like to acknowledge and thank the collaborating teams on this dissertation project. Drs. Claire McLean, owner of Rogue Physical Therapy, and Julie Hershberg, owner of Re+Active Physical Therapy, for the integral part you both played in participant recruitment and data collection for this project. Thank you both for your constant support of this project, willingness to collaborate with our USC team, and overall enthusiasm for this research. I appreciate your giving me the opportunities to speak to the physical therapy and Parkinson’s disease communities to share this area of study. In addition, I am grateful for the team at EZBiome, who were integral in the data extraction and analysis steps to my dissertation project. Without their guidance, this dissertation work would not have been possible. I would be remiss if I did not mention my former mentors whom I have been fortunate to have received guidance from during my entire graduate school tenure. Dr. James Bagley, thank you for inspiring me to begin this journey to become a scientist and educator. Your passion for science and encouraging mentorship inspire me to one day become just a great mentor to my future students. I and grateful for your mentorship and friendship. Dr. Christina Dieli-Conwright, thank you for introducing me to clinical vi exercise research and exposing me to opportunities to write grants, present, and conduct research studies. Lastly, I would like to thank my family and friends for supporting and encouraging me throughout the last six years during my time as a student at USC. Thank you for always being there for me during the difficult or stressful periods of this program. To my dad, grandparents, aunt Lynda, Emily, Jacob, Brian, Isaac, Amy, Christina, and all my other family and friends, thank you for providing me perspective when times in this program got tough, and for motivating me to keep going. I could not have done this without you all in my corner. vii Table of Contents Dedication .................................................................................................................................... ii Acknowledgments ....................................................................................................................... iii List of Tables ................................................................................................................................x List of Figures.............................................................................................................................. xi Abbreviations ............................................................................................................................. xii Abstract ..................................................................................................................................... xiii Chapter 1 Background & Significance ............................................................................... 1 Statement of the Problem ........................................................................................................... 1 Traditional, ‘Brain-First’ Etiology of PD ....................................................................................... 1 ‘Body-First’ Hypothesis of PD ..................................................................................................... 3 Causality Dilemma of PD ............................................................................................................ 9 PD treatments that target the GBA ........................................................................................... 10 Gaps in Knowledge & Overall Aims ......................................................................................... 15 Specific Aims............................................................................................................................. 16 AIM 1: ........................................................................................................................................ 16 AIM 2: ........................................................................................................................................ 16 AIM 3a: ...................................................................................................................................... 16 AIM 3b (Exploratory Analysis): ................................................................................................. 17 IMPACT: .................................................................................................................................... 17 Chapter 2 Gut microbiota differences .............................................................................. 18 ABSTRACT: .............................................................................................................................. 18 INTRODUCTION: ..................................................................................................................... 21 METHODS: ............................................................................................................................... 23 Study Participant Recruitment and Enrollment: .................................................................................. 23 Study Procedures: ............................................................................................................................... 24 Stool Sample Collection & Storage: .................................................................................................... 24 16s rRNA MTP Sequencing: ............................................................................................................... 25 Measurements of Potential Covariates: .............................................................................................. 26 STATISTICS: ............................................................................................................................ 28 Bioinformatics: ..................................................................................................................................... 28 Species Richness: ............................................................................................................................... 28 Species Evenness & Richness (Diversity): ......................................................................................... 29 Taxonomic Differences: ...................................................................................................................... 30 Covariate Identification: ....................................................................................................................... 31 RESULTS: ................................................................................................................................. 32 OTUs and Rarefaction Curves: ........................................................................................................... 32 Demographics: .................................................................................................................................... 33 Covariate Identification: ....................................................................................................................... 34 viii Species Richness only: ....................................................................................................................... 34 Species Richness and Evenness (Diversity): ..................................................................................... 35 Taxonomic Differences: ...................................................................................................................... 35 Phyla differences: ................................................................................................................................ 36 Class, order, family, genus, and species differences: ........................................................................ 37 Conclusion: ............................................................................................................................... 43 Chapter 3 Aim 1, Fitness and the gut microbiota in PwPD ............................................. 45 ABSTRACT: .............................................................................................................................. 45 INTRODUCTION: ..................................................................................................................... 47 METHODS: ............................................................................................................................... 48 Study Design: ...................................................................................................................................... 48 Participant Demographics: .................................................................................................................. 49 Aerobic Fitness Measurement: ........................................................................................................... 49 Stool Sample Collection & Storage: .................................................................................................... 49 Covariate Measurements: ................................................................................................................... 49 STATISTICS: ............................................................................................................................ 49 16s rRNA Amplicon Sequencing and Bioinformatics: ......................................................................... 50 Covariate Identification: ....................................................................................................................... 50 Linear Regression Models: ................................................................................................................. 51 Taxonomic Differences & Pearson Correlation: .................................................................................. 51 RESULTS: ................................................................................................................................. 52 DISCUSSION: ........................................................................................................................... 55 CONCLUSION: ......................................................................................................................... 59 Chapter 4 Aim 2, Motor function & the gut microbiota in PwPD ..................................... 61 ABSTRACT: .............................................................................................................................. 61 INTRODUCTION: ..................................................................................................................... 63 METHODS: ............................................................................................................................... 64 Study Design: ...................................................................................................................................... 64 Participant Demographics: .................................................................................................................. 65 Unified Parkinson’s disease rating scale III (UPDRS-III): ................................................................... 65 Short Physical Performance Battery (SPPB): ..................................................................................... 65 Stool Sample Collection & Storage: .................................................................................................... 66 Covariate Measurements: ................................................................................................................... 66 STATISTICS: ............................................................................................................................ 66 16s rRNA Amplicon Sequencing & Bioinformatics: ............................................................................ 66 Covariate Identification: ....................................................................................................................... 67 Linear Regression Models: ................................................................................................................. 68 Taxonomic Differences & Pearson Correlation: .................................................................................. 68 RESULTS: ................................................................................................................................. 70 DISCUSSION: ........................................................................................................................... 76 CONCLUSION .......................................................................................................................... 78 Chapter 5 Aim 3, Cognition and the gut microbiota in PwPD ......................................... 79 ABSTRACT: .............................................................................................................................. 79 ix INTRODUCTION: ..................................................................................................................... 82 METHODS: ............................................................................................................................... 84 Study Design: ...................................................................................................................................... 84 Participant Demographics: .................................................................................................................. 84 The Montreal Cognitive Assessment (MoCA): .................................................................................... 84 The Repeated Battery for Assessment of Neuropsychological Status (RBANS): .............................. 85 Covariate Measurements: ................................................................................................................... 86 Stool Sample Collection & Storage: .................................................................................................... 86 STATISTICS: ............................................................................................................................ 86 16s rRNA Amplicon Sequencing & Bioinformatics: ............................................................................ 86 Covariate Identification: ....................................................................................................................... 87 Linear Regression Models: ................................................................................................................. 87 Taxonomic Differences & Pearson Correlation: .................................................................................. 88 RESULTS: ................................................................................................................................. 89 MoCA Scores ...................................................................................................................................... 89 RBANS Scores .................................................................................................................................... 93 DISCUSSION: ........................................................................................................................... 96 CONCLUSION ........................................................................................................................ 100 Chapter 6 Summary & Conclusions ............................................................................... 101 CLINICAL IMPLICATIONS ..................................................................................................... 106 LIMITATIONS.......................................................................................................................... 106 FUTURE RESEARCH ............................................................................................................ 107 REFERENCES: ...................................................................................................................... 111 APPENDICES: ........................................................................................................................ 139 Appendix 1: Literature Review ................................................................................................ 139 Appendix 2: Inclusion criteria and procedures ....................................................................... 141 Appendix 3: Levodopa Equivalence Dose (LED) Table ......................................................... 143 Appendix 4: Gut composite score identification ..................................................................... 144 Appendix 5: Covariate Identification ....................................................................................... 145 Appendix 6: Normality Check ................................................................................................. 148 Appendix 7: Correlation analyses for aims 1-3 ...................................................................... 150 Appendix 8: Assumptions and Outliers .................................................................................. 154 Appendix 9: Informed Speculations ........................................................................................ 156 Appendix 10: Future Research ............................................................................................... 160 x List of Tables Table 2.1 Descriptive Statistics ........................................................................................ 33 Table 2.2 Bacterial taxa involved in metabolic functions shown to be different between PD and HC groups.......................................................... 38 Table 2.3 Bacterial taxa involved in immune functions shown to be different between PD and HC groups.......................................................... 39 Table 2.4 Bacterial taxa involved in neurologic functions shown to be different between PD and HC groups.......................................................... 41 Table 2.5 Bacterial taxa involved in PD Symptomology shown to be different between PD and HC groups. ........................................................ 41 Table 3.1 Multiple linear regression and robust regression models assessing correlations between aerobic fitness and gut diversity....................................................................................... 52 Table 4.1 Linear Regression and Robust Regression between ACE species richness and UPDRS-III scores. ................................................. 65 xi List of Figures Figure 1.1 The 'Brain-First' vs. 'Body-First' Hypotheses of PD Pathology. ........................................................................................... 3 Figure 1.2 Exercise effects both the gut microbiota and brain. ...................................................................................................................... 11 Figure 1.3 Proposed exercise effects on the gut microbiota. .................................................................................................................... 12 Figure 1.4 The Athletic Gut Continuum between people living with clinical conditions and those with optimal health, like athletes. .................................................................................. 13 Figure 2.1 Study Consort, describing the recruitment and enrollment efforts. ............................................................................... 23 Figure 2.2 Taxonomy tree that clusters bacterial markers (known as taxa) into species, genera, families, orders, classes, and phyla. ........................... ................................... 31 Figure 2.3 Operational Taxonomic Units (OTUs) per group, calculated by the observed richness versus total reads per sample. ....................................................................... 32 Figure 2.4 The Rarefaction Curves; the number of sampled reads versus the number of OTUs. ............................................................ 32 Figure 2.5 Gut Composite Score Differences Between Groups- A-C) Species richness scores between PD and HC groups; D-F) Diversity index scores between PD and HC groups; Abbreviations: CI, confidence interval; W, test statistic (p<.05). ...................................................................................................... 34 Figure 2.6 Phylogenetic Differences between groups- Mean relative phylogenetic abundance (%) in PD and HC groups. B) Differences between Actinobacteria and Proteobacteria between PD and HC groups via Multivariate Analysis of Variance analysis (MANOVA; p<.05). .......................................................................................... 35 xii Figure 2.7 Relative differences in bacterial families, classes, genera, and species. .................................................................................... 36 Figure 2.8 Taxonomic Differences Between Groups. ................................................. 37 Figure 3.1 Associations between a-diversity and aerobic fitness status among people with PD. (via robust linear regression; p<0.05) . ........................................................................ 53 Figure 3.2 A species from the Clostridium family was lower in the PD group was negatively correlated with aerobic fitness status (p<0.05). ........................................................................................................... 53 Figure 4.1 Inverse association between species richness and UPDRS-III scores. ................................................................................. 65 Figure 4.2 Clostridium ranosum was negatively correlated with UPDRS-III (p<0.05). ........................................................................... 65 Figure 4.3 Two species, Coprococcus comes and LN913006 sp. were positively correlated with SPPB. .................................................................................................................. 66 Figure 5.1 MoCA sub-domain, Orientation, was positively associated with ACE species richness. (p<0.05). ..................................................................................................................... 80 Figure 5.2 The RBANS sub-domain score, Immediate Memory, was associated with Shannon's Alpha Diversity in PwPD. .......................................................................... 81 Figure 5.3 Five bacterial taxa, one phyla (A), one family (B), one genus (C), and two species (D) were significantly associated with total MoCA scores in PwPD.(p<0.05). ............................................................................................81 Figure 5.4 Two bacterial taxa, the PAC000195 genus, and Blautia Hensenii species, were positively correlated with total RBANS scores in PwPD. ......................................................................................................... 82 xiii Abbreviations ACE Abundance Coverage Estimator AE Aerobic Exercise BBB Blood-Brain Barrier BG Basal Ganglia BMI Body Mass Index CA California CERC Clinical Exercise Research Center ENS Enteric Nervous System FDR False Discovery Rate GBA Gut-Brain Axis GI Gastrointestinal HC Healthy Control IBS Irritable Bowel Syndrome IRB Institutional Review Board KCAL calories KG kilogram KW Kruskal-Willis LDA LEfSe Linear Discriminant Analysis Effect Size LED Levodopa Equivalence Dose M meters MANOVA Multivariate Analysis of Variance MDS Movement Disorders Society MG milligrams MMSE Mini Mental State Exam MoCA Montreal Cognitive Assessment MTP Microbiome Taxonomic Profiling OTU Operational Taxonomic Unit PD Parkinson’s Disease PERMANOVA Permutational Multivariate Analysis of Variance PT Physical Therapy PwPD People with PD RBANS Repeatable Battery of Neuropsychological Status RE Resistance Exercise REM Rapid Eye Movement ROS Reactive Oxidative Species SCFA Short Chained Fatty Acid SD Standard Deviation SD Standard Deviation SNc Substantia Nigra pars compacta SPPB Short Physical Performance Battery UPDRS-III Unified Parkinson’s Disease Rating Scale III US United States UT Utah USC University of Southern California VO2 Volume of Oxygen Consumption xiv Abstract While PD has been thought to be a brain disorder, manifesting as motor and cognitive impairments, recent evidence has transpired to argue PD may be influenced by gut microbiota alterations, or dysbiosis. Along with motor and cognitive symptoms, people with PD (PwPD) experience non-motor symptoms related to gut health (i.e., constipation), and recent evidence has transpired in the last decade to show PwPD suffer from dysbiosis. It has been shown that dysbiosis is linked to PD symptomology, although the evidence that exists to date is sparse. Thus, more evidence is warranted to clarify the exact influence the gut microbiota has on PD symptomology. 2 There is also a need to shift the trajectory of treatments for PD symptoms from only targeting the brain to targeting the gut microbiota. One such treatment strategy that has been shown to improve gut health in non-PD populations and PD symptoms in PwPD is increasing fitness status. Therefore, this dissertation study aimed to explore the impact of fitness status for future potential therapeutic strategies to improve gut health in PwPD, and to clarify the role of the gut microbiota in PD symptomology. Study 1 examined the associations between markers of the gut microbiota and aerobic fitness status in 25 PwPD, after identifying gut microbiota composite scores by comparing PwPD to age-matched non-Parkinsonian individuals. This relationship has been seen in non-PD populations and has provided rationale for exercise as a therapeutic strategy to improve gut health. However, it was critical to identify a relationship between fitness status and gut health, specifically in a PD population, to provide similar rationale for future intervention-based research. In Study 1, a robust linear regression examined the relationship between aerobic fitness status (estimated maximal VO2) and two composite measures of gut health that indicate overall species xv richness and evenness. We demonstrated a positive relationship between aerobic fitness status and both composite measures of gut health. This study was the first of its kind to identify such associations between gut health and fitness status, specifically in a PD population. Based on these findings, future research can investigate the effects of an aerobic exercise intervention, which increases fitness status, to improve gut health in PwPD. Study 2 broadly assessed whether the gut microbiota impacts motor function in PwPD. in an effort to elucidate the role of the gut microbiota on PD symptomology and add to the sparse literature that exists to date. We examined the associations between gut microbiota composite scores and motor function in 25 PwPD, after identifying gut microbiota composite scores by comparing PwPD to age-matched non-Parkinsonian individuals. A robust linear regression showed an inverse relationship between motor symptoms (via Unified Parkinson’s disease Rating Scale III; UPDRS-III) and one gut microbiota composite score that measures species richness. In addition, one bacterial species involved in immune function was also inversely associated with UPDRS-III scores, suggesting that lower levels of bacterial taxa involved in immune regulation are linked to more severe motor symptoms. Further, two bacterial species involved in producing beneficial gut markers necessary for metabolic, immune, and neurologic functions were positively associated with physical function (via Short Physical Performance Battery; SPPB). Based on these findings, it appears that worse motor function is linked to lower levels of key markers indicative of gut health in PwPD. Finally, Study 3 examined whether the gut microbiota impacts cognition in PwPD to further explore the role of the gut microbiota on PD symptomology. The purpose of xvi this analysis was to identify associations between measures of the gut microbiota and global cognition, or total scores of the Montreal Cognitive Assessment (MoCA) and Repeatable Battery Assessment of Neuropsychological Status (RBANS) in 25 PwPD, after identifying gut microbiota composite scores by comparing PwPD to age-matched non-Parkinsonian individuals. While neither measure global cognition was significantly linked to composite measures of the gut microbiota, the orientation sub-score from the MoCA test was positively associated with a gut composite score that indicates species richness, and the visuospatial reasoning sub-score from the RBANS test was inversely associated with one gut composite score that measures species richness and evenness. In addition, several bacterial taxa were linked to total MoCA and RBANS scores. Interestingly, two taxa from similar bacterial families were linked to both motor and cognitive symptoms in our current study. Further, our study revealed that two taxa from similar bacterial families were linked to cognitive function, and these same taxa have previously been linked to motor symptoms in previous investigations in PD populations. Based on the findings from this dissertation, future intervention-based research is justified to assess whether improvements seen in motor and cognitive function from interventions, like exercise, are moderated by changes in the gut microbiota. Importantly, this current dissertation study provides the rationale for such an investigation 1 Chapter 1 Background & Significance Statement of the Problem: Parkinson's disease (PD) has traditionally been considered a brain and central nervous system disorder manifesting as motor and cognitive symptoms. However, it has recently been argued that the gut microbiota may play a role in PD onset, progression, and symptomology via the Gut-Brain Axis (GBA). 3 Along with cardinal motor and cognitive symptoms, gastrointestinal (GI) symptoms and gut alterations (dysbiosis) are also highly prevalent among people with PD (PwPD). 4,5 This evidence points to a rapidly evolving field of study considering the plausibility that PD is not solely a brain disorder but may be influenced by the GBA and dysbiosis. Although the exact role of dysbiosis in PD remains unclear, 6 preliminary evidence has shown links between gut health measures and motor and cognitive symptoms in PwPD. 7 Still, this research is sparse, and more evidence is warranted to elucidate the relationship between gut health and PD symptomology. Further, given the influence of the gut microbiota in PD, it is also crucial to identify potential therapeutic strategies that could alleviate dysbiosis to improve PD symptoms. Traditional, ‘Brain-First’ Etiology of PD: For over two centuries, Parkinson’s disease (PD) has been presumed to develop directly in the brain in a top-down manner, 8,9 known as the ‘Brain-First’ Hypothesis. 2 According to this hypothesis, PD occurs in part from the damage and degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNc) of the basal ganglia (BG) from misfolded -synuclein protein aggregations 9-14 and persistent, uncontrolled 2 neuroinflammation. 15-19 The BG is a group of cortico-striatal structures responsible for motor control, habit formation, learning, and retention. 20,21 In PD, the degeneration of these dopaminergic neurons in the BG leads to motor symptoms such as tremors, rigidity, and bradykinesia. 22-24 These symptoms are commonly used to classify PD severity. 25 Additionally, PwPD suffer from cognitive deficits, such as impaired executive function, attention, and memory. 26,27 The conventional presumption that explains the ‘Brain-First hypothesis’ of PD is that -synuclein aggregations and neuroinflammation originate in the brain from locally- derived toxins and stressors that propagate an unfavorable neurodegenerative cascade. In addition, PD is perpetuated by inflammatory stressors that compromise the Blood- Brain Barrier (BBB). 28 The BBB is a tightly-regulated system comprised of microvasculature and endothelial cells that provide a barrier to protect the brain from harmful constituents. 29 Substantial evidence supports the notion that locally-derived BBB dysfunction impacts PD 30-33 by contributing to neuroinflammation and dopaminergic degeneration. 18,34-37 However, along with brain-derived neurotoxins, the BBB is also affected by systemic inflammation from peripheral organ systems. As such, recent research has suggested that PD may be influenced by stressors that compromise peripheral organ systems connected to the brain. In particular, these peripheral organ systems may be more vulnerable to toxins and, thus, play a more prominent role in the development and progression of PD. 38 One peripheral organ system that interacts with the brain and is more susceptible than the brain to environmental stressors is the gut microbiota. 39,40 3 Disruptions to the Gut-Brain Axis Influence PD (‘Body-First’ Etiology of PD): Despite the proclivity towards a ‘Brain-First’ hypothesis for PD, more recent evidence has transpired to indicate PD may originate peripherally along the gastrointestinal (GI) tract. Some of the first indications that PD may be a ‘Body-First’ disease have been the high prevalence of non-motor symptoms related to the GI tract, like constipation. 41 70% of PwPD suffer from constipation, 41 and GI symptoms often occur prodromal to PD onset. Not only that, people who live with constipation for an average of 20 years have a 2- to 5-fold increased risk of developing PD. 4 Thus, while these symptoms were once thought to be a consequence of having PD, they may actually indicate a cause of PD. The high prevalence of non-motor symptoms in PD was paramount in expanding PD pathology to consider a ‘Body-First’ hypothesis. In contrast to the top-down nature of the ‘Brain-First’ hypothesis of PD, the ‘Body-First’ hypothesis argues that PD onset, progression, and symptomology occur in a bottom-up fashion, from the gut microbiota towards the brain. 9 (see Figure 1) This is due in part to a bi-directional communication system between the gut microbiota and brain, known as the Gut-Brain Figure 1.1 The 'Brain-First' vs. 'Body-First' Hypotheses of PD Pathology. 4 Axis (GBA). The GBA connects the enteric nervous system (ENS) along the GI tract and intestines to the brain via the vagus nerve. 42 The ENS comprises epithelial cells containing enteric neurons that work with the gut microbiota to communicate with the brain to regulate BBB permeability, 43 neurotrophic actions, 44,45 and neurotransmitter signaling. 46 Heiko Braak et al. (2003) first proposed the Body-First etiology, in which he argued that PD was influenced by peripheral alterations within the GI tract that impair GBA communication. 3 Confirming this hypothesis, Braak and others identified PD constituents, like -synuclein, in post-mortem individuals with PD within the ENS, the intermediolateral nucleus in the spinal cord, 47 the olfactory bulb, and the dorsal motor nucleus. 48 This evidence supported Braak’s presumption that -synuclein may form outside of the brain and contribute to PD in a bottom-up manner. 3 From this seminal evidence, Braak developed the Dual-Hit Hypothesis, which states that PD constituents originate peripherally along the 1) olfactory and 2) intestinal portions of the GI tract due to the inflammatory, environmental stressors placed upon the olfactory and intestinal microbiomes and then traverse the GBA to induce neurodegeneration. 3 Since Braak’s initial findings, evidence of -synuclein aggregates along the GI tract has been seen in animal models of prodromal stages of PD 49-51 and humans with PD 52-54 and has been shown to contribute to vagal nerve degradation. 55,56 From this evidence, a staging mechanism has been proposed to classify PD severity based on -synuclein aggregations from the ENS to the lower brainstem (stages 1-2), progressing to the mid- brain to manifest as motor symptoms (stages 3-4), and lastly affecting cortical brain regions to manifest as dementia (stages 5-6). 57,58 5 The ‘Body-First’ hypothesis of PD relies upon the presumption that the GBA is impaired from 1) -synuclein originating along the GI tract and 2) inflammatory constituents formed due to gut microbiota alterations, or dysbiosis. (see Figure 1.1) Both presumptions involve the gut microbiota, a microbial ecosystem that plays a prominent role in GBA functions. It comprises bacteria that produce functional, metabolic byproducts (i.e., short chain fatty acids, lipids, and vitamins) via interacting with ingested substrates (i.e., fiber). 59 In turn, these bacterial constituents interact with a respective host or organ system (i.e., the brain) to carry out immune, 60 metabolic, 61 and neurologic functions. 62 Immunologically, the gut microbiota responds to stressors by facilitating inflammatory processes (i.e., producing immune markers and shuttling out inflammatory products via gut mitochondria). 63 Metabolically, gut bacteria interact with ingested substrates and synthesize metabolites. 64 Short-chain fatty acids (SCFAs) are some the most abundant metabolites and function to maintain the intestinal epithelial lining, regulate immune responses, and facilitate metabolic processes necessary for brain functions. 65-67 Neurologically, the gut microbiota aids in producing precursors to neurotransmitters, including dopamine. Importantly, dopamine precursor bacteria, from the Firmicutes phyla (i.e., Enterococcus and Veillonella) can synthesize the metabolite Tyrosine, a precursor to L-Dopa. 68 As a result, over 50% of dopamine synthesis is made possible by the gut microbiota. 62,69 Collectively, these gut functions explain GBA involvement in PD, via immune regulation, 70-73 metabolism, and neurologic responses, like dopamine availability. 74,75 To facilitate these functions, the gut microbiota must maintain symbiosis, or the homeostatic balance of microbial markers. Gut symbiosis is measured by 1) adequate 6 bacterial diversity, or the proportional number of bacterial phyla present (measured as species richness and/or evenness), 2) sufficient metabolite concentrations, and 3) regulated immune or inflammatory responses. 76 However, when the gut microbiota is dysregulated, referred to as dysbiosis, there is uncontrolled, persistent inflammation that damages the intestinal lining to promote gut permeability. As a result, there is an unfavorable reconstitution of bacteria and, consequently, impaired GBA communication. Dysbiosis can be identified as 1) altered bacterial diversity, including an unfavorable reconstitution of bacteria towards more pathogenic and less commensal bacteria, 2) reduced metabolite concentrations, and 3) an excessive increase in inflammation. 77 Dysbiosis has been seen in PwPD, which may contribute to the disease process. To date, 35 studies have identified dysbiosis in PwPD compared to age-matched non- Parkinsonian individuals. (See Appendix 1) Across these studies, the gut microbial makeup has been shown to be roughly 50-80% different than age-matched non- Parkinsonian individuals. While gut microbiota research has yet to determine a consensus on which individual bacterial markers define dysbiosis, 78 there have been consistent, deleterious differences in bacterial concentrations observed between PwPD and age-matched controls. A recent meta-analysis by Shen et al. (2021) showed that, compared to age-matched controls, PwPD have lower concentrations of Prevotella, Faecalibacterium, and Lachnospiraceae, and higher concentrations of Bifidobacterium, Rumminococcaeae, Verrucomicrobia, and Christensenellaceae. 5 Further, bacteria involved in neurotransmitter synthesis (i.e., dopamine), metabolism (i.e., mitochondrial regulation), and immune function have been shown to be different between PD and non-Parkinsonian populations. 79-90 Lastly, pathogenic bacteria that perpetuate 7 inflammation are higher in PwPD compared to their age-matched counterparts. 80,88,90-92 Still, some inconsistencies have been seen, like increases in bacteria, such as Proteobacteria and Lactobacillus, 79,81,89,92 both of which promote neurotransmitter production, 93 and mixed findings have been seen in bacteria that regulate metabolism, 94 like Akkermansia. 86,95,96 Along with microbial composition, the depletion of key metabolites (i.e., SCFAs) may be a consequence of dysbiosis in PD. 97 SCFAs, including Butyrate, Acetate, and Propionate, are lower in PwPD compared to age-matched controls and have been linked to PD severity. 80,88,98 Other metabolites involved in breaking down the intestinal lining are elevated in PD, increasing endo- and neuro-toxins. 90 Gut inflammation seen in PD 81 has also been attributed to damage to the intestinal lining. 99 Not only that, dysbiosis see in PwPD has involved pro-inflammatory gut markers that can damage the ENS and potentially disrupt the GBA. 98 More recently, evidence has emerged to propose that gut health is linked to PD severity and symptomology, supporting the ‘Body- first’ hypothesis that the gut microbiota plays a role in the disease process. PD severity and disease duration have been attributed to dysbiosis in PwPD. 89,100,101 Regarding symptomology, early evidence in germ-free animal models has revealed motor symptoms to be aggravated by the transplantation of a PD gut microbiota. 102,103 More recently, several studies have identified GI symptoms to be linked to more severe motor impairments in PwPD. 104-110 In addition, eleven studies have shown that motor symptoms are linked to dysbiosis. 79,80,87,89,90,98,100,111-114 However, many of these studies have identified different bacterial taxa to be associated with motor function. For instance, Li et al. (2017) found 8 negative associations between UPDRS-III scores and the Blautia and Faecalibacterium bacterial taxa, while no other study identified these associations. 115 Further, Pietrucci et al. (2019) found positive associations between UPDRS-III scores and the Enterococcus and Lachnospiraceae bacterial taxa, 80 while no other study found these associations. Thus, there is no consensus as to which gut markers impact motor function. Given the inconsistencies seen across these studies, more evidence is warranted to confirm which bacterial markers are associated with motor function. Not only that but only two studies linking motor symptoms to the gut microbiota have been conducted in the United States (U.S.). 79,89 Since the gut microbiota varies significantly across countries, more evidence conducted within the US is needed. Less is known regarding cognitive symptoms and gut health in PD. To date, two studies have shown that cognition is linked to GI symptoms in PwPD, 116,117 and only two studies have linked cognitive symptoms specifically to the gut microbiota. One study in de novo PD patients showed that particular gut bacteria were associated with cognitive impairment (i.e., lower Roseburia and higher Ruminococaceae), 118 and another study revealed that bacterial markers associated with dysbiosis were more prevalent in PwPD who also suffer from mild cognitive impairment (i.e., Rikenellaceae and Butyricimonas). 119 Still, more evidence is needed to elucidate a link between cognitive function and the gut microbiota in PwPD. Given the impact of the gut microbiota on the brain in particular, it is especially critical to acquire more evidence regarding an association between the gut and PD symptomology. Also, it may be necessary to analyze cognition in PwPD using more comprehensive measures of cognitive function aside from the global cognitive scores often used in PD studies (like the Mini-Mental 9 State Exam, MMSE). Not only that, but it is now understood that there is an interplay between motor and cognitive functions within the brain. 120 Thus, it may be beneficial to consider both motor and cognitive symptoms simultaneously when assessing the involvement of the gut microbiota in PwPD. Causality Dilemma- is PD a ‘Brain-First’ or ‘Body-First’ neurodegenerative disorder? While the ‘Body-First’ hypothesis may be a plausible explanation of PD, it is important to note that there have been counterarguments against this ‘Body-First’ hypothesis. 121 In contrast to Braak’s Dual-Hit hypothesis, some individuals with PD do not experience GI symptoms prodromally and, instead, show dysbiosis or GI disruptions later in the disease process. 2 This would support the ‘Brain-First’ hypothesis and suggest gut impairments are a consequence of PD rather than a cause. In contrast, other individuals with PD suffer from symptoms related to the vagus nerve and GBA (i.e., REM sleep disruptions) in the prodromal stages of PD and are more likely also to have dysbiosis-induced -synuclein aggregations present along the GI tract earlier in the disease process. 122,123 This evidence would support the ‘Body-First hypothesis.’ The complexity and heterogeneity of PD preclude a consensus regarding its origin, whether neural damage originates in the brain or in peripheral systems like the gut microbiota. As such, Horesager et al. (2020) has argued that the cause of PD could include both hypotheses and that prodromal, non-motor symptoms may indicate sub-types of PD. 2 Regardless of this causality dilemma, however, the fact that dysbiosis is highly prevalent in PD and has been preliminarily linked to PD symptoms implies that the gut microbiota and GBA play some role in the disease process. Still, the associative evidence seen to date between PD symptomology and gut health that could support this 10 argument is sparse (a lack of emphasis on cognitive symptoms), inconsistent (heterogeneous links between motor symptoms and gut markers), and insufficient (only two studies have been conducted in the U.S.). Thus, more evidence is warranted to clarify the exact influence the gut microbiota has on PD symptomology. There is a need to shift the trajectory of PD treatments from only targeting the brain and central nervous system to also targeting the gut microbiota. Despite this causality dilemma regarding PD and the GBA and insufficient associative evidence between PD symptomology and gut health, it is apparent that the gut microbiota plays a role in PD. Therefore, treatment strategies are needed that target the gut microbiota. To date, PD treatments have focused on improving brain function. However, upon consideration of the GBA, it is possible that the gut microbiota may moderate the improvements in PD symptomology seen from these long-standing treatments. From a ‘Brain-First’ perspective, pharmacological PD treatments have been utilized that facilitate dopamine availability in the brain to improve PD outcomes. (i.e., PD medications) 14,124-126 Non-pharmacological interventions have also been implemented in PD populations to target the brain, like exercise. 127 Both cognitive and motor symptoms have been improved via aerobic (AE) and resistance exercise (RE). 128- 130 In fact, work from Fisher et al. (2008 and 2013) has played a pivotal role in this research, identifying that intensive AE in PwPD can improve motor and cognitive symptoms via neuroplastic mechanisms. 131,132 In addition, substantial exercise research exists in PwPD that has shown interventions that require processing in the brain via skill acquisition (i.e., dance, tai chi, and balance training) improve motor function and cognition. 133,134 11 In an interest to identify the ‘key ingredient’ for exercise-induced improvements observed in individuals with PD, Petzinger and Fisher (2013) put forward the idea that motor and cognitive improvements from exercise are due to a skill acquisition process that presumably increases neuroplasticity (see Figure 1.2). 135 The assumption is that these forms of exercise elicit neurophysiologic modifications directly in the brain. 131,136 However, it is important to note that this assumption does not account for the exercise-induced peripheral, physiologic restorations that occur with exercise and how these physiologic effects influence neurodegeneration in PD. Thus, it’s highly plausible that exercise benefits for PD go beyond skill acquisition. This is because exercise imposes a sheer volume and load that induces the physiologic restoration of systems, particularly those pertaining to the GBA, including skeletal muscle and aerobic respiration, that are often dysregulated in PD. 137,138 As such, AE and RE also elicit a beneficial physiologic response in PD. Overall, it appears that exercise improves neurophysiological processes by increasing neurotrophic factors, improving cerebral blood flow, and attenuating neuroinflammation. 130,133,139 However, upon considering the probable role of the gut microbiota in the disease process of PD, it is essential to also understand treatments that can improve gut health in PwPD. Fortunately, an exciting and important overlap Figure 1.2 Exercise effects both the gut microbiota and brain. 12 exists between PD research and gut microbiota research that can (and should) be considered: not only does exercise improve PD symptomology from a brain perspective, but it has also been shown to improve the gut microbiota in non-PD populations. (See Figure 1.2) Distinct from PD, exercise is a non- pharmacological strategy shown to restore the gut microbiota. Motivated by seminal research by Clark et al. (2014), a higher physical fitness status (i.e., submaximal VO2) is associated with higher gut diversity. 140 Similarly, correlative evidence shows that people who are more physically fit (i.e., athletes) have a different, arguably better, gut microbiota composition than sedentary individuals (See Appendix 1) 140-147 For instance, compared to sedentary individuals, athletes have been shown to have higher gut diversity, markedly higher concentrations of bacteria involved in supporting metabolism (i.e., Verrucomicrobia), 141,148 and greater metabolite production (i.e., Butyrate). 141,145 However, some studies have not shown differences in the gut microbiota between athletes and sedentary individuals. 149 Even still, these elevated markers of gut health can be attributed to the fact that athletes exercise more frequently and thus require greater metabolic demand from the gut microbiota, resulting in increased beneficial bacteria and gut metabolites necessary for energy production. (See Figure 1.3 Proposed exercise effects on the gut microbiota. 13 Figure 1.3) In addition to the proposition of the athletic gut model, numerous studies have linked physical fitness status to better gut health in non-PD populations (see Appendix 1). Thus, if exercise can be implemented in a PD population that improves physical fitness, gut health can also be improved to mimic that of an athlete’s gut microbiota, eliciting a shift closer to the ‘athletic gut’ on the gut health continuum. (See Figure 1.4) It is important to note that excessive exercise often performed by high- caliber athletes (i.e., ultra-marathon runners) may elicit adverse changes to the gut microbiota acutely. 150,151 However, these acute deleterious changes may lead to long- term benefits to the gut microbiota, like an ability to adapt to environmental stressors better than sedentary individuals. 152 Notably, many gut markers linked to fitness status are the same gut markers dysregulated in PD. (See Appendix 1) Gut diversity, which is reduced in PD, 92,96,153 has been positively associated with physical fitness status (sub- maximal VO2) in healthy young adults, 154-157 older adults, 158 type I diabetics, 159 and breast cancer survivors. 160,161 Moreover, gut bacteria that have consistently been Figure 1.4 The Athletic Gut Continuum between people living with clinical conditions and those with optimal health, like athletes. 14 shown to be reduced in PD, like Prevotella, 82,84-88 are positively associated with VO2. 160 In addition, metabolites that are often reduced in PD, 89,162 have also been positively associated with VO2 in non-PD populations. 141 This preliminary, observational evidence has motivated a decade’s worth of intervention-based exercise research aiming to restore the gut microbiota. Overall, it appears that AE has the potential to improve gut diversity, 11,163-168 increase commensal bacteria 163,169,170 and metabolites, 165,171-178 and regulate inflammation. 169,179-182 Thus, exercise can provide a stable, robust stimulus to restore similar dysregulations to the gut microbiota often seen in PD. In response to exercise, the gut microbiota produces and utilizes microbial by-products which restore immune, neurologic, and metabolic function. 183 Given that these processes are governed by the cardiovascular and musculoskeletal systems, which can be significantly modified by exercise, exercise likely restores the gut microbiota and improves GBA function. (See Figure 1.3) Therefore, this begs the question- are the benefits to PD symptomology from exercise moderated by exercise-induced improvements to the gut microbiota? Gaps in Knowledge & Overall Aims Overall, this dissertation challenges the top-down approach to PD treatments and shifts the trajectory of PD research toward the gut microbiota. The GBA and Dual-Hit hypothesis is a compelling gateway to better-understand PD pathology and substantiates the rationale for more effective treatment strategies that target both the brain and the gut microbiota. However, this area of research is in its infancy. To date, evidence has provided three key ‘puzzle pieces’ that this dissertation aims to connect: 1) the gut microbiota is likely a large influencer of PD, 34,35 2) there is sparse research 15 linking PD symptomology to gut health, and 3) improving fitness status may be an effective strategy to target the GBA in PD, given that PD research shows exercise which improves fitness status ameliorates brain outcomes, and non-PD research shows exercise which increases fitness status improves the gut. 138,184,185 However, critical preliminary steps need to be taken in this line of research in order to put these puzzle pieces together. Overall, this dissertation aims to take these critically foundational steps. Specifically, this dissertation aims to explore the role of the gut in PD symptomology and the impact of fitness status on gut health in PD. In doing so, the findings from this dissertation can provide a framework for future exercise intervention studies to alleviate gut dysbiosis in PwPD as an effort to enhance fitness levels and improve PD symptomology. Specific Aims The purpose of this dissertation was to analyze the following aims in 25 people with Parkinson’s disease (PwPD; Hoehn and Yahr stages I-III): AIM 1: Examine the association between aerobic fitness (estimated maximal VO2 via 6-minute walk test) and two of six previously-identified gut microbiota composite measures that assess species richness and species evenness. • Hypothesis 1. Based upon non-PD research that has shown a positive relationship between various measures of aerobic fitness and gut diversity (see Appendix 1), 140,141,144,155,158,186-189 estimated maximal VO2 will be positively associated with gut microbiota composite measures, particularly gut diversity. 16 AIM 2: Examine the association between motor symptoms (Unified Parkinson’s disease Rating Scale-III; UPDRS-III) and one of six previously-identified gut microbiota composite measures that assess species richness. • Hypothesis 2. Since sparse evidence in PD has shown links between lower levels of individual bacteria and more severe motor impairments in PwPD (See Appendix 1), 80,98,101,1144 UPDRS-III will be negatively associated with the gut microbiota composite measures. AIM 3a: Examine the association between cognitive symptoms (Montreal Cognitive Assessment; MoCA, and Repeatable Battery Assessment of Neuropsychological Status; RBANS) and two of six gut microbiota previously identified composite measures that assess species richness and/or species evenness. • Hypothesis 3a. Since sparse evidence in PD has shown links between lower levels of individual bacteria and worse cognitive function in PwPD (see Appendix 1), 118,119 total MoCA and RBANS scores will be positively associated with the gut microbiota. AIM 3b (Exploratory Analysis): As an exploratory aim, examine the association between two of six previously-identified gut microbiota composite measures that assess species richness and/or evenness and sub-domains of total cognitive score from the MoCA and RBANS tests. • Hypothesis 3b. Given that PwPD suffer from immediate memory, visuospatial reasoning, and attention deficits, 190 and that these cognitive sub-domains that have been attributed to GI symptoms in PD, 116,191,192 and the gut microbiota in other neurodegenerative disorders, 193 the MoCA and RBANS sub-scores, immediate 17 memory, visuospatial reasoning, and attention, will be positively associated with the gut microbiota. IMPACT: This dissertation was the first study of its kind to consider the role of fitness status on gut health in PwPD and adds to the sparse evidence indicating the influence of the gut microbiota on symptomology. The findings from this dissertation will elucidate the impact of the gut microbiota on PD and can inform future intervention-based research to improve fitness in PwPD and PD symptomology via moderating the gut microbiota. 18 Chapter 2 Differences in the gut microbiota between people with Parkinson’s disease and age-matched non-Parkinsonian individuals ABSTRACT: Introduction: It is clear that the gut microbiota plays a role in Parkinson’s disease (PD). Thus, this dissertation aims to consider therapeutic strategies that can improve gut health in people with PD (PwPD) in an effort to improve symptomology. However, before Aims 1-3 are assessed, it is first necessary to identify gut markers of interest based on our particular cohort of PwPD to be used as outcome measures for each aim. While it is possible to reference previous studies to identify these gut markers, there are limitations to this approach, including the heterogeneous nature of the gut microbiota. Therefore, the purpose of this preliminary analysis was to 1) identify the most relevant gut markers to use in the primary analyses for each dissertation aim (e.g., gut composite scores) and, for the exploratory correlation analyses for each aim, 2) identify individual bacterial taxa that are significantly different in the current study cohort of PwPD compared to non-Parkinsonian individuals. Methods: We collected fecal samples from 25 PwPD (Hoehn and Yahr I-III) and 21 age-matched controls. The fecal samples were tested by EZBiome (Gaithersburg, MD) using their proprietary technique called 16s Microbiome Taxonomic Profiling (MTP) rRNA amplicon sequencing. MTP rRNA amplicon sequencing identified operational taxonomic unit (OTU) reads of bacterial markers for each participant. Bioinformatics were then conducted to identify the most appropriate gut composite scores to be used in the subsequent analyses for aims 1-3. Gut composite scores were identified that measure species richness (Abundance Coverage Estimator; ACE, Jackknife, and 19 Chao1) and both species richness and evenness (Shannon’s -diversity, Simpson’s - diversity, and Phylogenetic Diversity). To compare each of the gut composite scores between groups, a multivariate analysis of variance with 999 permutations (PERMANOVA) was conducted. Eight a priori confounding variables (e.g., age, levodopa equivalence dose, and diet) were tested against 1) the gut composite scores identified for each aim and 2) each dissertation aim outcome measure (Aim 1. Aerobic fitness; Aim 2. Motor symptoms; and Aim 3. Cognitive symptoms). The covariates identified from these two tests were included in the statistical models for each aim. In addition, taxonomic profiling was performed to identify group differences at the phylum, class, order, family, genera, and species bacterial levels (see Figure 2.2). A non- parametric factorial Kruskal-Wallis (KW) sum rank test and Wilcoxon Rank-Sum test were employed to reduce the number of taxa identified to only those that were statistically different between groups and clinically relevant taxa. Then, a t Linear Discriminant Analysis (LDA) effect size (LEfSe) method was used to compare group differences, ranking the most significant differences by an effect size. Finally, the Benjamini and Hochberg false discovery rate (FDR) post hoc analysis was employed to adjust for multiple comparisons. Taxa with an FDR of over two were included. If a taxon was absent in at least 70% of the participants, the taxon was omitted. Results: Six gut composite scores were identified in our cohort, three scores that measure species richness (Abundance Coverage Estimator; ACE, Jackknife, and Chao1) and three that measure species richness and evenness (Shannon’s -diversity, Simpson’s -diversity, and Phylogenetic Diversity). Shannon’s -diversity, Simpson’s - diversity, Phylogenetic Diversity, Chao1, Jackknife, and ACE's were not different 20 between groups. Of the five most dominant phyla identified between groups, Actinobacteria was significantly higher (p=0.039) and Proteobacteria significantly lower in the PD group (p=0.04). In addition, one order, one class, three families, 12 genera, and 23 species were significantly different between groups. In the PD group, bacterial families that inhibit dopamine synthesis were higher, multiple genera previously associated with PD symptoms were higher, and various species involved in metabolism and inflammation were altered compared to the age-matched control group. Conclusion: These findings will be used to investigate the role of gut health on fitness status (aim 1, chapter 3), motor symptoms (aim 2, chapter 4), and cognitive symptoms (aim 3, chapter 5) in PwPD. 21 INTRODUCTION: As established in Chapter 1, the gut microbiota plays a role in Parkinson’s disease (PD), thus, treatment strategies that target the gut are warranted. 3 This cross- sectional research study will be the first of its kind to assess the role that fitness status plays on gut health in a mediating effort to improve PD symptomology. As a critical first step, fitness status, motor function, and cognition will be associated with gut bacterial markers of interest in a PD population. However, in order to execute Aims 1-3, it is first necessary to identify particular bacterial markers of interest within the current study cohort to use in the statistical model for each aim. While it is possible to reference previous gut microbiota studies in PD to identify gut markers to use in this current analysis, this approach has three limitations. First, the gut microbiota is exceedingly heterogeneous from one sample and study population to another. 194 Dietary habits, age, biological sex, environmental exposures, and region of residence (i.e., country or state) all significantly confound the gut microbiota. 195,196 Thus, one limitation to using previously-published evidence would be that the bacterial markers shown to be significant in one population may not be relevant for our current study cohort. A second limitation that this analysis will address is that it is difficult to interpret the vast number of bacterial markers in the gut microbiota, which often ranges between 1,000-2,000 taxa. In the gut microbiota research space, the most common approach to address this limitation is to use a gut composite score that represents the overall gut bacterial composition and landscape, such as -diversity. 197 Thus, gut composite scores will be calculated to be used in all statistical models in this dissertation to address this 22 limitation. It is important to note that, to obtain these composite scores, an age-matched non-Parkinsonian group is needed to compare with the PD group. The third limitation that this chapter will address is the fact that, although gut composite scores, like -diversity, are widely utilized and considered a reliable measure of gut health, these composite scores do not represent the individual functions of each particular bacterial taxa. Instead, these composite scores compress all bacterial marker concentrations, so if one bacterial marker is under-abundant while another is over- abundant, the gut composite scores will misrepresent those specific bacterial alterations. 198 As such, individual bacterial markers still need to be considered. As an exploratory associative analysis, a more sophisticated statistical technique will also be employed to address this third limitation and reduce the number of bacterial markers to be used. To account for these limitations and control for the heterogeneous nature of the gut microbiota, the purposes of this preliminary analysis were to 1) compare the gut microbiota between our particular study cohort of PwPD (PD group) and an age- matched control group (HC group), 2) identify the most relevant gut composite scores in our cohort to indicate gut health to use in Aims 1-3, and 3) employ a statistical reduction technique to identify specific bacterial markers that are significantly different between groups and may be associated with each dissertation aim (1. Aerobic fitness, 2. Motor symptoms, and 3. Cognitive symptoms). 23 METHODS: Study Participant Recruitment and Enrollment: The study was approved by the University of Southern California (USC) Institutional Review Board (study IRB # UP-20- 1394). Participants were recruited from 1) Parkinson’s disease support groups in greater Los Angeles and Orange Counties; 2) physical therapy clinics in Southern California that specialize in the treatment of individuals with PD (Rogue Physical Therapy, Re+Active Physical Therapy, or Casa Colina Physical Therapy) and 3) the Movement Disorder Center at Keck Medicine, USC (Los Angeles, CA). When possible, spouses or partners of the participants with PD were recruited to control for dietary and lifestyle confounders of the gut microbiota. Age-matched controls were recruited if a participant with PD did not have a partner or spouse. All participants completed an initial screening based on the inclusion criteria (see Appendix 2). Once enrolled, participants completed an informed consent. Initially, 32 PwPD and 26 age- matched control participants were enrolled (see Figure 2.1). 6 PwPD and 3 HC did not submit stool samples and, thus, were excluded from the study. 1 PwPD was omitted from the study because they were a consistent outlier on all statistics, potentially due to their age being 3.9 standard deviations lower than the group mean (age 42 versus Figure 2.1 Study Consort, describing the recruitment and enrollment efforts. 24 group mean 69.9 ±7.06 SD). In total, 25 PD and 21 HC (9 spouses) were included in all analyses. Participant demographics can be found in Table 2.1. Study Procedures: All PwPD were on PD-related medication at the time of stool sample collection and all testing procedures (see Appendix 2). The average time between taking regular PD medication and the testing was 90 minutes, and Levodopa Equivalent Dose (LED) was calculated (see Appendix 3). Participants underwent all tests either in the Clinical Exercise Research Center (CERC, CHP 149) at USC or one of the collaborating off-site physical therapy clinics (Rogue PT, Re+Active PT, or Casa Colina). Before their testing appointment, participants were encouraged to drink water and take their medication as recommended by their doctor. The laboratory tests included a walking test (6-minute walk test), two motor function tests (Unified Parkinson’s disease Rating Scale III for the PwPD group only, and the Short Physical Performance Battery), and two cognitive tests (The Montreal Cognitive Assessment and the Repeatable Battery of Neuropsychological Status). All tests will be detailed in chapters 3-5. Once completed, each participant was sent home with a stool sample collection kit and questionnaires about their dietary habits (7-day dietary recall), physical activity (International Physical Activity Questionnaire), and digestion (ROME-IV Digestion Survey). They were given 14 days to complete all testing materials. Stool Sample Collection & Storage: Stool samples were collected within 14 days of initial testing by the participant in their home (unsupervised) using the DNA Genotek OMNIgene collection kit (Ontario, Canada), including specific directions for stool sample collection. Each 0.25 mL collection tube contained 200 µL stabilizing liquid. Participants were instructed to add approximately 50mg of feces, close the tube, and shake until the 25 feces were mixed with the stabilizing liquid. Once collected, participants were instructed to place the collection tube into a biohazard bag within a shipping box and keep it stored at room temperature until obtained from the research staff. Samples were received by the research staff from the participant within 24 hours of stool collection and then stored in a -80  C freezer. Once all samples were collected, they were shipped to EZBiome (Gaithersburg, MD) to be sequenced and analyzed. 16s rRNA MTP Sequencing: 16S rRNA amplicon sequencing and bioinformatics were conducted by EZBiome (Gaithersburg, MD). V3-V4 hypervariable region paired- end amplicon sequencing was performed. Operational taxonomic units (OTUs) were then obtained, clustered by weight, and compared to identical taxa from the pre-existing reference library from EZBiome. Next, Microbiome Taxonomic Profiling (MTP) method was employed. The MTP method is a cost-effective, statistically valid equivalent to a metagenomic, traditionally more comprehensive approach. MTPs are generated from next generation sequencing raw data using the EZBioBloud MTP pipeline and EZBioCloud 16S database. All these data were uploaded and integrated into the database, EzBioCloud® 199 via the CL_OPEN_REF_UCLUST MC2 method, which references the Human Microbiome Project to classify bacteria. 200 Once OTUs were obtained (see Figure 2.3), rarefaction curves were calculated to normalize species diversity by plotting the correlation between the size of the sample data and the number of OTUs (see Figure 2.4). This process reveals whether each sample has a sufficient number of OTU reads. If the curve is too shallow, it indicates the sample does not have adequate reads and must be omitted from statistical analyses. 201 Bacterial reads that fell above a pre-prescribed cutoff of 5,000 reads were compiled. 26 Two samples from the HC group were omitted from the analysis based upon the cutoff. This strategy is called ‘Open-reference OTU picking’. 202-204 Measurements of Potential Covariates of the Gut Microbiota: Eight variables were interrogated as potential covariates of the gut microbiota, selected A Priori based on 35 previous studies comparing the gut microbiota between PwPD and age-matched controls, those that impact PD symptomology (motor and cognitive function), and those that affect aerobic fitness status (see Appendix 1). Those that were significantly correlated to measures of the gut microbiota and the outcome measures for each aim included age, years of PD diagnosis, Levodopa equivalent dose (LED), dietary fat, carbohydrate, and protein consumption (% of daily caloric intake over an average 5-day dietary recall) 205 , body max index (BMI, kg/m 2 ), and constipation severity, (ROME-IV Survey). Age & Years of PD Diagnosis: All participants were asked about their sex and age at the time of study enrollment. In addition, the PD participants were asked to indicate the date at which their primary PD diagnosis occurred (reported in years). Levodopa Equivalence Dose (LED): Current medication status was indicated by PD participants (see Appendix 3). LED was defined as the PD medication type and dosage intake at the time of study participation. This information was entered into an LED calculator to determine the relative levodopa exposure. 206 All participants were on medication at the time of testing (the average time since their medication was taken was 90 minutes). Dietary Habits: Participants were asked to complete at least five days of a food log, known as the 7-Day Diet Recall survey. This test has been validated in older adult 27 populations as a stable, comprehensive measurement of dietary patterns. 205 The participant stated their daily food intake in detail over the five to seven-day period before collecting the stool sample. Dietary information for each participant was then inputted into a computer database (My Fitness Pal), and the proportional (%) amounts of carbohydrates, fats, and proteins intake were calculated as follows: Body Mass Index: Body weight was assessed via a bioelectrical impedance device (InBody 570, Cerritos, CA) if the participant tested in the laboratory at USC (n=13) or an electronic scale if the participant tested offsite (n=12). The two different devices have been shown to be standardized to one another. 207 Participants were asked to remove their shoes and socks and stand on the device and body weight was recorded (kilograms, kg). Height was measured using a stadiometer, in which the participant was asked to stand facing away from the monitor while the research assistant measured their height in inches. BMI was measured by calculating BMI (kg/m 2 ) = weight (kg)/ height (m 2 ). Constipation Severity: Participants were asked to complete a questionnaire to assess their constipation status, called the ROME IV Diagnostic Criteria. 208 The ROME IV Diagnostic Criteria is a subjective examination of gastrointestinal symptoms that has been validated in the PD population. 209 It includes 89 questions pertaining to all possible gastrointestinal symptoms a person may experience. Nine questions related to constipation, including having less than three bowel movements per week; the Bristol % fat = avg. fat intake (grams) * 9 kcal/total avg. kcal intake % carb = avg. carb intake (grams) * 4 kcal/total avg. kcal intake % protein = avg. protein intake (grams) * 4 kcal/total avg. kcal intake 28 Stool Chart to assess the quality of stool (i.e., hard or soft); incomplete defecation; obstruction/blocks; straining; and the need to use laxatives for at least six months. Constipation severity was assessed by summing the total score from each of the nine questions related to constipation (total score out of 90 points). STATISTICS: Bioinformatics: The data obtained from the rarefaction OTU analysis was used to measure species richness and species evenness. In total, six gut composite scores were identified, three scores related to species richness only and three scores related to species richness and evenness. Species Richness: To measure species richness, three techniques were considered: 1) Abundance Coverage Estimator (ACE), 2) Chao1, and 3) Jackknife. ACE is a non-parametric richness index that uses sample coverage based upon the sum of the frequency probabilities of observed species. 210 The formula for ACE is as follows: Chao1 is an estimator of species richness that considers the total number of species present based on the number of singletons, or species that are only observed once, and doubletons, or species that are observed twice. 211 Chao1 was calculated using the following formula: Abundance Coverage Estimator (ACE)= Sobs + ((Fa^2)/ (2 * Fb)) Sobs= number of species observed Fa= number of species observed only one time in a sample. Fb= number of species observed exactly two times in a sample. 29 Jackknife is an indicator of species richness (total number of species in a sample) that is sensitive to rare OTUs (singletons and doubletons) as well as abundant OTUs (tripletons and more). Higher values indicate higher diversity. 212 Species Evenness & Richness (Diversity): Three indices were used to measure species evenness and richness: 1) Shannon -diversity, 2) Simpson -diversity, and 3) Phylogenetic Diversity. Shannon’s -diversity measures the proportional distribution of the number of each species in a sample that exhibits values greater than 0 and typically captures random or uncommon species types. 213 The formula to calculate Shannon’s diversity Index is as follows: Simpson’s -diversity measures the proportional distribution of the number of each species within the sample by identifying the probabilities that two randomly selected sequences are the same species, with values ranging from 0 to 1. 213 Simpson’s - diversity Index typically measures the dominance index, which refers to the bacterial Chao1= Sobs + ((n1^2)/ (2 * n1)) Sobs= number of species observed n1 is the number of species observed only once (singletons). n2 is the number of species observed twice (doubletons). Jackknife = ((n-1) / n) * Σ((θ_i - θ)^2) n is the total number of data points or observations. θ_i is the estimator calculated using all data except the i-th observation. θ is the estimator calculated using the entire dataset. Shannon’s Diversity Index = -∑(pi * ln pi) pi = the proportion of individuals belonging to the ith species ln = natural logarithm 30 species that are most prominent. The formula for Simpson’s Diversity Index is as follows: Phylogenetic Diversity measures the proportional distribution of each species within the sample by creating statistical hierarchical trees, or clusters, based on similar groups of species. The branch length refers to the distance, or the difference in species type between each group. Once these species clusters are obtained, the sum of the branch lengths connecting a sequence is calculated. This calculation indicates the diversity of the sample. The formula for Phylogenetic Diversity is as follows: A permutational multivariate analysis of variance (PERMANOVA) was employed for each model, with 999 permutations to compare differences between group diversity indices. Taxonomic Differences: Bacterial taxa were also assessed at each taxonomic level, which is represented in Figure 2. At the phyla level, all identified phylum that comprised more than 3% of the total relative bacterial abundance were included in a multivariate analysis of variance (MANOVA) to determine significant differences between PD and HC groups. Simpson’s Diversity Index = 1 - ∑(ni/ N)^2 ni = the number of individuals belonging to the ith species N = the total number of individuals in the community Phylogenetic Diversity = Σ (1 - d(i)) Σ = the sum 1-d= the unique difference between each branch (i)= each branch (will depend on sample) 31 Metagenomic biomarker discovery was utilized to identify significant differences in bacterial taxonomic ranks. First, the non-parametric factorial Kruskal-Wallis (KW) sum rank test and Wilcoxon rank- sum test were performed to identify initial significant differences between groups. Then, the Linear Discriminant Analysis effect size (LDA-EfSe) method was employed to compare group differences. LEfSe supports high- dimensional class comparisons and determines features most likely to explain differences between taxa by grouping statistically significant differences. Adding an additional test ensures biological consistency and effect by ranking the clinical relevance of different bacterial differences (LEfSe). 214 The Benjamini and Hochberg false discovery rate (FDR) posthoc analysis was employed to adjust for multiple comparisons. All significant bacterial taxa differences with an FDR below 2 were omitted. In addition, if a bacterial taxon was absent in ≥70% of the participants, it was also omitted. Covariate Identification: For each aim, two best subset selection models were employed to identify covariates from the eight that were measured: 1) to determine the covariate(s) for the gut composite score(s) used in the regression model for each aim and 2) to determine the covariate(s) for the primary outcome measure for each aim Figure 2.2 Taxonomy tree that clusters bacterial markers (known as taxa) into species, genera, families, orders, classes, and phyla. 32 (fitness, motor function, and cognition). Appendix 5 displays the results for covariates via each best subset selection model. RESULTS: OTUs and Rarefaction Curves: Figure 3 details the analysis to obtain OTUs, based on average reads versus observed richness for each group. The average reads per sample for the HC group was 21,021 (minimum = 7,990 and maximum= 31,570) and 18,304 for the PD group (minimum = 5,367 and maximum= 32,336). The 16s rRNA amplicon data contained an average of 314.04 OTUs in the PD group and 333.1 in the HC group. (p=0.15). Figure 4 displays the rarefaction curve for each participant, which indicates the amount of OTUs that were able to be detected for each participant, based upon the quality of the sample provided. If a curve was more concave, it indicated a more suitable read, meaning a sufficient number of OTUs present to Figure 2.4 Operational Taxonomic Units (OTUs) per group, calculated by the observed richness versus total reads per sample. Figure 2.3 The Rarefaction Curves; the number of sampled reads versus the number of OTUs. 33 properly analyze the sample’s bacterial content. If the sample curve was less concave, then the OTUs Table 2.1 Descriptive Statistics obtained from that participant was not sufficient and needed to be omitted from further statistical analyses. Demographics: Descriptive statistics for both groups are detailed in Table 2.1. Abbreviations- PD, Parkinson's disease; HC, Healthy Control; SD, Standard Deviation; UPDRS-III, Unified Parkinson's Rating Scale 3 BMI, Body Mass Index; IBS, Irritable Bowel Symptoms; VO2, oxygen consumption; MoCA, Montreal Cognitive Assessment; RBANS, Repeatable Battery of Neuropsychological Status 34 Covariate Identification: The identification of covariates via the best subset selection technique for each aim and are detailed in Appendix 5. For aim 1, fat and years of diagnosis were identified as covariates to the gut composite scores identified (Shannon and Simpson -Diversity) and age and BMI (kg/m 2 ) were identified as covariates for aerobic fitness status. For aim 2, dietary fat (%) was identified as a covariate for the gut composite score identified (ACE), and levodopa equivalence dose (LED) and age were identified as covariates for motor function. For aim 3, dietary fat (%) was identified as a covariate for the gut composite scores utilized (ACE and Figure 2.5 Gut Composite Score Differences Between Groups- A-C) Species richness scores between PD and HC groups; D-F) Diversity index scores between PD and HC groups; Abbreviations: CI, confidence interval; W, test statistic (p<.05). 35 phylogenetic diversity), and age and BMI (kg/m2) were identified as covariates for cognitive function. Species Richness only: Figures 2.5a-2.5c show differences in the two -diversity indices, which indicates species richness and evenness. There were no significant differences in Jackknife (p=0.64), ACE (p=0.29), or Chao 1 (p=0.63). between the HC and PD groups. Species Richness and Evenness (Diversity): Figures 2.5d-2.5f show differences in diversity indices, which indicates species richness and evenness. There were no significant differences in Shannon’s -Diversity (p = 0.45), Phylogenetic Diversity (p=0.59), or Simpson’s -Diversity (p= 0.57) between HC and PD groups. Taxonomic Differences: In total, 11 phyla, 43 orders, 25 classes, 88 families, 484 genera, and 1,689 species were detected across both groups. The number of taxa was then reduced based on whether they were significant between groups and withstood the statistical criteria (LDA-EfSe and posthoc A) B) Figure 2.6 Phylogenetic Differences between groups- Mean relative phylogenetic abundance (%) in PD and HC groups. B) Differences between Actinobacteria and Proteobacteria between PD and HC groups via Multivariate Analysis of Variance analysis (MANOVA; p<.05). 36 FDR) detailed in the previous section, Statistics. The remaining taxa are described below. Phyla differences: Five phyla were most abundant across the PD and HC group, while phyla that represented <3% of the total relative abundance were grouped as ‘Unclassified’ (see Figure 2.6a). The most abundant phyla were Firmicutes, Bacteroidetes, Actinobacteria, Verrucomicrobia, and Proteobacteria. Of these phyla, Figure 6b shows that Actinobacteria was significantly more abundant in the PD group (1309.4 1679.9; p= 0.39) than the HC group (499.8  484.2), and Proteobacteria was Figure 2.7 Relative differences in bacterial families, classes, genera, and species. 37 significantly lower in the PD (368.5  316.6; p= 0.04) versus the HC group (647.4  769.9). Class, order, family, genus, and species differences: 42 taxa were shown to be different between PD and HC groups: one class, one order, three families, 13 genera, and 24 species (detailed in Figures 2.7 and 2.8). These taxa were from the Firmicutes, Actinobacteria, Proteobacteria, Bacteroides, and Verrucomicrobia phylum. While, in general, bacterial taxa are responsible for various functions, the functions of these 42 taxa most relevant to PD are highlighted below. As the gut microbiota plays a significant role in metabolic, immune, and neurologic functions, the following discussion will highlight each of these roles. From a metabolic standpoint, bacteria (at the genus a species levels) that produce key Figure 8: Taxonomic differences between PD and HC groups, based on Kruskal-Willis and LDA tests. (LDA ≥2) Figure 2.8 Taxonomic Differences Between Groups. 38 metabolites are highlighted in Table 2.2. Notably, short-chain fatty acids (SCFAs) are some of the most abundant metabolites and play a critical role in metabolic functions. Metabolites are produced by bacteria interacting with ingested substrates. They are responsible for facilitating metabolism, supporting mitochondrial quality and function, maintaining the epithelial lining of the intestine, and even promoting immune and neurologic functions. 215-218 Typically, the higher the levels of metabolites, the better these functions are able to be carried out. Thus, the bacteria which play a role in SCFA production and were shown to be significantly different between our PD and HC groups are highlighted below (see Figure 2.8, which demonstrates statistical differences in bacterial genus and species between groups): Table 2.2 Bacterial taxa involved in metabolic functions shown to be different between PD and HC groups. METABOLIC FUNCTIONS Higher in PD // Lower in PD Function (a summary of our data) Phyla Class Order Family Genus Species Firmicutes Clostridia Clostridiales Lachnospiraceae Longicatena Produces SCFAs 219,220 (7 taxa were higher in our PD group.) Firmicutes Clostridia Clostridiales Lachnospiraceae Adlercreutzia Firmicutes Clostridia Clostridiales Ruminococcaceae PAC001168 Firmicutes Clostridia Clostridiales Ruminococcaceae Pseudoflavonifractor LN869527 Firmicutes Clostridia Clostridiales Lachnospiraceae Blautia Blautia hansenii Actinobacteria Coriobacteriia Coriobacteriales Coriobacteriaceae Adlrecreutiza Adlercreutzia equolifaciens Verrucomicrobia Verrucomicrobiae Verrucomicrobiales Akkermansiaceae Akkermansia KQ968618 Firmicutes Clostridia Clostridiales Lachnospiraceae PAC000195 Produces SCFAs 219,220 (10 taxa were lower in our PD group) Firmicutes Clostridia Clostridiales Lachnospiraceae FTRU Firmicutes Clostridia Clostridiales Lachnospiraceae Blautia LN913006 Firmicutes Clostridia Clostridiales Lachnospiraceae PAC000195 PAC000195 Firmicutes Clostridia Clostridiales Lachnospiraceae PAC000692 PAC001229 Firmicutes Clostridia Clostridiales Ruminococcaceae FTRU PAC001607 Firmicutes Clostridia Clostridiales Ruminococcaceae Oscillibacter PAC001469 Firmicutes Clostridia Clostridiales Ruminococcaceae Pseudoflavonifractor PAC002119 Firmicutes Clostridia Clostridiales Ruminococcaceae Sporobacter PAC000739s Bacteroidetes Bacteroidia Bacteroidales Odoribacteraceae Butyricimonas Butyricimonas facihominis Actinobacteria Coriobacteriia Coriobacteriales Coriobacteriaceae Lactonifactor (+) associated with metabolic dysfunction 221 Seven taxa that produce SCFAs were shown to be higher in our PD group, whereas ten were shown to be lower in our PD group. These reductions are consistent with what 39 has been seen in previous PD and gut studies, 219,222 and may indicate a reduction in SCFAs. In particular, one SCFA-producing, anti-inflammatory species was lower in our PD group, 223,224 Butyricimonas facihomini (see Figure 2.8). This species produces Butyrate, one of the most essential SCFAs for gut health, 224 thus a reduction may indicate that less Butyrate is produced in PwPD. In addition to these SCFA-producing bacteria, one taxon previously associated with metabolic syndrome in PD 221 was higher in our group, Lactonifactor, a genus from the Actinobacteria phyla. Therefore, it is possible that PwPD may experience metabolic dysfunction. As it pertains to immune functions in the gut microbiota, taxa that have previously been deemed pro- and anti-inflammatory which were shown to be different between our PD and HC groups are highlighted in Table 2.3. Pro-inflammatory bacterial taxa are those that activate the immune system. When this activation occurs in an uncontrolled, persistent manner, gut inflammation is perpetuated, which can promote dysbiosis and inhibit GBA function. In contrast, anti-inflammatory bacterial taxa modulate inflammatory responses to attenuate inflammation and balance the gut microbiota's immune responses. Since each taxa have complex and context-dependent responses, individual bacterial taxa are responsible for multiple functions and are not just classified as pro- or anti-inflammatory. However, previous gut microbiota literature in PD populations has characterized particular taxa generally into anti- or pro-inflammatory categories. 38,77,225,226 In alignment with these classifications, we highlighted differences between the PD and HC groups seen in the current analysis: Table 2.3 Bacterial taxa involved in immune functions shown to be different between PD and HC groups. IMMUNE FUNCTIONS Higher in PD // Lower in PD Function 40 Phyla Class Order Family Genus Species (a summary of our data) Firmicutes Clostridia Clostridiales Mogibeterium f Gordonibacter Pro- inflammatory 226 Firmicutes Clostridia Clostridiales Mogibeterium f Parasutterella Actinobacteria Coriobacteriia Coriobacteriales Coriobacteriaceae PAC000692 Anti- inflammatory 227,22 8 (5 taxa were lower) Firmicutes Clostridia Clostridiales Lachnospiraceae PAC001046 PAC001046 Firmicutes Clostridia Clostridiales Lachnospiraceae Clostridium g24 PAC001164 Firmicutes Clostridia Clostridiales Ruminococcaceae Eubacterium g23 PAC001051 Bacteroidetes Bacteroidia Bacteroidales Odoribacteraceae Butyricimonas Butyricimonas facihominis Firmicutes Clostridia Clostridiales Ruminococcaceae Eubacterium g23 PAC001050 Anti- inflammatory 229,23 0 (4 taxa were higher in our PD group) Firmicutes Erysipelotri chi Erysipelotric hales Erysipelotrichace ae Clostridium g6 Clostridium ramosum Firmicutes Clostridia Clostridiales Christensenellaceae PAC001437 PAC001437 Actinobacteria Coriobacteriia Coriobacteriales Coriobacteriaceae Adlrecreutiza Adlercreutzia equolifaciens Firmicutes Clostridia Clostridiales Ruminococcaceae Ruminococcus Ruminococcus cal. Degrades mucin that lines the intestines. Our findings indicate that PwPD may be more susceptible to gut inflammation. We found one pro-inflammatory genus to be higher in the PD group (Gordonibacter), while two were lower (Parasutterella and PAC000692). Notably, PAC000692 has previously been shown to be lower in PwPD compared to an age-matched control group in previous studies. 227 Three anti-inflammatory species were higher, while five were lower in the PD group. Further, pro-inflammatory Adercreutzia equolifaciens was higher in the PD group. This elevation has previously been seen in PD and may be a compensatory attempt to counteract the excessive gut inflammation seen in PD. 229 In addition to inflammation, one Firmicutes species that degrade the protective mucus lining of the intestines to promote permeability was actually lower in the PD group (Rumminococcus callus). 231 While this reduction seems counterintuitive since it implies a reduction in inflammatory-induced intestinal permeability, an excessive reduction in permeability may actually inhibit the transport of critical metabolites and neurotransmitters from the gut to the brain. 5 41 From a neurologic standpoint, Table 2.4 highlights bacterial taxa were different between PD and HC groups: Table 2.4 Bacterial taxa involved in neurologic functions shown to be different between PD and HC groups. NEUROLOGIC FUNCTIONS Higher in PD // Lower in PD Function Phyla Class Order Family Genus Species Firmicutes Clostridia Clostridiales Eubacteriaceae Inhibits tryptophan metabolism, 86 a precursor to dopamine synthesis. 93 Firmicutes Clostridia Clostridiales Peptococcaceae Proteobacteria Betaproteobacteria Burkholderiales Sutterellaceae Correlated with depression. 232 Eubacteriaceae and Peptococcaceae were lower in our PD group compared to the HC group. These bacterial families have been shown to inhibit the metabolism of tryptophan, 86 a key precursor to dopamine synthesis. 93 Previously, no study in PD has identified differences in Peptococcaeae. However, the higher levels of Eubacteriaceae observed in this analysis are consistent with some previous evidence in PD, 233 but conflicting with others. 89 In addition, one bacterial family that was lower in our group, Sutterellaceae, has previously been correlated with depression. 232 While the evidence linking the gut microbiota to PD symptomology is sparse, previous evidence has shown particular bacteria to be linked to motor and cognitive symptoms. In agreement, our current findings show that these bacteria are significantly different in PwPD compared to the HC group (shown in Table 2.5): Table 2.5 Bacterial taxa involved in PD Symptomology shown to be different between PD and HC groups. PD SYMPTOMOLOGY Higher in PD // Lower in PD Function Phyla Class Order Family Genus Species Proteobacteria Betaproteobacteria Burkholderiales Sutterellaceae PAC0001046 (-) correlated with depression 232 42 Firmicutes Erysipelotrichi Clostridiales Peptococcaceae PAC0001236 (-) correlated with dyskinesia 222 Firmicutes Clostridia Clostridiales Lachnospiraceae Coprococcus g2 Coprococcus comes (-) correlated with akinesia 222 Firmicutes Erysipelotrichi Erysipelotrichales Erysipelotrichaceae Longicatena Longicatena caecimuris (+) correlated with worse cognitive symptoms (MMSE) in non-PD 234 Firmicutes Erysipelotrichi Erysipelotrichales Erysipelotrichaceae Longicatena Clostridium innocuum In particular, one Firmicutes genus that was lower in our PD group has previously been correlated with an increase in dyskinesia symptoms in PwPD. 222 In addition, one Firmicutes species that was lower in our PD group has previously been linked to an increase in akinesia. 222 Regarding cognitive symptoms, two Firmicutes species that were higher in our PD group have previously been linked to worse cognition on the Mini- Mental State Exam (MMSE) in non-PD populations. 234 Interestingly, differences between PD and HC groups were seen along the entire Proteobacteria taxonomy, from phyla to species: Bacteria within this Proteobacteria taxonomy clear out reactive oxidative species (ROS), 235 have been shown to promote mood, 232 produce SCFAs, 236 and promote anti- inflammatory responses. 236 This is the first PD and gut microbiota study from the U.S. that has shown significant differences among any markers in this taxonomy. Although only one non-U.S. study found a different bacterial family within another Proteobacteria taxonomy to be higher in PD (Enterobacteriaceae). 88 Another notable finding was that one species from the Akkermansia genus was elevated in PwPD: PHYLA → CLASS → ORDER → FAMILY → GENUS → SPECIES *Higher in PD group // Lower in PD group Proteobacteria → Betaproteobacteria → Burkolderiales → Sutterallaceae → Parasutterella → Parasutterella excrementihominis 43 Akkermansia has consistently been elevated in both U.S.-based and non-U.S. PD and gut microbiota studies. 82,86,89,95,96,101,111,114,237-240 This elevation has led to conflicting conclusions, given that Akkermansia plays a beneficial role in the production of SCFAs, 241 but is also pro-inflammatory, linked to constipation, and contributes to neurodegeneration in Alzheimer’s disease. 242 Conclusion: Globally, 35 studies have compared the gut microbiota in a PD population. While it is difficult to identify consistent differences in PwPD across these studies (due to methodologic and sample size differences), the findings from this comparative analysis align with previous gut microbiota and PD studies. In particular, several studies similarly found no significant difference in -diversity indices between PD and HC groups. 87,89,92,100,111,238,239 In addition to composite gut diversity and richness scores, the taxonomic differences between PD and non-PD groups observed in this current analysis are consistent with some previous evidence. Specifically, the current study revealed consistent reductions in the Blautia taxa, 89,115,233 and increases in Clostridium inoculum, Clostridium ranosum, 81,233,237,239 Coriobacteriales, 238,243 and Akkermansia. 82,86,89,95,96,101,111,114,237-240 While broadly, consistencies were seen between the current analysis and previous studies, many of these bacterial markers differed in their taxonomy of origin (i.e., previous studies have shown changes in Clostridium species originating from the PHYLA → CLASS → ORDER → FAMILY → GENUS → SPECIES *Higher in PD group // Lower in PD group Verrucomicrobia → Verrucomicrobiae → Verrucomicrobiales → Akkermansiaceae → Akkermansia → KQ968618_s 44 Bacillota phyla, whereas our current findings show Clostridium species originating from the Firmicutes phyla) and taxonomic level (i.e., previous studies have identified differences in Rumminococcaeae at the genus level, whereas our findings show differences at the species level). In addition, we found conflicting results compared to previous studies, including lower levels of Oscillibacter in the PD group, whereas previous studies showed higher levels. 89 The heterogeneity seen in this study compared to previous investigations stems from the fact that many of these previous investigations were non-U.S. studies where diet, for one, would be significantly different. This substantiates our methodologic approach to acquire data from a healthy control group rather than use markers shown to be different in previous investigations. In doing so, we were able to instead identify the bacterial differences observed among our specific study cohort. By conducting this preliminary analysis, it is possible to use these bacterial markers characteristic of our specific study cohort to investigate the role of gut health on fitness status (Aim 1, Chapter 3), motor function (Aim 2, Chapter 4), and cognition (Aim 3, Chapter 5) in PwPD. 45 Chapter 3 The gut microbiota is associated with aerobic fitness status in people with PD ABSTRACT: Introduction: Treatments for Parkinson’s disease (PD) have traditionally been focused on improving brain function, like PD medications and exercise. However, since it has become apparent that the gut microbiota plays a role in PD, it is necessary to re- direct the trajectory of PD treatments to also improve gut health. To date, little attention has been given to potential therapeutic strategies that can improve gut health in people with PD (PwPD). Fortunately, the same exercise modalities that improve brain function and PD symptoms have also been shown to restore the gut microbiota in populations other than PD. This exercise evidence was substantiated by several associative studies that linked gut health to fitness status in non-PD populations. However, in PD, no study to date has provided this necessary, foundational associative evidence seen in non-PD populations. Therefore, the purpose of this study was to identify the relationship between fitness status and the gut microbiota in PwPD (Hoehn and Yahr I-III). Methods: 25 PwPD underwent a 6-minute walk test to assess their aerobic fitness status (estimated maximal oxygen consumption, VO2). Stool samples were collected and analyzed to assess the gut microbiota (detailed in Chapter 2). To determine the association between VO2 and composite measures of gut health (with covariates), a robust linear regression model was employed to account for outliers and linear regression assumption violations. A best subset selection was performed to identify which of the six possible gut composite scores to use in the model (identified in Chapter 2; Shannon’s -diversity, Simpson’s -diversity, Phylogenetic Diversity, ACE, Chao1, or Jackknife). To identify covariates for the model, two best subset selections 46 were performed, one to identify covariates for estimated VO2 and one to identify covariates for the gut composite scores. As an exploratory analysis, a Pearson’s correlation test was conducted to determine associations between estimated maximal VO2 and the individual bacterial taxa previously identified in Chapter 2. Results: The best subset selections identified Shannon and Simpson’s diversity indices as relevant predictors for the gut microbiota. Age and BMI were identified as covariates for aerobic fitness status, and dietary fat and years of diagnosis were identified as covariates for the gut composite scores. Age was removed from the model, as it was colinear to years of diagnosis (r=0.407, p<0.05) and deemed not as relevant to a PD population as years of diagnosis. As hypothesized, aerobic fitness was positively correlated with the Shannon (estimate: 23.06, p=0.021) and Simpson (estimate: 502.02, p=0.015) -diversity indices. In addition, one bacterial species from the Clostridium family, PAC001164 sp., was negatively correlated with estimated maximal VO2 (r= - 0.495, p<0.05). Conclusion: This study was the first of its kind to identify an association between composite measures of gut health and aerobic fitness levels in PwPD. Thus, it is plausible that improving fitness status through modalities like exercise can improve gut health in PwPD. Elucidating this relationship between gut health and fitness provides the critical, foundational evidence needed for future exercise interventions that target gut health in a PD population. 47 INTRODUCTION: Parkinson’s disease (PD) has traditionally been presumed to be a ‘Brain-first’ disorder. Thus, treatment strategies for PD have been focused on improving brain function. These treatments include pharmacological strategies, like PD-related medication, 124 and nonpharmacological strategies, like exercise. 244-246 Aerobic exercise, in particular, has been shown to improve motor and cognitive symptoms characteristic of PD. 131,132,135,247,248 Importantly, recent evidence has transpired that has turned PD on its axis to alternatively argue that the gut microbiota plays a role in the disease process. 5 Due in part to the Gut-Brain Axis (GBA), this ‘Body-First’ hypothesis of PD has gained immense traction in the past decade, 249 substantiating the need to shift the target of PD treatments from the brain to the gut microbiota. Unfortunately, however, little to no attention has been given to treatments that can improve gut health in PD. An interesting overlap exists that can and should be exploited as a gut-related treatment in a PD population: the same exercise interventions that improve PD symptoms have also been shown to improve the gut microbiota in non-PD populations. 250 Aerobic exercise has been shown to improve composite scores of gut health, like -diversity, and bacterial taxa beneficial to gut health in populations including cancer survivors and older adults. 251,252 The rationale to support exercise as a therapeutic intervention for gut health was substantiated by numerous associative studies that have shown higher aerobic fitness status (estimated maximal oxygen consumption; VO2) to be linked to gut health. For instance, -diversity has consistently been positively associated with aerobic fitness status in various populations. 158,187 (See Appendix 1) In addition, particular bacterial markers have been associated with VO2, like Prevotella, 160 Clostridium, 157 and Lactobacillus. 140 From this evidence, it would be 48 plausible to suggest that, while exercise has been shown to improve brain function in people with PD (PwPD), the gut microbiota may moderate these exercise-induced improvements. However, in PD, no study to date has provided the necessary, foundational associative evidence of a relationship between gut health and fitness status seen in non-PD populations. Therefore, the purpose of Aim 1 was to identify whether a relationship exists between fitness status and the gut microbiota in PwPD (Hoehn and Yahr I- III). It was hypothesized that common composite scores to define gut health would be positively associated with aerobic fitness status (estimated maximal VO2 via a 6-minute walk test). In addition, we hypothesized that aerobic fitness status would be correlated with particular gut bacterial markers shown to be different in our PD group when compared to age-matched non-Parkinsonian individuals (see Chapter 2 for definitions and methods for each of the above measures). This was the first study of its kind to assess links between gut health and fitness status in a PD population. METHODS: Study Design: In this case-control study, 49 PwPD were recruited. Initially, 32 met the inclusion criteria (see Appendix 2), 26 completed all necessary study procedures, and one outlier was removed due to their age being 3.9 standard deviations below the group mean. Thus, a total of 25 PwPD were included in the analyses. Once enrolled and informed consent was signed, study measures were assessed at one point either at the Clinical Exercise Research Center at USC or an outpatient neurologic physical therapy clinic (Rogue Physical Therapy, Re+Active Physical Therapy, or Casa Colina Physical 49 Therapy). After this testing, participants were sent home with a stool sample collection kit and surveys to be completed within 14 days of initial testing. The stool sample collection, diet recall, and the ROME-IV Survey gastrointestinal symptom survey are detailed in Chapter 2. Participant Demographics: Demographics are detailed in Chapter 2. Specifically, age and body weight (kg) were obtained to calculate aerobic fitness status. 1 Aerobic Fitness Measurement: To obtain estimated maximal VO2, participants underwent the 6-minute walk test, a clinically validated indirect measure of aerobic capacity for individuals with PD (reliability coefficient= 0.77). 253,254 Participants were instructed to walk as quickly as possible without running on an indoor pre-measured walkway for 6 minutes. A research assistant walked behind the participant so as not to pace them, but rather to ensure their safety during the test and to track the distance covered in meters. Once completed, the following formula was used to estimate VO2 max: Stool Sample Collection & Storage: Stool sample collection and storage are detailed in Chapter 2. Covariate Measurements: Covariate measurements are detailed in Chapter 2. STATISTICS: All statistical analyses were performed using R Statistical Package (version 4.2.0). 255 Estimated VO2 max = 70.161 + (0.023 * meters) - (0.276 * sex, men 1/ women 0)- 0.193 * (resting heart rate) - (0.191 * weight kg) 1 50 16s rRNA Amplicon Sequencing and Bioinformatics: Microbial sequencing and analyses are detailed in Chapter 2. Six gut composite scores indicative of gut health were considered as potential predictors to link fitness to gut health. Three composite scores were considered that measure both species evenness and richness simultaneously: 1) Shannon -diversity, 2) Simpson -diversity, and 3) Phylogenetic Diversity. Three composite scores were considered that measure species richness only: 4) The Abundance Coverage Estimator (ACE), 5) Jackknife, and 6) Chao1. All six composite scores were included in a best subset selection model with aerobic fitness status as the outcome measure to reduce the number of predictors in the model to adhere to the sample size. From this analysis, two composite scores were identified and used in the regression models as predictors: 1) Shannon -diversity, and 2) Simpson -diversity (see Appendix 4). Details about how gut measures were obtained and calculated are found in Chapter 2. Covariate Identification: The analysis for covariate identification can be found in Appendix 5. First, the following measures were selected a priori as potential covariates of estimated maximal VO2: age, body mass index (kg/m2; BMI), constipation severity, average dietary fat, carbohydrate, and protein consumption (%). A best subset selection was performed to reduce the number of covariates to aerobic fitness to be included in all statistical models. Age and BMI (kg/m 2 ) were identified as covariates of estimated maximal VO2. Second, the following measures were selected a priori as potential covariates of Shannon’s and Simpson’s -diversity: age, BMI (kg/m 2 ), levodopa equivalence dose 51 (LED, see Appendix 3), years of diagnosis, average dietary fat, carbohydrate, and protein consumption. A best subset selection was conducted to identify covariates of Shannon’s and Simpson’s -diversity composite scores to be included in all statistical models. Dietary fat (%) and years of diagnosis were identified as covariates of the gut microbiota. Thus age, years of diagnosis, BMI (kg/m 2 ), and dietary fat (%) were included in the model as covariates. However since age and years of diagnosis were colinear, (r=0.407, p<0.05), age was removed from the model as years of diagnosis was deemed a more relevant measure specifically for PwPD (see Appendix 5). The final covariates included in the model were years of diagnosis, BMI (kg/m 2 ), and dietary fat consumption (%). Linear Regression Models: A multiple linear regression was preliminarily employed to assess the relationship between estimated maximal VO2 and Shannon’s -diversity, Simpson’s -diversity, years of diagnosis, BMI, and dietary fat (%) (covariates). This linear regression model showed statistical significance, but one linear regression assumption was violated. Thus, a robust linear regression model was subsequently conducted. A robust linear regression implements a stringent statistical penalty, reducing the skewing of data due to outliers without removing any data. This was deemed the most appropriate statistical model to preserve the small sample size. Taxonomic Differences & Pearson Correlation: Species differences between PwPD and age-matched controls were previously identified in Chapter 2, and used to explore specific bacterial taxa that may be correlated with aerobic fitness. To do so, a Pearson correlation was employed (p< 0.05), 52 since estimated maximal VO2 was normally distributed. Since the primary aim of this study involves gut composite scores, the results of this exploratory analysis can be found in Appendix 7. RESULTS: Aerobic fitness status was positively associated with -diversity: First, a linear regression model revealed a significant positive correlation between estimated maximal VO2 and Shannon -diversity (p=0.013) and Simpson’s - diversity (p=0.007). Since two linear regression assumptions were violated (homoscedasticity and outliers; see Appendix 8), a robust linear regression was alternatively used (See Table 1; effect size= 0.319). Both Shannon (p=0.021) and Simpson -diversity (p=0.015) indices were positively correlated with estimated maximal VO2 (See Figure 3.1). Aerobic fitness status was inversely associated with one bacterial species. The Pearson Correlation analysis revealed one negative correlation between estimated maximal VO2 and a bacterial species from the Clostridium family (R= -0.495; p<0.05; see Figure 3.2). However, no other phyla, class, order, family, or genus was correlated with aerobic fitness status (see Appendix 7). VO 2 Max (Robust Regression) VO 2 Max (Linear Regression) Table 3.1 Multiple linear regression and robust regression models assessing correlations between aerobic fitness and gut diversity. 53 In Chapter 2, we identified a number of bacterial taxa that were significantly different in PwPD compared to the non-PD participants (higher and lower concentrations). Informed speculations for differences between PwPD and controls in absolute concentrations of the bacterial taxa identified in Chapter 2 and hypotheses with respect to their role in aerobic fitness are included in Appendix 9. DISCUSSION: Overall, it appears that one of the most commonly used measures of gut health, -diversity, is positively linked to aerobic fitness status in PwPD. Thus, it can be inferred that increasing aerobic fitness status with exercise will lead to improvements in gut diversity. These findings are consistent with previous evidence in non- PD populations, which has Figure 3.1 Associations between -diversity and aerobic fitness status among people with PD. (via robust linear regression; p<0.05) Figure 3.2 A species from the Clostridium family was lower in the PD group was negatively correlated with aerobic fitness status (p<0.05). 54 consistently shown -diversity to be positively linked to aerobic fitness status. 141,144,158,187-189,256 Not only that, but -diversity has also consistently been shown to be lower in PD populations compared to age-matched controls. 100,222,257-259 This evidence would suggest that, by increasing the fitness level in PwPD, -diversity would also increase. Importantly, this study was the first to identify an association between fitness level and gut health in a PD population. One species, PAC001164 sp., was shown to be negatively correlated with aerobic fitness status (r= -0.495, p<0.05; see Figure 3.2). This is consistent with previous studies that have shown an association between fitness and lower Clostridium taxa at the family level in the athletic population and in older adults. 156,188,260 However, since this species is involved in producing SCFAs and is considered anti-inflammatory, it is reasonable to assume that the relationship we found would be in the opposite direction: that increased fitness levels would be associated with increased PAC001164 sp. Given that higher fitness status is attributed to an increase in SCFA and less inflammation, 251 it is unclear why we found a negative correlation to fitness in our current analysis in PwPD. One possible hypothesis is that the higher a person’s aerobic fitness status, the more they are utilizing said bacterial strain to produce SCFAs, and, therefore, a reduction would be seen in those who are more aerobically fit. However, it is important to note that broad inferences pertaining to the gut microbiota and fitness status made at the species level should be made with caution, given that individual species are unlikely to significantly impact overall gut health. Based on our results of a relationship between fitness level and -diversity, it would appear that improving fitness status in PwPD could improve gut health overall. 55 However, it is necessary to determine whether our participants with PD were unusually fit, as many were participating in regular exercise at the time of testing. In other words, it may be difficult to improve fitness in a group of individuals who have hit the ceiling in terms of fitness capacity. Based on normative classifications for aerobic fitness using age, biological sex, and total meters walked in 6 minutes, 1 only seven out of the 25 PwPD met the criteria for aerobic fitness norms. Thus, it would be reasonable to suggest that improvements to aerobic fitness status are needed and could, in fact, improve gut health in our study population. However, it is important to consider the fact that the aerobic fitness norm criteria we used only include age and biological sex and do not take into account the years of PD onset or PD severity. In a PD population, these factors can greatly impact normal age-related changes to health. However, to date, there are no fitness norms specifically for PwPD. Despite these novel findings, there were limitations to this study. Firstly, based on the sample size, the effect size from these findings was moderate, at 0.319. Thus, conclusions must be made with caution. Secondly, our inclusion criteria was to include PwPD between H&Y I-III, and previous findings have found that disease severity confounds significant differences in PwPD and controls in gut markers, particularly in H&Y stages 4 and 5 of PD. 87,242 Thus, the heterogeneous nature of both the gut microbiota and PD across disease severity stages warrants future studies with a larger sample size to stratify by disease severity more precisely than H&Y stages. Thirdly, given the development of more advanced methodologies for measuring the gut microbiota, different gut measurements can and should be considered. For instance, it may be necessary to employ a metabolomic analysis to obtain metabolite measures, 56 like SCFAs, which have been positively correlated with VO2 in non-PD populations. 141 Fortunately, we have collected additional stool samples from this current study and plan to measure metabolites in the future. Lastly, it may be advantageous to measure aerobic fitness directly, via a maximal VO2 test. This test uses a spirometer and metabolic cart to directly measure oxygen consumption during exertional cycling or treadmill via a respiratory exchange ratio, or the amount of oxygen inhaled versus carbon dioxide exhaled (Parvo Medics Inc., Salt Lake City, UT). However, this test may be less feasible for PwPD, depending on their symptoms and disease severity, as it is uncomfortable to wear a mask while exercising. 261 CONCLUSION: Findings from this study support the inference that improving fitness status can improve gut health, thereby targeting the Gut-Brain Axis in PD. These findings are promising because they provide the first implications that exercise could be a potential therapeutic strategy to improve gut health in PwPD. Given that it is not possible to infer causality between fitness and gut health in this current study, future studies should utilize a longitudinal, cross-sectional design to investigate this potential causality further. In Chapter 2, we identified various bacterial taxa that were significantly different between PwPD and the non-PD group, but we did not find associations between fitness and these taxa. Although it is unclear what this data may suggest in terms of fitness from the current findings, this information can be used in a future intervention study. For example, if in a future intervention study, these taxa favorably change as fitness level improves in PwPD following an exercise intervention, it would establish that the absolute amount of these bacterial taxa play a role in fitness status in PwPD. 57 Chapter 4 The gut microbiota is associated with motor symptoms in people with PD ABSTRACT: Introduction: Motor symptoms are a hallmark characteristic of Parkinson’s disease (PD) that are primarily attributed to dopaminergic loss and degeneration of dopamine-producing cells in the substantia nigra pars compacta (SNc) originating in the midbrain. 25 However, recent evidence has shown that PD is impacted by alterations to the gut microbiota (dysbiosis) and Gut-Brain Axis (GBA). This involvement of the GBA challenges the hypothesis that motor symptoms in PD are due to brain dysfunction. Instead, the gut microbiota and dysbiosis may also contribute to motor symptoms. However, only sparse evidence exists to date that shows gut alterations are linked to motor function in PD. 80,95,98,101,111,115 As such, more evidence is warranted. Therefore, the second aim of this dissertation was to identify whether motor function was linked to the gut microbiota in people with PD (Hoehn and Yahr I-III). Methods: 25 PwPD underwent the Unified Parkinson’s Disease Rating Scale III (UPDRS-III) and Short Physical Performance Battery (SPPB) to assess their motor function. Stool samples were collected and analyzed to assess the gut microbiota (detailed in Chapter 2). To determine associations between composite measures of gut health and 1) UPDRS-III scores and 2) SPPB scores (with covariates), two robust linear regression models were employed to account for outliers and linear regression assumption violations. A best subset selection was performed to identify which of the six possible gut composite scores to use as predictors in both models (Shannon’s - diversity, Simpson’s -diversity, Phylogenetic Diversity, ACE, Chao1, or Jackknife). To 58 identify covariates, three best subset selection analyses were performed, 1) for the gut composite scores, 2) for UPDRS-III scores, and 3) for SPPB scores. As an exploratory analysis, a Pearson’s correlation test and a Spearman’s correlation test were conducted to determine associations between 1) UPDRS-III scores and 2) SPPB scores (respectively) and each bacterial taxa previously identified in Chapter 2. Results: The best subset selection models revealed ACE species richness as a relevant predictor for the gut microbiota for both UPDRS-III and SPPB linear models. Levodopa equivalence dose (LED) was a covariate for UPDRS-III scores, age was a covariate for SPPB scores, and dietary fat (%) was a covariate for the ACE score. UPDRS-III was negatively correlated with species richness (ACE; estimate: -0.05; p= 0.014), after controlling for LED and dietary fat (%). SPPB scores were not significantly associated with any gut composite measure. One bacterial species from the Clostridium family, Clostridium ranosum, was negatively correlated with UPDRS-III scores (r=-0521, p<0.001). One bacterial species from the Blautia family, LN913006 sp., was positively correlated with SPPB scores (r=0.521, p<0.001). One species from the Lachnospiraceae family, Coprococcus comes, was positively correlated with SPPB scores (r=0.402, p<0.05). Conclusion: Findings from this study indicate that lower levels of species richness in the microbiota may contribute to more severe motor impairments in PwPD. The current analysis supports the notion that the gut microbiota may moderate treatment-induced improvements to motor symptoms and, therefore, should be considered in future interventions to improve PD symptomology. 59 INTRODUCTION: Motor impairments are a hallmark characteristic of Parkinson's disease (PD). Motor symptoms, like tremors, rigidity, and bradykinesia, 22-24 occur primarily from degeneration of and damage to dopaminergic neurons of the basal ganglia (BG); specifically in the substantia nigra pars compacta (SNc). Overall, this leads to a loss of dopamine in the striatum (caudate nucleus and putamen). Damage in the SNc is due to misfolded -synuclein protein aggregations 9-14 and neuroinflammation. 15-19 Since the BG is responsible for motor control, 20,21 this degeneration of dopaminergic neurons seen in PD leads to motor impairments. Motor symptoms are indicative of PD, thus, they are commonly used to classify disease severity. 25 However, given the influence of the Gut-Brain Axis (GBA) in PD, it is possible that these motor symptoms may be influenced by the gut microbiota (see Chapter 1 for a description of the GBA). The involvement of the GBA in PD challenges the hypothesis that motor impairments in PD are due entirely to brain dysfunctions. In addition, alterations to the gut microbiota (or dysbiosis) may contribute to motor symptoms. Recent evidence has emerged to support this plausibility. In PD animal models, motor impairments are aggravated by dysbiosis, 102,103 and dopamine metabolism has been shown to be disrupted by the gut microbiota (via the tyrosine hydroxylase gut metabolite pathway involved in dopamine synthesis). 262 More recently, several studies have identified GI symptoms to be linked to more severe motor impairments in people with PD (PwPD). 104- 108 In addition, among the observational evidence showing gut differences between PwPD and age-matched non-Parkinsonian individuals (detailed in Appendix 1), some have included secondary analyses linking motor symptoms to particular bacterial markers. 80,95,98,111,115,233 However, there have been mixed findings, and some studies 60 have shown no associations. 79,87,100 Another limitation in being able to conclusively state that the gut microbiota is associated with motor impairments in PwPD is that it is challenging to compare data across studies. A limited number of these studies considered covariates of the gut microbiota in their associative analyses, 80 while others did not. Notably, only two of these studies have been conducted in the United States (US), 79,98,233 which is problematic since gut microbiota significantly differs between countries of origin. Given the lack of consistent evidence seen to link motor function to the gut microbiota in PD, it is difficult to confirm the exact role of the gut on motor symptomology. Confirming that gut health is linked to PD symptomology would provide evidence to support the ‘Body- first’ hypothesis and indicate that the gut microbiota plays an influential role in the disease process. As such, more evidence is warranted to link motor function to the gut microbiota, particularly evidence that includes gut microbiota covariates and is conducted in the US. Therefore, the second aim of this dissertation was to identify whether motor function was linked to the gut microbiota in PwPD. It was hypothesized that particular gut composite scores would be associated with motor function. In addition, it was hypothesized that motor function would be associated with specific gut bacterial markers indicative of PD based on our study and previous studies (see Chapter 2 for definitions and methods for each of the above composite gut measures). METHODS: Study Design: In this case-control study, 49 PwPD were contacted. Initially, 32 met the inclusion criteria (see Appendix 2), 26 completed all necessary study procedures, and one outlier 61 was removed due to their age being 3.9 standard deviations below the group mean. Thus, a total of 25 were included in the analyses. Once enrolled and informed consent was signed, assessments of study measures were conducted at one-time point either at the CERC laboratory at USC or an outpatient neurologic physical therapy clinic (Rogue Physical Therapy, Re+Active Physical Therapy, or Casa Colina Physical Therapy). After this testing, participants were sent home with a stool sample collection kit and surveys to be completed within 14 days of initial testing. The stool sample collection, diet recall, and the ROME-IV Survey gastrointestinal symptom survey are detailed in Chapter 2. Participant Demographics: Demographics are detailed in Chapter 2. Unified Parkinson’s disease rating scale III (UPDRS-III): To obtain motor symptoms, the UPDRS-III scale was administered by a certified Movement Disorders Society (MDS) UPDRS individual. The MDS UPDRS was developed to evaluate various aspects of Parkinson’s disease, including non-motor and motor experiences of daily living and motor complications. For this study, the motor sub- scale UPDRS-III, was used, which includes various motor tasks for the participant to execute that are rated on a scale of 1-4. 263 Short Physical Performance Battery (SPPB): The SPPB was also administered by a research assistant. The SPPB is a reliable measure of physical function among older adults 264 and individuals with PD. 265 This assessment includes three tests. First, Timed Balance was employed, where participants were asked to stand in three conditions for 10 seconds each: a) side-by- side stand with both feet planted on the ground; b) semi-tandem stand with the side of 62 the heel of one foot touching the big toe of the other; and c) a full tandem stand with the heel of one foot touching the big toe of the other foot. Second, a Gait Speed assessment was employed, in which the participant was asked to stand at the designated start line and walk at their "usual" pace for the 4-meter distance for two attempts (The fastest time to completion between the two trials was recorded). Third, the Chair Stand Assessment was employed, where participants were asked to stand up from a seated position in a chair with their arms crossed at their chest under two conditions: a) participants performed a single chair stand without assistance using a chair that did not have armrests; and, if the first condition was successfully completed, b) participants were asked to perform five repeated chair stands as quickly as possible in the same chair (The time to completion was recorded). Stool Sample Collection & Storage: Stool sample collection and storage are detailed in Chapter 2. Covariate Measurements: Covariate measurements are detailed in Chapter 2. STATISTICS: All statistical analyses were performed using R Statistical Package (version 4.2.0). 255 16s rRNA Amplicon Sequencing & Bioinformatics: Microbial sequencing and analyses are detailed in Chapter 2. Six gut composite scores indicative of gut health were considered as potential predictors to link fitness to gut health. Three composite scores were considered that simultaneously measure both species evenness and richness: 1) Shannon -diversity, 2) Simpson -diversity, and 3) Phylogenetic Diversity. Three composite scores were considered that measure species 63 richness only: 4) The Abundance Coverage Estimator (ACE), 5) Jackknife, and 6) Chao1. All six composite scores were included in two best subset selection models, one with UPDRS-III as the outcome measure and one with SPPB as the outcome measure to reduce the number of predictors in the model to adhere to the sample size. From this analysis, one composite score was identified and used in both regression models: 1) ACE. Details about how these measures were obtained and calculated are found in Chapter 2. Covariate Identification: First, the following measures were selected a priori as potential covariates of motor function: age, years of diagnosis, levodopa equivalence dose (LED), and body mass index (kg/m2; BMI). A best subset selection model was performed to reduce the number of covariates to motor function for each of the linear models, the model with UPDRS-III as the outcome measure and the model with SPPB as the outcome measure. LED was identified as a covariate for UPDRS-III scores, and age was identified as a covariate for SPPB scores. Second, the following measures were selected a priori as potential covariates of ACE: age, BMI, levodopa equivalence dose (LED), years of diagnosis, and average dietary fat, carbohydrate, and protein consumption. A best subset selection was conducted to identify covariates of the ACE composite score to be included in all statistical models. Dietary fat (%) was identified as a covariate of ACE. The final covariates included in the UPDRS-III model were LED and dietary fat (%). The final covariate included in the SPPB model was age (See Appendices 4 and 5). 64 Linear Regression Models: Two separate multiple linear regressions were preliminarily employed: 1) to assess the relationship between UPDRS-III scores and ACE, with LED and dietary fat (%) as covariates, and 2) to assess the relationship between SPPB scores and ACE, with age and dietary fat (%) as covariates. The linear regression model including UPDRS-III as the outcome measure showed statistical significance, but one linear regression assumption was violated. Thus, a robust linear regression model was subsequently conducted. A robust linear regression implements a stringent statistical penalty, reducing the skewing of data due to outliers without removing any data. This was deemed the most appropriate statistical model to preserve the small sample size. Taxonomic Differences & Pearson Correlation: Species differences between PwPD and age-matched controls were previously identified in Chapter 2 and used to explore specific bacterial species that may be correlated with motor function. To determine specific species that could be correlated with motor function, a Pearson correlation was employed (p< 0.05), since UPDRS-III scores were normally distributed (see Appendix 6). To determine specific species that could be correlated with SPPB, a Spearman correlation was employed (p< 0.05), since SPPB scores were not normally distributed. Since the primary aim of this study involves 65 gut composite scores, the results of this exploratory analysis can be found in Appendix 7. RESULTS: UPDRS-III scores were negatively associated with species richness (ACE). A robust regression revealed a negative correlation between the ACE index and UPDRS- III scores (estimate- 0.05, p=0.014). In other words, the higher the species richness, the lower the UPDRS-III score and the less severe the motor symptoms in PwPD (see Figure 4.1). SPPB was not correlated with any gut diversity measure. UPDRS-III scores were negatively associated with one bacterial species. A Pearson correlation analysis revealed only one Figure 4.2 Clostridium ranosum was negatively correlated with UPDRS-III (p<0.05). UPDRS-III (Robust Regression) UPDRS-III (Linear Regression) Table 4.1 Linear Regression and Robust Regression between ACE species richness and UPDRS-III scores. Figure 4.1 Inverse association between species richness and UPDRS-III scores. 66 negative correlation between UPDRS-III scores and the species Clostridium ranosum. (see Figure 4.2; r=-0.411, p<0.05). No other phyla, class, order, family, or genus was correlated with UPDRS-III scores (see Appendix 7). SPPB scores were positively associated with one bacterial species. A Spearman correlation analysis revealed only one positive correlation between SPPB scores and one species from the Blautia family, LN913006 sp. (see Figure 4.3; r= 0.521, p<0.001). No other phyla, class, order, family, or genus was correlated with SPPB scores (see Appendix 7). In Chapter 2, we identified a number of bacterial taxa that were significantly different in PwPD compared to the non-PD participants (higher and lower concentrations). However, given that we did not find associations between motor symptoms and these taxa, it is unclear what these differences might mean concerning motor symptomology. Informed speculations for differences between PwPD and controls in absolute concentrations of the bacterial taxa identified in Chapter 2 and hypotheses with respect to their role in motor symptomology are included in Appendix 9. Figure 4.3 Two species, Coprococcus comes and LN913006 sp. were positively correlated with SPPB. 67 DISCUSSION: Overall, it appears that the lower the species richness of the gut microbiota, the more severe the motor symptoms are in PwPD. This was the first study of its kind to identify a link specifically between species richness and motor symptoms. These findings suggest that a reduction in species richness may be one reason motor symptoms are more severe, substantiating the ‘Body First’ hypothesis. In particular, these findings implicate that a reduction in species richness may reflect reductions in gut bacteria that synthesize beneficial neural constituents involved in motor function. (i.e., gut bacteria involved in synthesizing dopamine). 68 Still, more evidence is needed to elucidate the role of gut composite scores on motor function in PD. The Clostridium ranosum species was inversely associated with UPDRS-III scores in PwPD. This would suggest that the lower this species concentration is, the more severe the motor symptoms are in a person with PD. This is the first time this particular species has been linked to motor symptoms in a PD population. Interestingly, elevated Clostridium ramosum has been attributed to immune function. 266 Thus, it is reasonable to assume that the relationship between UPDRS-III scores and Clostridium ramosum may indicate a lower level of this species, thus less immune regulation, which can contribute to motor impairments in PwPD. In addition, one species from the Blautia family, LN913006 sp. and one species from the Clostridia family, Coprococcus comes, were positively associated with SPPB scores in PwPD. This would suggest that the higher a person’s physical function, the higher these species are in PwPD. These species have been shown to produce SCFAs, like Butyrate. 219,222 Thus, it can be inferred that improvements to physical function may be attributed to higher levels of SCFA production. Even still, it is difficult to make conclusive deductions regarding our 68 findings, given that these taxa associations were identified at the species level, which is small and often does not significantly impact overall gut health. Not only that, but it is not possible to make claims regarding the links between SPPB scores and bacterial taxa, since there appears to be a ceiling effect, at which nine of the PD participants received a perfect score (12/12 points), which could have skewed the results. Although our hypothesis was partially supported, there were limitations to this study. First, as stated, the sample size was relatively small. However, the effect size for the association between ACE species richness and UPDRS-III scores was moderate (0.447). Still, a larger sample size would be beneficial to confirm the associations observed in the current study. Secondly, despite the fact that UPDRS-III is a clinically- validated measure of motor symptoms in PwPD, 263 it would be beneficial to either include additional measures of motor function (i.e., timed up and go test or a sit-to- stand), parse out specific sections of the UPDRS-III (i.e., freezing of gait), or even stratify more precisely based on disease severity to elucidate whether specific motor symptoms are particularly linked to the gut microbiota. In the current analysis, our sample size was too small to stratify sub-scores of motor function for PD or disease severity in such a way. Thirdly, similar to aim 1, it would be advantageous to measure metabolites via a metabolomic analysis to obtain fecal SCFA concentrations, providing a more comprehensive assessment of gut health. To date, serum SCFAs have been shown to have a significant influence on motor function in PwPD, 267-269 but only one study has assessed links between motor function and fecal SCFAs in PwPD to date. 98 We plan to measure these gut markers in future analyses. 69 CONCLUSION We found that species richness is inversely associated with UPDRS-III scores, thus increasing species richness can reduce motor symptom severity (lower UPDRS-III scores). These findings support the hypothesis that the gut microbiota may impact motor function in PwPD, although a more thorough investigation is warranted. Based on these findings, future, intervention-based research with a larger sample of PwPD is necessary to assess whether improvements seen in motor function from various PD treatments (i.e., exercise) are moderated by changes in the gut microbiota. Importantly, this current analysis provides the rationale for such an investigation. 70 Chapter 5 The gut microbiota is associated with cognition in people with PD ABSTRACT: Introduction: Although Parkinson’s disease (PD) is typically attributed to motor symptoms, cognitive impairments are also prevalent among people with PD (PwPD). Given the recent evidence indicating the involvement of the Gut-Brain Axis (GBA) in PD, it is likely that these cognitive symptoms are not only due to impairments to the brain but also influenced by gut alterations (or dysbiosis). While PD and gut research has significantly expanded in the last decade to show gut alterations are present in PwPD, little to no attention has been made to investigating how these gut alterations impact cognitive function. To date, only two studies have attempted to link cognitive symptoms to gut health in PwPD, with mixed findings. Therefore, the third aim of this dissertation was to identify whether cognitive symptoms were associated with the gut microbiota in PwPD (Hoehn and Yahr I-III). Methods: 25 PwPD underwent the Montreal Cognitive Assessment (MoCA) and Repeatable Battery Assessment of Neuropsychological Status (RBANS) tests to assess total, or global cognition. Stool samples were collected and analyzed to assess the gut microbiota, detailed in Chapter 2. To determine associations between composite measures of gut health and 1) MoCA scores and 2) RBANS scores (with covariates), two robust linear regression models were employed to account for outliers and linear regression assumption violations. A best subset selection was performed to identify which of the six possible gut composite scores to use (Shannon’s -diversity, Simpson’s -diversity, Phylogenetic Diversity, ACE, Chao1, or Jackknife). To identify which 71 covariates to include in the regression models, three best subset selection models were performed: 1) for the gut composite scores, 2) for MoCA scores, and 3) for RBANS scores. As an exploratory analysis, two linear regression models were employed to determine the association between the gut microbiota and domains of cognition from the MoCA and RBANS tests, accounting for covariates. Two best subset selection models were employed to determine which cognitive sub-domain scores to include. Lastly, as an additional exploratory analysis, two Pearson’s correlation tests were conducted to determine associations between each bacterial marker previously identified in Chapter 2 and 1) MoCA and 2) RBANS. Results: Neither measure of global cognition (total MoCA or total RBANS scores) was associated with any gut composite score. However, the Orientation domain from the MoCA assessment was positively associated with ACE species richness (estimate: 47.88, p=0.015), and the Visuospatial Reasoning domain of the RBANS assessment was inversely associated with Phylogenetic Diversity (estimate: -2.70, p=0.050). Total MoCA scores were correlated with the Actinobacteria phyla (r= -0.42, p=0.038), the Peptococcaceae family (r= -0.412, p<0.05), the FTRU genus (r= -0.433, p<0.05), the Coprococcus comes species (r= 0.484, p<0.05), and the PAC001607 species (r= -0.509, p<0.05). In addition, total RBANS scores were correlated with the PAC000195 genus (r= 0.489, p<0.05) and Blautia hensenii species (r= 0.608, p<0.001). Conclusion: This was one of the first studies of its kind to analyze links between cognitive function and gut health in PwPD. While global cognition was not associated with composite scores of gut health in PwPD, specific domains of cognition were (Orientation and Visuospatial Reasoning). In addition, bacterial taxa involved in 72 producing beneficial metabolites were linked to global cognitive scores. The current analysis provides the plausibility that the gut microbiota may moderate treatment- induced improvements to cognition and, therefore, should be considered in future interventions to improve PD symptomology. 73 INTRODUCTION: While Parkinson’s disease (PD) is typically attributed to hallmark motor symptoms, cognitive impairments are also prevalent among people with PD (PwPD) and therefore have become recognized as characteristic of the disease as well. PwPD suffer from cognitive deficits, such as impairments in executive function, attention, and visuospatial reasoning. 26 While the etiology is complex, cognitive impairments are likely due in part to -synuclein protein aggregations 9-14 and neuroinflammation. 15-19 Although -synuclein protein aggregations and neuroinflammation are known to affect sub- cortical brain regions to induce motor impairments in PwPD, they also impact cortical regions of the brain typically responsible for more cognitively-demanding tasks. Moreover, as PD progresses, disruptions to cortical and basal ganglia neural circuitry become progressively more impaired, exacerbating cognitive deficits. 270,271 Upon consideration of the Gut-Brain Axis (GBA) in PD, it has become ever more critical to elucidate the influence that gut alterations, or dysbiosis, may have on cognitive function in PwPD. While PD and gut research has significantly expanded in the last decade to show that gut alterations are present in PwPD (see Appendix 1), little to no attention has been made to how these gut alterations impact PD symptomology. Most of the sparse evidence pertaining to PD symptomology that does exist to date has focused only on motor symptoms since these symptoms are most commonly attributed to PD. However, cognitive impairments are becoming more widely recognized as hallmark PD symptoms as well. Thus, there is a need to include these symptoms in the gut microbiota and PD research space. To date, gastrointestinal (GI) symptoms have been associated with 74 cognitive performance among PwPD, 116 but only two studies have attempted to link gut microbial markers with cognition in PwPD. 118,119 Thus, more evidence is warranted. There is also a need to utilize cognitive assessments that capture the complex nature of the cognitive deficits seen in PwPD. In PD populations, the most common cognitive tests used to measure cognition typically assess overall, or global cognition (i.e., the Montreal Cognitive Assessment; MoCA). The MoCA effectively measures global cognitive impairment, particularly for people living with Alzheimer’s disease or dementia. 272 However, PwPD often suffer from more heterogeneous cognitive impairments. 273-275 For instance, some PwPD have global cognitive deficits that are well-represented by a cognitive test, like the MoCA. In contrast, other PwPD may not have global cognitive deficits but, more specifically, suffer from impairments in specific cognitive domains, like attention or visuospatial deficits. Thus, there is a need to use comprehensive measures of cognition that can also capture these particular cognitive domains (i.e., attention or visuospatial capabilities) in PwPD. Therefore, the third aim of this dissertation was to identify whether cognitive symptoms were linked to the gut microbiota in PwPD (Hoehn and Yahr I-III). It was hypothesized that global cognitive scores would be positively associated with the composite gut health measures. In addition, particular cognitive sub-domains were hypothesized to be linked to gut health in PwPD. PwPD suffer from immediate memory, visuospatial reasoning, and attention deficits. 190 Immediate memory deficits have been linked to the gut microbiota in other neurodegenerative populations, 276 visuospatial reasoning has been attributed to gastrointestinal (GI) symptoms in PD, 116,191,192 and attention deficits has been linked to the gut microbiota in individuals 75 with mild cognitive impairment. 193 Therefore, it was additionally hypothesized that these cognitive domain scores from the MoCA and RBANS tests would be positively associated with composite gut health measures in PwPD. Lastly, it was hypothesized that total MoCA and RBANS scores would be associated with particular gut bacterial taxa indicative of PD based on our study (see Chapter 2 for definitions and methods for each of the above measures). METHODS: Study Design: In this case-control study, 49 PwPD were contacted. Initially, 32 met the inclusion criteria (see Appendix 2), 26 completed all necessary study procedures, and 25 were included in the analyses (1 outlier was removed). Once enrolled and informed consent was signed, assessments of study measures were conducted at one time point either at the CERC laboratory at USC or an outpatient neurologic physical therapy clinic (Rogue Physical Therapy, Re+Active Physical Therapy, or Casa Colina Physical Therapy). After this testing, participants were sent home with a stool sample collection kit and surveys to be completed within 14 days of initial testing. The stool sample collection, diet recall, and the ROME-IV Survey gastrointestinal symptom survey are detailed in Chapter 2. Participant Demographics: Demographics are detailed in Chapter 2. The Montreal Cognitive Assessment (MoCA): A research assistant administered the MoCA. The MoCA is a 13-item test designed to assess global cognition, as well as visuospatial, executive, memory, attention, orientation, and language skills among individuals with neurodegenerative 76 disorders. 272 including PD. .277,278 A total score of 30 points was calculated to assess global cognition. In addition, the following cognitive domains were scored: 279 (1) delayed recall, where the research assistant read five words to the participant and asked them to repeat it back to them (two practice trials), after which participants were asked to recall the five words after approximately five minutes (5 points); (2) visuospatial abilities, via a clock drawing task (3 points) and a cube drawing task (1 point); (3) executive function, via a trail making task (1 point), F-word fluency task (1 point), and a two-item visual abstraction task (2 points); (4) attention, concentration, and working memory via a tapping task (1 point), a serial subtraction task (3 points), and a digits forward and backward task (1 point); (5) language via three-item animal naming task (3 points), sentence-repeating task (2 points), and F-word fluency task (1 point); and (6) orientation via evaluation of time, date, and location (6 points). 279 The Repeated Battery for Assessment of Neuropsychological Status (RBANS): A certified research assistant administered the Repeatable Battery of Neuropsychological Status (RBANS). The RBANS is a comprehensive yet time-effective cognitive test for PwPD. 273 It involves 12 tasks that measure global cognition as well as five cognitive domains: (1) Immediate memory via list-learning and story memory, (2) Visuospatial/Constructional abilities via copying an abstract picture and line orientation, (3) Language via picture naming and semantic fluency tasks, (4) Attention via digit span and coding tasks, and (5) Delayed Memory via list recall, list recognition, story memory, and abstract figure recall. 280 The RBANS is a valid measure not only of global cognition but also of each of the five domains of cognition, 274 and has been used to discriminate 77 between PD-specific cognitive impairments and other neurodegenerative disorders .273,275,281 Covariate Measurements: Covariate measures are detailed in Chapter 2. Stool Sample Collection & Storage: Stool sample collection and storage are detailed in Chapter 2. STATISTICS: All statistical analyses were performed using R Statistical Package (version 4.2.0). 255 16s rRNA Amplicon Sequencing & Bioinformatics: Microbial sequencing and analyses are detailed in Chapter 2. Six gut composite scores indicative of gut health were considered potential predictors to link fitness to gut health. Three composite scores were used to measure both species evenness and richness simultaneously: 1) Shannon -diversity, 2) Simpson -diversity, and 3) Phylogenetic Diversity. Three composite scores were considered that measure species richness only: 4) The Abundance Coverage Estimator (ACE), 5) Jackknife, and 6) Chao1. All six composite scores were used in two separate best subset selection models, one with total MoCA scores as the outcome variable and one with total RBANS scores as the outcome variable, to reduce the number of predictors in the model to adhere to the sample size. From this analysis, the composite score, ACE, was identified to be used in the regression model for total MoCA scores, and Phylogenetic Diversity was identified to be used in the regression model for total RBANS scores. Details about how these measures were obtained and calculated are found in Chapter 2. 78 Covariate Identification: First, the following measures were selected a priori as potential covariates of cognitive function: age, years of diagnosis, levodopa equivalence dose (LED), and body mass index (kg/m2; BMI). Best subset selection models were performed to reduce the number of covariates to cognitive function for each linear model, the model with MoCA as the outcome measure and the model with RBANS as the outcome measure. Age was identified as a covariate for MoCA scores, and BMI (kg/m 2 ) was identified as a covariate for RBANS scores. Second, the following measures were selected a priori as potential covariates of ACE and Phylogenetic Diversity: age, BMI, levodopa equivalence dose (LED), years of diagnosis, and average dietary fat, carbohydrate, and protein consumption. Two best subset selection models were conducted to identify covariates of each of the gut composite scores identified to be used in the regression models (ACE and Phylogenetic Diversity). Dietary fat (%) was identified as a covariate for ACE, and dietary fat (%) was identified as a covariate for Phylogenetic Diversity. The final covariates included in the MoCA model were age and dietary fat (%). The final covariates included in the RBANS model were BMI (kg/m 2 ) and dietary fat (%). (See Appendices 4 and 5) Linear Regression Models: Two separate multiple linear regressions were employed: 1) to assess the relationship between total MoCA scores and ACE and age and dietary fat (%) as covariates and 2) to assess the relationship between total RBANS scores and the Phylogenetic Diversity index score with BMI and dietary fat (%) as covariates. Secondly, as an exploratory analysis, cognitive sub-scores were measured from both MoCA and 79 RBANS tests. To determine which cognitive sub-domains to use for each model, best subset selection models were employed. For the MoCA score model, the Orientation sub-score was identified as the predictor to include. For the RBANS score model, visuospatial reasoning was identified as the predictor to include. Both linear regression models showed statistical significance, but one linear regression assumption was violated in each model. Thus, a robust linear regression model was subsequently conducted. A robust linear regression implements a stringent statistical penalty, reducing the skewing of data due to outliers without removing any data. This was deemed the most appropriate statistical model to preserve the small sample size. Taxonomic Differences & Pearson Correlation: Species differences between PwPD and age-matched controls were previously identified in Chapter 2 and used to explore specific bacterial species that may be correlated with cognition. To determine specific species that could be correlated with MoCA scores, a Pearson correlation was employed (p< 0.05) since estimated MoCA scores were normally distributed (see Appendix 6). To determine specific species that could be correlated with RBANS scores, a Pearson correlation was employed (p< 0.05), since estimated RBANS scores were normally distributed (see Appendix 6). 80 RESULTS: MoCA Scores While no associations were seen between total MoCA scores and any gut composite score, the MoCA sub-domain test, Orientation, was positively associated with species richness (ACE). Total MoCA scores were not associated with ACE species richness. However, as detailed in Figure 5.1, the MoCA sub-test, Orientation, was positively associated with ACE species richness (estimate: 47.88, p- 0.015). In other words, the ability of a PwPD to navigate their orientation (i.e., the date, time, and location of testing), the higher their species richness. No other sub-domain of the MoCA test was significantly associated with gut diversity. (p>0.05) Total MoCA scores were associated with five bacterial taxa, one phylum, one family, one genus, and two species. As Figure 5.2 indicates, at the phyla level, Actinobacteria was negatively correlated with MoCA scores (r= -0.417, p=0.038). In addition, the Peptococcaceae family (r= -0.412, p<0.05) and FTRU genus (r= -0.433, p<0.05) were negatively correlated with MoCA scores. Finally, the Coprococcus comes Figure 5.1 MoCA sub-domain, Orientation, was positively associated with ACE species richness. (p<0.05) 81 species was positively associated with MoCA scores and (r=0.484, p<0.05), and the PAC001607 species was negatively associated with MoCA scores. (r=-0.509, p<0.05). RBANS Scores While no significant associations were identified between the total RBANS scores and any gut composite score, the RBANS sub-domain, Visuospatial Reasoning, was negatively associated with Shannon’s diversity index. Total RBANS scores were not associated with Phylogenetic Diversity. However, Figure 5.3 Five bacterial taxa, one phyla (A), one family (B), one genus (C), and two species (D) were significantly associated with total MoCA scores in PwPD. (p<0.05) Figure 5.2 The RBANS sub-domain score, Immediate Memory, was associated with Shannon's Alpha Diversity in PwPD. 82 Figure 5.3 shows that the RBANS sub-test, Visuospatial Reasoning, was negatively associated with Phylogenetic Diversity (estimate: -2.70, p=0.05). In other words, the ability of a PwPD to immediately recall, the lower their gut diversity. No other sub- domain of the RBANS test was significantly associated with gut diversity (p>0.05). Total RBANS scores were associated with two bacterial taxa, one at the genus level and one at the species level. As seen in Figure 5.4, at the genus level, PAC000195 was positively correlated with RBANS (r=0.489, p<0.05). At the species level, Blautia hensenii was positively correlated with RBANS (r=-.608, p<0.001). In Chapter 2, we identified several bacterial taxa that were significantly different in PwPD compared to the non-PD participants (higher and lower concentrations). Informed speculations for differences between PwPD and controls in absolute concentrations of the bacterial taxa identified in Chapter 2 and hypotheses with respect to their role in cognitive symptomology are included in Appendix 9. DISCUSSION: This analysis was one of the first of its kind to identify links between cognitive function and the gut microbiota in PwPD. Overall, global cognition appears not to be linked to gut composite scores in PwPD, which is consistent with previous findings. 118,119 Figure 5.4 Two bacterial taxa, the PAC000195 genus, and Blautia Hensenii species, were positively correlated with total RBANS scores in PwPD. 83 This may be due to 1) the particular cognitive impairments characteristic of PD of which global cognitive scores do not accurately represent or 2) the fact that gut composite scores, like -diversity, do not represent the individual bacteria that may be involved in cognitive function in PwPD. Firstly, global cognitive scores were not significantly associated with gut composite scores in PwPD. This may be because since PwPD experience more particular cognitive deficits than other neurodegenerative disorders, 277,282 and a global cognitive score is not the most appropriate way to measure cognitive impairments in PwPD. Instead, stratifying sub-domains of cognition from the MoCA and RBANS tests was likely a more appropriate approach to assessing the influence of the gut microbiota on cognitive symptoms in PwPD. In doing so, we found orientation scores (from the MoCA) to be linked to higher species richness. In contrast, visuospatial reasoning scores (from the RBANS) were linked to lower species richness and evenness (gut diversity). Notably, orientation sub-scores like that from the MoCA assessment have been deemed a measure of visuospatial capabilities as well. 283 Thus, it is possible that brain regions involved in visuospatial tasks may be impacted by the level of species richness in the gut microbiota. 284 In PD, visuospatial deficits have been identified, 190,285 and attributed to alterations to the subthalamic nucleus and its cortical projections. 286 As it pertains to the involvement of the gut microbiota, gut alterations have been attributed to worse visuospatial abilities in individuals living with GI-related disorders, like irritable bowel syndrome. 287 In addition, PwPD who have GI symptoms, like constipation, have been shown to have worse visuospatial capabilities. 191,192 Thus, the association we identified between Orientation sub-scores from the MoCA assessment and species 84 richness points to the plausibility that the gut microbiota impacts brain regions that carry out these cognitive functions. However, as seen in Figure 5.1, 19 of 25 PwPD scored a 6/6 points on the Orientation portion of the MoCA, thus, the association observed was likely skewed due to a ceiling effect of a majority of participants obtaining a perfect score (6/6 points). In contrast to the positive association between species richness and the Orientation sub-score from the MoCA assessment, the inverse relationship between the visuospatial reasoning sub-score from the RBANS assessment and Phylogenetic Diversity that was found was perplexing. It is important to note that lower levels of this gut composite score indicate lower species richness and, importantly, less species evenness (or unevenness). This unevenness can indicate an underabundance or overabundance in particular bacterial taxa. One explanation for the inverse relationship between phylogenetic diversity and visuospatial scores observed in the current study could be that bacterial taxa that impacts visuospatial capabilities were underabundant or overabundant in our group of PwPD. For instance, if a bacterial taxon involved in promoting visuospatial abilities was higher in our group, it would have swayed the diversity scores because it would cause unevenness. Thus, this uneven microbial environment might have led to the link between lower phylogenetic diversity scores and better visuospatial capabilities. However, based on the phylogenetic diversity score alone, it is not possible to identify which bacterial taxon could be altered, which is why it is important to measure individual taxa in conjunction with gut composite scores. Secondly, there may be individual bacterial taxa that impact cognition that a gut diversity composite score cannot accurately represent. When assessing bacteria that 85 could be linked to cognitive scores in PwD, we found five taxa to be significantly correlated with total MoCA scores and two taxa to be significantly correlated with total RBANS scores. The phyla, Actinobacteria, was inversely associated with total MoCA scores. This is the first study to identify such a correlation. However, it is not feasible to attribute this correlation to any particular gut function, as taxa at the phyla level are too diverse and carry out numerous functions. 288 Three SCFA-producing taxa were positively correlated to cognition. Coprococcus comes positively correlated with MoCA scores, whereas PAC000195 g. and Blautia hensenii were positively correlated with total RBANS scores. Importantly, Blautia hansenii has previously been linked to higher cognitive scores in one other study that correlated bacterial taxa with cognition in PwPD. 119 In contrast, two SCFA-producing taxa, the FTRU genus and PAC001697 species, were negatively correlated to MoCA scores. Given these mixed findings, whether higher or lower levels of SCFA-producing bacterial taxa are linked to cognitive function in PwPD is unclear. Higher levels of SCFA-producing bacteria have been shown to be linked to better cognitive function in other neurodegenerative populations. 289,290 In addition, in PD, two previous studies have shown that serum and fecal SCFAs are positively linked to cognitive symptoms. 267,268 Thus, more investigation is warranted, particularly that involves testing SCFAs directly along with SCFA-producing bacteria. Interestingly, two taxa were linked to cognitive function in the current study that has previously been linked to motor symptoms in PwPD. One family, Peptococcaceae, was negatively correlated to MoCA scores, suggesting that higher levels of this taxa are linked to worse cognitive function. Peptococcaceae has previously been positively 86 associated with dyskinesia in PwPD, 222 thus, higher levels of this taxa may be not only linked to worse motor function but also worse cognitive function. In addition, Coprococcus comes species was positively associated with total MoCA scores. This species has also been shown to be negatively linked to akinesia in PwPD. 222 Thus, these findings collectively support the implication that higher levels of Coprococcus comes may lead to less severe motor and cognitive symptoms in PwPD. Despite the fact that our hypotheses were supported, there were limitations to this study. First, the sample size was relatively small; thus, conclusions regarding the impact of gut health and cognition should be made with caution. Even still, the effect size for the association between ACE species richness and orientation scores from the MoCA was moderate (0.529), and Phylogenetic Diversity and Visuospatial reasoning from the RBANS was also moderate (0.560). Still, a larger sample size may be beneficial to confirm the associations observed in the current study. Secondly, given that many of the bacterial taxa linked to cognitive scores are involved in producing SCFAs, it would be advantageous to measure metabolites via a metabolomic analysis to obtain fecal SCFA concentrations, of which serum SCFAs have been shown to have a significant influence on motor 267,268 We plan to measure these gut markers for future analyses. Lastly, only 18 of the 25 PwPD completed the RBANS assessment. This was primarily due to many PwPD refusing to participate in this test due to fatigue and agitation from a lengthy testing session (roughly 2.5 hours to complete all tests in one day). Thus, when testing both cognitive tests in future analyses, it may be advantageous to schedule two separate days of testing to reduce the prevalence of fatigue and increase participation in both cognitive tests. 87 CONCLUSION While cognitive impairments occur in PwPD, they are often more particular than the global cognitive deficits seen in other neurodegenerative disorders. In the current analysis, this was mirrored in the gut microbiota of PwPD; global cognitive scores did not appear to be linked to gut health, but particular cognitive domains were. Orientation and visuospatial reasoning were linked to species richness and gut diversity, respectively. In addition, SCFA-producing bacterial taxa which carry out metabolite, immune, and neurologic functions for the brain and GBA were linked to global cognitive scores. Not only that, but particular bacteria previously linked to worse motor outcomes in PwPD were also linked to worse cognitive outcomes in the current study. Overall, this analysis adds to the sparse evidence linking cognition to gut health in PwPD, although the impact of the gut microbiota on cognition in PwPD warrants further investigation. 88 Chapter 6 SUMMARY AND CONCLUSIONS This dissertation assessed the role of the gut microbiota in Parkinson’s disease (PD) and took the first foundational steps to identify potential therapeutic strategies to improve gut health in people with PD (PwPD) via fitness status. Gut microbial diversity is linked to higher levels of aerobic fitness in PwPD. The first study of this dissertation (Chapter 3) identified aerobic fitness status to be positively correlated with the gut microbiota in PwPD. Specifically, Study 1 used two common composite measures of the gut microbiota that indicate both species richness and evenness to comprehensively assess overall gut health. This study's findings indicate that PwPD with higher aerobic fitness levels also have higher gut diversity, thus, better gut health. The theoretical concept used in formulating the hypothesis for study 1 is referred to as ‘The Athletic Gut Microbiota’: individuals who are extremely physically fit and considered to be the pinnacle of health have distinctly different, arguably healthier gut microbiota. 291 In particular, athletes have been shown to have an increase in gut diversity and marked increases in beneficial bacterial markers (i.e., metabolites). 145 These improvements to gut health can be attributed to the fact that athletes exercise more frequently and thus require greater metabolic demand from the gut microbiota, 148 resulting in increased beneficial bacteria and gut metabolites necessary for metabolic, immune, and neurologic function. 292 The findings from Study 1 provide rationale for the implication that interventions that increase aerobic fitness status, like exercise, could improve gut health in PwPD. This is the first time this implication has ever been considered in the PD research space. Since PwPD suffer from lower gut diversity and gut alterations (dysbiosis) that can impact disease onset, 89 progression, and symptomology (see Appendix 1), investigating modalities that can improve gut health in PD is even more critical. Based on the findings from Study 1, it appears aerobic exercise may be one such modality. In addition to dysbiosis, a large majority of PwPD suffer from non-motor symptoms related to the gut, like constipation. 4,209 Since aerobic exercise has been shown to improve colonic transit time and reduce the risk of constipation, 157 the link identified between aerobic fitness and gut health may indicate that improving aerobic fitness status can mitigate constipation in PwPD. 4 Overall, Study 1 addressed a critical gap in the PD literature in exploring therapeutic strategies to improve gut health. Findings from this study provide the foundational rationale for future intervention-based studies using exercise to target gut health in PD. Gut species richness is linked to worse motor function in PwPD. The second study (Chapter 4) assessed whether motor function (measured via UPDRS-III and SPPB tests) was correlated with the gut microbiota PwPD. Similar to Study 1, one common composite measure of the gut microbiota was used to assess overall gut health. Findings from Study 2 indicate that PwPD with more severe motor symptoms (or higher UPDRS-III scores) had lower species richness levels and, thus, worse gut health. To date, this is the first study to assess relationships between a composite measure of the gut microbiota and motor function, particularly UPDRS-III scores, in PwPD. Other than that, roughly ten studies that have compared the gut microbiota between PwPD and non-Parkinsonian individuals have included ancillary correlations between UPDRS-III scores and individual bacterial taxa in PwPD (see Appendix 1). Study 2 also assessed links between worse motor function and lower 90 levels of individual bacterial taxa, showing one bacterial taxa (Clostridium ramosum) to be linked to UPDRS-III scores that have previously been attributed to immune function. 266 In addition, we found one taxa (Coprococcus comes) to be linked to better motor function (via SPPB scores) that has previously been linked to less-severe akinesia in PwPD. 222 Overall, it appears that lower species richness and particular bacterial taxa are linked to worse motor function. Findings from Study 2 add to the PD and gut literature and support our hypothesis that the gut microbiota plays a role in PD symptomology. More importantly, these findings offer justification for our broader hypothesis that the gut microbiota could moderate the benefits seen from exercise (which improves fitness status) to motor function in PwPD. 250 It is well-established that exercise improves motor function in PwPD. 131,135,293 However, combined findings from Study 1 and Study 2 introduce the innovative plausibility that the gut may play a moderating effect on the exercise-induced improvements in motor function seen in PwPD. Future exercise intervention studies are necessary to assess whether the gut microbiota moderates the motor improvements seen from exercise in PwPD. The cognitive domains, orientation and visuospatial reason, are linked to gut health in PwPD. The third study (Chapter 5) assessed whether cognition (measured via MoCA and RBANS tests) was correlated with the gut microbiota in PwPD. Like Studies 1 and 2, common composite measures of the gut microbiota were used to assess overall gut health. Findings from Study 3 indicate that PwPD with better orientation capabilities (e.g., knowing the date, time, and their location at the time of testing) have higher levels of species richness, or better gut health. However, in contrast to our hypothesis, PwPD 91 with better visuospatial reasoning actually had lower levels of gut diversity (Phylogenetic Diversity) or presumably worse gut health. One possible explanation is that the lower the diversity, the more uneven the microbial environment, which is potentially driven by an overabundance or underabundance of particular bacterial taxon. It is possible that, in our group of PwPD, there was an underabundance or overabundance of a particular bacterial taxon that is involved in visuospatial reasoning that was driving the unevenness and led to a link between reduced phylogenetic diversity scores and better visuospatial scores. However, this was the first study of its kind to identify links between composite gut health scores and sub-domains of cognition in PwPD; therefore, more evidence is warranted to investigate this presumption further. To date, only two studies have linked individual gut bacterial taxa to total cognitive scores in PwPD. 118,119 In the present study, no consistent links were observed between bacterial taxa and cognition compared to these previous investigations. However, two taxa that have previously been linked to motor symptoms in PwPD were linked to cognition in our group. Specifically, Peptococcaeae, which was linked to worse MoCA scores in our group, has previously been linked to more severe dyskinesia in PwPD. 222 Thus, higher concentrations of this taxa may be linked to worse motor and cognitive function in PD. In addition, Coprococcus comes, which was linked to better MoCA scores in our group, has previously been linked to less severe akinesia in PwPD. 222 Thus, lower concentrations of this taxa may be linked to worse motor and cognitive function. We also identified three bacterial taxa that produce beneficial gut metabolites to be positively linked to cognitive scores, suggesting that an increase in metabolite-producing capabilities in the gut may also lead to improved cognition in 92 PwPD. Findings from Study 3 add to the PD and gut literature and support our hypothesis that the gut microbiota plays a role in PD symptomology. Additionally, these findings combined with Study 1 support our overall hypothesis that the gut microbiota could moderate the benefits seen from exercise to PD symptomology in PwPD. 250 Much like motor function, it is known that exercise improves cognitive function in PwPD. 132,135,294 However, upon considering these preliminary findings, it is possible that the gut microbiota moderates these improvements. Future exercise intervention studies are necessary to assess whether the gut microbiota moderates cognitive improvements seen from exercise in PwPD. Particular bacterial taxa are similarly linked to cognitive symptoms and aerobic fitness status in PwPD. From this dissertation alone, it is not possible to assess the moderating effect that the gut microbiota may have on improvements to PD symptomology from a therapeutic modality, like exercise. However, the current findings provide some of the first insights into the plausibility of such a moderating effect. Interestingly, two taxa (PAC00164 sp. and FTRU genus) from the same family (Lachnospiraceae) were shown to be similarly correlated to both cognition (MoCA scores) and aerobic fitness status in PwPD. In addition, two taxa (Peptrococceae and PAC001164 sp.) from the same taxonomic order (Clostridialies) were correlated to both cognition (MoCA scores) and aerobic fitness status. This preliminary evidence from studies 1 and 3 would suggest that the gut microbiota is involved in both aerobic fitness status and cognitive function in PwPD. 135,294,295 While implications must be made with caution from this study alone, this 93 evidence points to the hypothesis that the gut microbiota moderates exercise that improves fitness status and may even benefit cognition in PwPD. CLINICAL IMPLICATIONS 1. Improving aerobic fitness status via exercise can potentially improve gut microbial diversity in PwPD. 2. Motor function appears to be linked to the overall richness of microbial species in the gut microbiota. Thus, increasing species richness may attenuate motor impairments seen in PwPD. 3. The fact that global cognitive scores were not linked to gut health but particular cognitive domains were, supports the notion that, not only do PwPD suffer from particular cognitive impairments versus overall cognitive decline, but that the gut microbiota may be involved in these particular facets of cognition. 4. Many bacterial taxa involved in producing SCFAs were altered and linked to various aspects of PD symptomology in this current dissertation work. Thus, future investigations should emphasize SCFA production to clarify the role metabolites play in PD symptomology. Given that SCFAs are increased with exercise, a metabolomic analysis could further support our hypothesis that exercise-induced improvements to the gut microbiota may also impact motor and cognitive symptoms in PwPD. LIMITATIONS As discussed in each chapter, this dissertation work has several limitations. Namely, the small sample size prevents irrefutable conclusions from being made from this preliminary analysis. Even still, the effect sizes for the interactions between the gut microbiota and PD symptomology were moderate (motor function= 548; cognition= 94 0.526-0.560), and the effect size for the interaction between the gut microbiota and aerobic fitness status was moderate as well, at 0.319. However, conclusions should be made cautiously, and future research is needed to add to this groundbreaking analysis. Another limitation of this dissertation is the fact that only 16s rRNA analysis was conducted to obtain bacterial marker data, but no metabolomic analysis was done to obtain metabolite concentration data. Particularly as it relates to fitness status, metabolites are a key marker involved in fitness and exercise. Thus, it will be essential to add to this evidence with future metabolomic analyses to compare bacterial associations to metabolite associations between each aim (fitness status, motor function, and cognition). A third limitation was that not all PwPD participated in both cognitive tests, which may have skewed the results. This was primarily due to the participants becoming fatigued after a long testing session (1 day for 2-3 hours). Future studies that involve PwPD should consider changing the study design to cater to this limitation and possibly split the multiple tests into two separate testing days. The last limitation was the fact that more comprehensive or direct measures of each aim might be better to use. For instance, an indirect measure was employed to measure aerobic fitness status. In addition, more comprehensive measures of motor function may capture the particular motor impairments from which PwPD suffer (i.e., timed up and go test). Lastly, directly measuring the brain may more accurately demonstrate cognitive dysfunctions and the impact of the gut microbiota in PD. FUTURE RESEARCH Future research is necessary to accomplish the overall goals of 1) improving the gut microbiota with therapeutic interventions in PwPD and 2) elucidating the role of 95 the gut microbiota on PD symptomology. Findings from this dissertation are a critical first step and provide the necessary rationale for future intervention-based research to determine whether exercise-induced improvements to the gut microbiota impact motor and cognitive function in PwPD. In addition to this intervention-based research, it will also be important for future research to emphasize 1) the impact of alternative forms of fitness (i.e., muscular fitness or physical activity habits) on gut health, 2) the role of the Gut-Brain Axis on the proposed interplay between motor and cognitive function simultaneously, and 3) alternative, more specific measures of the gut microbiota (i.e., β-diversity and metabolomics). First, assessing other forms of fitness that impact the gut microbiota in PwPD may be beneficial, like muscular strength or physical activity frequency. The gut microbiota has been associated with muscular strength in animal models, 296 and with handgrip strength in healthy adults. 189,297 Exercise frequency has also been associated with gut health in non-PD populations. 144,145,156 Thus, future investigations may be needed to determine the influence of these forms of fitness on gut health in PwPD. In doing so, this evidence could inform researchers and clinicians of the most appropriate exercise modalities to administer to a PD population to improve the gut microbiota. Second, it may be necessary to consider the interplay between motor and cognitive functions that exists in PwPD. While motor and cognitive function are often assessed separately in PwPD, there is an irrefutable connection between motor and cognitive function that should be considered. Rather than segmenting these motor and cognitive functions into separate assessments, measuring motor 96 and cognitive function simultaneously with a test like dual-task walking may be advantageous. In this current dissertation work, we found various bacterial taxa to be similarly linked to motor and cognitive function. For instance, Coprococcus comes species is similarly linked to motor and cognitive function in studies 2 and 3; the higher the concentration of this species, the better SPPB and MOCA scores were in PwPD. In addition, species from the Blautia family were similarly linked to motor and cognitive function; the higher the concentration of LN913006 sp and PAC00195 g, the higher the RBANS scores, and the higher the concentration of Blautia hensenii, the higher the SPPB. From these findings, it can be surmised that testing motor and cognitive function simultaneously may have added benefit to elucidating the role of the gut microbiota on PD symptomology. Thirdly, it will likely be necessary to incorporate additional measures of the gut microbiota when assessing exercise-induced gut changes (β-diversity dissimilarities index) or to add to the existing dissertation findings via a metabolomic analysis. Along with the gut composite scores used in this dissertation work, we also assessed β-diversity gut composite scores between PwPD and an age-matched healthy control group. This measure compares how dissimilar the microbiota is between two groups (e.g., a control and experimental group) or between two time points (e.g., before and after a study intervention). As detailed in Appendix 10, we found the Jensen-Shannon β-diversity model to be significantly different between groups. (pseudo-F= 1.585, p= .034), and a trending difference in Bray-Curtis β-diversity between groups (pseudo-F= 1.310, p= .07). However, the goal of the current dissertation study was to observe one time point 97 and one group, not to compare between two time points or compare groups. Thus, β-diversity was not appropriate to report on. For future exercise-intervention-based studies, in which gut composite scores will be compared before and after an exercise intervention, it will be more suited to report on β-diversity to assess the effects of an exercise intervention on the gut microbiota. In addition, it may be necessary to test gut metabolites to assess the functional state of the gut microbiota. Previous evidence has shown that serum metabolite markers, like short chain fatty acids, play a role in motor symptoms in PwPD. 267,268 In addition, sparse evidence has shown fecal SCFAs to be reduced in PD. 267,298 However, no study has assessed fecal metabolomics and the link between SCFAs and fitness status in PD. Fortunately, we plan to do such an analysis to add to the findings from this dissertation work. While the gut microbiota is currently at the forefront of PD pathophysiology research, there has been no consideration of the moderating effects of the gut microbiota on PD symptoms or treatments. Since 90,000 Americans are diagnosed with PD annually, 299 a number that is expected to rise exponentially in the coming years, there is an imminent need to better understand the influence of the Gut-Brain Axis on PD outcomes. 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Front Cell Infect Microbiol. 2022;12:877914. 126 APPENDICES: Appendix 1: Gut studies between PD and HC, symptomology, and fitness Gut Marker No diff.   Motor Symptom Cognitive Symptoms Fitness /(-) / (+) Shannon’s Diversity 87,100,23 8 222,257-259 112,113,240 (+) Diversity 98,239 140,141,144 ,155,158,18 6-189 Simpson’s Diversity 87,238 100,222,257 240 155,187 Chao1 87,89,92,1 00,111,23 8,239 222,258 101,112,113,2 40 187,188 Phylogenetic Diversity 155,187 Firmicute/Bacteroidetes Ratio 154 Bifidobacterium (genus) 79,83,222 81,84,111,112 ,229,233,238- 240,243 156 Proteobacteria 300 Bacteroides Fragilis 81,84,88281,82 ,84,88,239 89,111,229,24 0 Prevotella 85 79,81,82,84,86- 88,112,113,229 142 Rikenellaceae (family) 119,258 (-) UPDRS-III 101,114 Barnisella (genus) 92 MMSE and MoCA (-) 119 Alistipes onderdonkii 113 MMSE and MoCA (-) 119 189,301 Bacteroides dorei 113 Odoribacter (genus) 239 MMSE and MoCA (-) 119 156 Lactobacillales (order) 79,96,237 79- 81,84,86,92,1 01,114,229,23 7,302 UPDRS-III (- ) 101 Lachnospiracaece (family) 79,80,101,238, 239,258,300 302 UPDRS-III (+) 80 Erysipelotrichaceae (order) 86 188 Roseburia (genus) 89,101,233,237 ,238,258,259 MMSE and MoCA (-) 118 156 Blautia (genus) 89,115,233,259 ,300 UPDRS-III (- ) 115 Clostridium (genus) 81,95,259 81,113,233,23 7,239 UPDRS-III (+) 4 188 Rumminococcaeae (genus) 237 79,87,233 UPDRS-III (+) 115 MMSE and MoCA (+) 118,119 140,156 Oscillospiraceae (genus) 84,88,113,115, 238,259 89,243 Christensenella (genus) 79,84,101,238 UPDRS-III (- ) 101 Butyricicoccus (order) 258 MMSE and MoCA (-) 119 Coprococcus 239 Proteobacteria (phyla) 101,239,259 Akkermansia (genus) 242 82,86,89,95,9 6,101,111,114 ,237-240 140,141,156 127 E. Shigella 81,90,91,303 80,81,92,101, 115 UPDRS-III (+) 115 E. Coli 90,91,303 189 Turibacter 101 Anaerotruncus 101 Streptococcus 101,115 UPDRS-III (+) 101 Bilophila 101,238 Acidominococcus 101 Desulfovibrionaceae (genus) 109,238,239 Collinsella 243 238,243 Enterobacteriaceae 300 Enterococcus UPDRS-III (+) 80,115 Flavonifractor UPDRS-III (- ) 101 Faecalibacterium UPDRS-III (- ) 115 189,260,301 Rikenellaceae UPDRS-III (- ) 111 MMSE and MoCA (+) 119 Coriobacteriaceae 304 Fusobacterium 304 128 Appendix 2: Inclusion criteria and testing procedures Inclusion Criteria Exclusion Criteria Individuals with PD -60 years of age or older; -Has been diagnosed with PD by a movement disorder specialist or neurologist (H&Y Stages I-III); -Stable medication status for 3 months and ON meds while testing; -Has not been diagnosed with any other neurologic disorder, including Alzheimer's or Huntington's disease; -Is cleared by their physician to participate in exercise testing, including walking, cycling, and standing up from a chair; -Is able to comprehend, carry out, and mail their stool sample for analysis (postage fees and materials will be provided); -Is willing and able to have a licensed phlebotomist collect blood samples. -Has made significant changes to their diet or nutrition within the previous year (i.e., changed macronutrient intake or caloric intake increases or reductions more than 500 calories per day for one month); -Smoke or has smoked within the last 12 months; -Taken antibiotics within the last 60 days; -Currently taking anticholinergic medications; -Has had mild-severe diarrhea in the last two days (3-10 loose or watery stools per day); -Has lived outside the United States within the last 12 months; -Has been diagnosed with uncontrolled type II diabetes (fasting blood glucose > 125 mg/dL); -Has been diagnosed with obesity (BMI > 30 kg/m 2 ; calculated using height and weight). Age or Spousal- matched Controls -60 years of age or older; -Has not been diagnosed with any neurologic disorder, including PD, Alzheimer's disease, Huntington's disease; -Is cleared by their physician to participate in exercise testing, including walking, cycling, and standing up from a chair; -Is able to comprehend, carry out, and mail their stool sample for analysis (postage fees and materials will be provided); -Is willing and able to have a licensed phlebotomist collect blood samples. -Has made significant changes to their diet or nutrition within the previous year (i.e., changed macronutrient intake or caloric intake increases or reductions more than 500 calories per day for one month); -Smoke or has smoked within the last 12 months; -Taken antibiotics within the last 60 days; -Has had mild-severe diarrhea in the last two days (3-10 loose or watery stools per day); -Has lived outside the United States within the last 12 months; -Has been diagnosed with uncontrolled type II diabetes (fasting blood glucose > 125 mg/dL); -Has been diagnosed with obesity (BMI > 30 kg/m 2 ; calculated using height and weight). 129 130 Appendix 3: Levodopa Equivalence Dose (LED) Table Participant Drug Dose (daily) Conversion Factor LED (mg) FITGUT01 Azilcet Levodopa/Carbidopa Amantadine 1mg 300mg 100mg 100 0.75 100 425mg FITGUT03 Levodopa 200mg 1 200mg FITGUT05 Levodopa/Carbidopa 300mg 0.75 225mg FITGUT09 Levodopa/Carbidopa 25/100mg x 6 0.75 563mg FITGUT11 Levodopa/Carbidopa 300mg 0.75 225mg FITGUT13 Levodopa 200mg 1 200mg FITGUT15 Levodopa/Carbidopa 300mg 0.75 225mg FITGUT17 Levodopa/Carbidopa 300mg 0.75 225mg FITGUT19 Levodopa/Carbidopa 500mg 0.75 375mg FITGUT21 Levodopa 700mg 1 700mg FITGUT25 Levodopa/Carbidopa 400mg 0.75 300mg FITGUT27 Levodopa/Carbidopa 400mg 0.75 300mg FITGUT31 Levodopa 400mg 1 400mg FITGUT33 Levodopa/Carbidopa 400mg 0.75 300mg FITGUT35 Sinimet 300mg 1 300mg FITGUT39 Levodopa 400mg 1 400mg FITGUT43 Duodopa 400mg 1.11 444mg FITGUT47 Rasagiline 1mg 100 100mg FITGUT51 Nupro (Rotigotine transdermal patch) 6mg 30 180mg FITGUT53 Sinimet 300mg 1 300mg FITGUT55 Levodopa/Carbidopa 400mg 0.75 300mg FITGUT57 Levodopa/Carbidopa 400mg 0.75 300mg FITGUT59 Levodopa/Carbidopa 600mg 0.75 450mg FITGUT61 Levodopa/Carbidopa 400mg 0.75 300mg FITGUT63 Levodopa 400mg 1 400mg Average= 328.5 (130.45)mg | [100-700] 131 Appendix 4: Gut microbiota composite score identification via Best Subset Selection Aim 1 Estimated maximal VO2 Aim 2 UPDRS-II SPPB Aim 3 MoCA RBANS 132 Appendix 5: Covariate Identification; best subset selection process and collinearity between covariates Aim 1 Shannon/Simps on Estimated maximal VO2 Aim 2 133 ACE gut composite score UPDRS-II SPPB Aim 3 ACE gut composite score Phylogenetic Diversity MoCA 134 RBANS Aim 3c, MoCA sub-domains Aim 3d, RBANS sub-domains Used adjusted r2 because BIC yielded collinearity in the final linear model. 135 Appendix 6: Normality for Gut Measures and primary outcomes Variable Normal parameters Mean/Median Approximately equal value. Skewness -1<x>+1 Kurtosis -1<x>+1 Shapiro- Wilkes P>0.05 Normalcy Shannon 4.03/ 3.97 -0.04 -1.27 W=0.95 P=0.23 NO Simpson 0.04/ 0.04 0.59 -0.69 W=0.86 P=0.003 NO Phylogenetic Diversity 508.6/ 502 0.12 -0.9 W= 0.97 P=0.74 YES ACE 364.8/ 366.65 0.37 -0.55 W= 0.96 P=0.32 YES Jackknife 377.2/377 0.34 -0.7 W= 0.95 P= 0.29 YES Chao1 355.6/349.0 0.31 -0.64 W= 0.96 P= 0.48 YES 136 Variable Normal parameters Mean/Median Approximately equal value. Skewness -1<x>+1 Kurtosis -1<x>+1 Shaprio- Wilkes P>0.05 Normalcy VO2 Max 28.1/ 27.88 0.23 -0.32 W= 0.97 P=0.67 YES UPDRS-III 26.2/ 28 -0.24 -0.96 W= 0.97 P=0.58 YES SPPB 10.12/11 -1.11 0.56 W=0.83 P= 0.0008 NO MoCA 24.2/ 24.5 -0.35 -096 W= P=0.11 YES RBANS 95.1/ 100.5 0.28 -0.56 W= P=0.85 YES 137 Appendix 7: Correlation analyses for aims 1-3 Aim 1, aerobic fitness Aim 2, motor function—UPDRS-III 138 Aim 2, motor function—SPPB 139 Aim 3, cognitive function—MoCA Aim 3, cognitive function—RBANS 140 141 Appendix 8: Assumptions and outliers for each aim Aim Linear Regression Assumptions Outliers Variance Inflation Factors (VIF)s (must be <5) 1, Aerobic Fitness Shannon = 4.45; Simpson = 5.00; BMI= 2.0; Years of Diagnosi s= 1.2; BMI= 1.9; Fat= 1.87 2a. Motor function , UPDRS -III ACE= 1.15; LED = 1.25; Fat= 1.34 2b. Motor function , SPPB ACE= 1.00; Age= 1.00 3a. Cognitiv e function , MoCA n/a, since non- significant whether running linear model or robust linear model n/a n/a 3b. Cognitiv e Functio n, RBANS n/a, since non- significant whether running linear model or robust linear model n/a n/a 142 3c. MoCA sub- domain s Orien= 1.02; Age= 1.02; Fat= 1.03 3d. RBANS , sub- domain s Visuo= 1.16; Age= 1.05; Fat= 1.20 143 Appendix 9: Informed Speculations for Future Research AIM 1- FITNESS STATUS While only one species was linked to aerobic fitness status, a number of similarities were observed between the current analysis and previous studies in non-PD populations that have associated measures of gut health with aerobic fitness. Blautia hensii and Adlercreutzia were higher in our PD group, and have been shown to be lower in fit breast cancer survivors 187 and older adults 296,305 respectively. This supports the notion that increasing fitness status may lead to a reduction these bacteria, which could benefit a person living with PD. In contrast, Oscillibacter and Burkholderiales were lower in our PD group. Oscillibacter, has been shown to be higher in athletes, 144 and Burkholderiales has been shown to be higher in people who have greater exercise capacity. 306 Therefore, increasing fitness status in PwPD may help to consequently increase these bacterial markers. Many Rumminococcaeae species are involved in producing SCFAs. In our PD group, 10 of the 12 Rumminococcaeae species identified were lower, which suggests less SCFA production. Similarly, 6 SCFA-producing Lachnospiraceae as well as the Butyricimonas facihominis species were lower in our PD group, further supporting the need to increase SCFA production, and one way to do this may be increasing fitness status. In older adult populations, higher levels of SCFA-producing bacteria have consistently been linked to higher fitness status. 306-308 Thus, it is possible that increasing fitness status could also lead to an increase in these SCFA-producing Rumminococcaeae species. In contrast, in younger adults, Rumminococcaeae has also been linked to lower fitness levels 189 and higher sedentary time. 301 This may conflict with our hypothesis, that increasing fitness status would alleviate adverse differences 144 related to these SCFA-producing bacteria seen in the gut microbiota. However, it is important to note that there is a distinct difference in the microbial makeup of people younger than 50 compared to those that are older than 50. 309,310 Our group, in particular, was comprised of people ages 51-86, therefore it is necessary to compare studies with comparable age groups. There were also conflicting findings regarding various Clostridium taxa. Our group of PD participants showed higher levels of Clostridium ramosum and Clostridium innocuum. But higher levels Clostridium genus and families have previously been attributed to lower fitness status in younger 189 as well as older adults. 188,296 However, previous fitness and gut health studies have identified Clostridium strains at different levels of taxonomy compared to our findings, and it is important to note that various Clostridium taxa function differently depending on the taxonomic level at which they are identified. AIM 2- MOTOR SYMPTOMS: In support of our findings, a reduction in PAC0011236 g. has previously been linked with dyskinesia. 222 Not only did we see a reduction in the PD group, but there was also a trending association between PAC0011236 g. and UPDRS-III (r=-0.212; p= 0.06), suggesting that the lower this bacterial genus, the more severe motor symptoms. A similar nonsignificant inverse correlation was seen between UPDRS-III and Lactonifactor, (-0.212, p=0.07), PAC001437 g. (-0.340, r=0.055), LN869527 s. (r=- 0.392, p=0.055), and Longicatena caecimuris (r= -0.283, p= 0.059; and in our PD group. These taxa were also significantly higher in our PD group, implicating that alterations in these bacterial taxa may be linked to more severe motor symptoms in PwPD. 145 Conflicting with our findings, previous evidence has shown a positive correlation between UPDRS-III and the Clostridium genus from the Clostriales family PwPD. 95 However, in the present study, we found a Clostridium ranosum from the Erysipelotrichi family to be negatively associated with UPDRS-III scores. However, it is probable that these conflicting findings are because each study identified a slightly different strain/type. AIM 3- COGNITIVE FUNCTION: Our study revealed Clostridium inoculum was higher in the PD group. Clostridium inoculum has previously been negatively correlated with MMSE scores in PwPD. 4 Our study also revealed Longicatena caecimuris was higher in the PD group. Longicatena caecimuris has previously been negatively correlated with MMSE scores in PwPD. 4 Peptococcaeae was higher in our PD group. Lower levels of Peptococceae were linked to higher MoCA scores. Thus, a reason why PwPD may be experiencing worse cognition (lower MoCA scores) is because of a higher concentration of Peptococceae. Importantly, Peptococcaceae has previously been linked to dyskinesia, so a higher level may induce motor impairments and worse cognition. 222 Coprococcus comes was lower in our PD group. Lower levels were linked to lower MoCA scores as well. Thus, it can be inferred that increasing this species may help to improve cognition. It is important to note that lower levels of Coprococcus comes have previously been linked to akinesia in PwPD. 222 Thus, a higher level may reduce akinesia prevalence and improve cognition. 146 Bacterial Taxa Aerobic Fitness UPDRS-III SPPB MOCA RBANS PAC001164 sp. Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Clostridium_g24 Negative (r=-0.495, P<0.05) Peptococceae Firmicutes: Clostridia: Clostridiales Negative (r=- 0.412, p<0.05) FTRU g. Firmicutes: Clostridia: Clostridiales: Lachnospiraceae Negative (r=- 0.433, p<0.05) PAC000195 g. Firmicutes: Clostridia: Clostridiales: Lachnospiraceae Positive (r=0.489, p<0.05) LN913006 Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Blautia Positive (r=0.521, p<0.001) Blautia hensenii Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Blautia Positive (r=0.608, p<0.001) PAC001607 sp. Firmicutes: Clostridia: Clostridiales: Ruminococcaceae: FTRU_g Negative (r=- 0.509, p<0.05) Coprococcus comes Firmicutes: Clostridia: Clostridiales: Lachnospiraceae: Coprococcus_g2 Positive (r=0.402, p<0.05) Positive (r=0.484, p<0.05) Clostridium ranosum Firmicutes: Erysipelotrichi: Erysipelotrichales: Erysipelotrichaceae: Clostridium_g6 Negative (r=-0.411, p<0.05) Actinobacteria Negative (r=- 0.417, p<0.05) 147 Appendix 10: Future Research—β-Diversity Beta Diversity Data: Species Dissimilarities (β-Diversity): Two indices were used to measure between-group bacterial community dissimilarity via principal coordinates analyses (PCoA): Jensen-Shannon β-diversity and Bray-Curtis β-diversity. 0 indicates that the two groups are similar, whereas 1 indicates that the two groups are entirely different. The Jensen-Shannon formula does not consider sample size differences, whereas Bray-Curtis adjusts the formula to account for sample size differences. • The Bray-Curtis formula is as follows: • The Jensen-Shannon formula is as follows: Bray-Curtis Diversity Index = (Σ |ai - bi|) / (Σ ai + Σ bi) ai = abundance of the ith species in the first site bi = abundance of the ith species in the second site A) B) Figure 1: Species dissimilarity Principal Component Analyses (PCoA) between PD and HC groups. Abbreviations: PC, principal component; p<.05 148 A PERMANOVA was employed for each model to compare groups with 999 permutations. Figure 1 displays species dissimilarity (β -diversity) between groups. First, 5a shows the Jensen-Shannon β-diversity model, with a statistically significant difference between the HC and PD groups. (pseudo-F= 1.585, p= .034). Since the Jensen-Shannon β-diversity model does not adjust for the sample size differences between groups, the Bray-Curtis β-diversity index was employed (see Figure 5b), and a trending difference was seen (pseudo-F= 1.310, p= .07). In particular, several studies similarly found no significant difference in -diversity indices between PD and HC groups, 87,89,92,100,111,238,239 whereas Bray-Curtis 3,6,9,11-13,17 and Jensen-Shannon 240 β- diversity indices have been shown to be significantly different. Jensen Shannon Diversity Index = (1/2) * [KL(P || M) + KL(Q || M)] KL = Kullback-Leibler divergence between two probability distributions P = probability distribution of species abundance in the first site Q = probability distribution of species abundance in the second site M = the average of P and Q, i.e., (P + Q)/2

Abstract (if available)

Abstract While Parkinson’s disease (PD) has been considered a brain disorder, PD is also influenced by gut microbiota alterations or dysbiosis. PwPD suffer from dysbiosis linked to symptomology, although more evidence is warranted. PD treatments that target the gut microbiota also need to be considered. One potential treatment strategy identified in non-PD populations is increasing fitness status. This dissertation aimed to determine the impact of fitness status on gut health and clarify associations between the gut microbiota and symptomology in PwPD. Study 1 correlated gut health and fitness status (estimated VO2). A robust linear regression revealed a positive relationship between VO2 and Shannon’s and Simpson's α-diversity gut composite scores. Study 2 correlated gut health and motor symptoms (Unified Parkinson’s Disease Rating Scale III, UPDRS-III; Short Physical Performance Battery, SPPB) in PwPD. UPDRS-III was inversely associated with Abundance Coverage Estimator (ACE) gut composite score (via robust linear regression). Study 3 correlated gut health and cognition (Montreal Cognitive Assessment, MoCA; and Repeatable Battery Assessment of Neuropsychological Status, RBANS) in PwPD. While gut health was not associated with total MoCA or RBANS scores, Orientation scores (MoCA) were positively associated with ACE, and Visuospatial scores (RBANS) were inversely associated with the Phylogenetic Diversity gut composite score (via robust linear regressions). Several bacterial taxa were also associated with motor and cognitive scores. This dissertation was the first to identify associations between gut health and fitness status and provides the rationale for future exercise intervention research to improve the gut microbiota in PwPD.

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Creator Zapanta, Kaylie Rebekah-Marsh (author)

Core Title Exploring the gut-brain axis in Parkinson’s disease and the influence of physical fitness to restore gut health

School School of Dentistry

Degree Doctor of Philosophy

Degree Program Biokinesiology and Physical Therapy

Degree Conferral Date 2023-08

Publication Date 06/30/2023

Defense Date 06/30/2023

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Tag aerobic fitness,cognition,exercise physiology,gut microbiota,gut-brain axis,Montreal cognitive assessment,motor function,OAI-PMH Harvest,Parkinson's disease,repeatable battery assessment of neuropsychological status,short physical performance battery,six minute walk test,unified Parkinson's disease rating scale

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