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Scholarly Works (7 results)

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UC San Diego Electronic Theses and Dissertations (2024)

There is increasing recognition that the gut microbiome may influence the development and progression of neurological diseases. Microbes in the digestive tract and their metabolic products are known to regulate and modulate metabolic and immunological effectors of neurological disease through the gut microbiota-brain axis. These microbes and their chemicals are currently being researched in both human and animal models. Just as the microbiome changes throughout the lifespan, the gut-microbiota brain axis is relevant in neurological diseases affecting all ages of humans, such as neurodevelopmental disorders in young children, mood and neuropsychological disorders in young adults, and neurodegenerative disorders in older adults. This thesis addresses all three classes of neurological disorders across the lifespan. While several correlative studies have already been published in the scientific literature, this thesis addresses additional factors relevant to the gut-microbiota brain axis. Specifically, lifestyle exposures that are associated with neurological disorders including diet and medication, which are strongly intertwined with the gut microbiome, are considered. Furthermore, the scope of the gut microbiome-induced changes is augmented with the addition of an untargeted metabolomics data layer. This thesis makes use of the latest multi-omics integration technology as well as updated compositional data analysis techniques to ensure its statistics are robust. In the first part of this thesis, the typical microbiota shifts that occur across the human lifespan are outlined and biological and environmental factors that can disrupt the gut microbiome are proposed. Next, microbiome and metabolomic analyses of Pitt Hopkins Syndrome, a severe autism spectrum disorder, are presented. In chapter three, we demonstrate that while medication use has a larger effect on the microbiome than neuropsychiatric disorders, there are still reproducible microbial markers of neuropsychiatric disorders in young adults. Finally, we examine changes to the microbiome and metabolome after adults at risk of cognitive impairment are put through both a low-fat and ketogenic dietary intervention. Signals for both dietary change and risk of cognitive impairment are identified using a linear mixed effects model with Bayesian Inferential regression.

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UC San Diego Previously Published Works (2023)

Objective

The goal of this study was to examine if mental health and psychosocial well-being differed between middle-aged (MA; 40-59 years), younger-old (YO; 60-79 years), and older-old (OO; 80+ years) adults with respect to their trends, heterogeneity, and correlates.

Methods

Eighteen mental health and psychosocial well-being instruments were administered to 590 adults over age 40. Cross-sectional data also included self-report-based measures of sociodemographics, cognitive functioning, physical health and activity, and body mass index.

Results

Age trends across instruments varied in magnitude and shape, but generally supported an inverted U-shaped trend in mental health and psychosocial well-being, with small increases from MA to YO age (d = 0.29) and smaller declines from YO to OO age (d = -0.17). A U-shaped association between age and mental health heterogeneity was also observed. The strongest correlates of mental health and psychosocial well-being differed by age (MA: perceived stress; YO: successful aging; OO: compassion toward others), as did the associations of a flourishing versus languishing mental health and well-being profile.

Conclusions

Our findings support the "paradox of aging," whereby declines in physical and cognitive health co-occur with relatively preserved mental health and well-being. Our findings indicate that variance in mental and psychosocial health does not increase linearly with age and support careful consideration of heterogeneity in mental health and aging research. Our findings also suggest that mental health and psychosocial well-being decouple from stress-related dimensions in MA and become increasingly associated with positive, other-oriented emotions in OO, broadly supporting socioemotional theories of aging.
    Cover page: Trends, heterogeneity, and correlates of mental health and psychosocial well-being in later-life: study of 590 community-dwelling adults aged 40–104 years
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    UC San Diego Previously Published Works (2023)
    Herein, we present a tool called Evident that can be used for deriving effect sizes for a broad spectrum of metadata variables, such as mode of birth, antibiotics, socioeconomics, etc., to provide power calculations for a new study. Evident can be used to mine existing databases of large microbiome studies (such as the American Gut Project, FINRISK, and TEDDY) to analyze the effect sizes for planning future microbiome studies via power analysis. For each metavariable, the Evident software is flexible to compute effect sizes for many commonly used measures of microbiome analyses, including α diversity, β diversity, and log-ratio analysis. In this work, we describe why effect size and power analysis are necessary for computational microbiome analysis and show how Evident can help researchers perform these procedures. Additionally, we describe how Evident is easy for researchers to use and provide an example of efficient analyses using a dataset of thousands of samples and dozens of metadata categories.
      Cover page: Determination of Effect Sizes for Power Analysis for Microbiome Studies Using Large Microbiome Databases.
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      UC San Diego Previously Published Works (2021)
      Alterations to the gut microbiome have been reported between children with autism spectrum disorders (ASDs) and typically developing (TD) children. Characterizing these differences has led to the proposal of new treatments for ASD, such as probiotic interventions and fecal matter transplants. However, no study to date has characterized the gut microbiome or metabolome in Pitt Hopkins syndrome (PTHS), a severe ASD with a high incidence of gastrointestinal (GI) disturbances such as constipation. Here, we surveyed the gut microbiome and metabolome in a cohort of PTHS individuals and their unaffected parents. We focused our analysis on Clostridium bolteae, a microbe previously associated with ASD known to chemically modify bile acids in the gut. PTHS individuals carry a higher load of C. bolteae than their parents as well as both ASD and non-ASD individuals from the American Gut Project cohort. Specific metabolites were associated with PTHS, including bile acids and sphingosines. With a metadata reanalysis tool, we found that PTHS-associated metabolites have previously been identified in inflammatory bowel disease and obesity patients. These results suggest microbial involvement in PTHS, but further research must be performed to clarify the exact mechanisms through which microbes may act. Furthermore, new associations between PTHS-specific metabolites and other conditions may lead to additional therapeutic options for PTHS individuals. IMPORTANCE GI disturbances in ASD such as severe constipation can be medically significant and often require medication. This is especially true for individuals with PTHS, suggesting that the gut microbiome may be involved in PTHS's pathology. Revealing associations between specific gut microbes and PTHS may allow the development of new therapeutics or the application of existing therapeutics to ease day-to-day challenges encountered by PTHS individuals. In this study, we characterized an association between C. bolteae and PTHS, in addition to metabolites linked to both PTHS and C. bolteae. We also identified other microbiome-involved medical conditions where PTHS-associated metabolites have been isolated. Utilizing common metabolites to identify conditions with similar phenotypes may suggest new therapeutic options for GI-related symptoms.
        Cover page: The Fecal Microbiome and Metabolome of Pitt Hopkins Syndrome, a Severe Autism Spectrum Disorder
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        UC San Diego Previously Published Works (2022)
        Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data. IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.
          Cover page: Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype
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          UC San Diego Previously Published Works (2023)
          INTRODUCTION: The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimers disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD-mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low-fat-diet intervention. METHODS: Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low-fat American Heart Association diet or high-fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6-week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals microbiome and metabolome throughout the dietary interventions. RESULTS: Participants with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila. MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase-containing microbes and an altered bile acid pool, suggesting reduced gut motility. DISCUSSION: Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut-transit time.
            Cover page: Effects of a ketogenic and low-fat diet on the human metabolome, microbiome, and foodome in adults at risk for Alzheimers disease.
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            UC San Diego Previously Published Works (2024)
            Alzheimers disease (AD) is influenced by a variety of modifiable risk factors, including a persons dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KDs metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.
              Cover page: Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimers disease.
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