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Big data analytics frameworks for the influence of gut microbiota on the development of tic disorder.

ABSTRACT: The association between gut microbiota and psychiatric disorders has received increasing research attention. Meanwhile, big data analysis has been utilized in many filed including business, human healthcare analysis, etc. The primary objective of this article was to provide insights into Big Data Analytics (BDA) to clarify the association between gut microbiota and TD (Tic disorder). Specifically, we investigated the recent studies related to gut microbiota composition differences in patients with TD compared to health people. We searched on PubMed and Embase (Ovid) databases for relevant published articles until June 15, 2021. A total of 78 TD and 62 health control stool samples were examined. Case-control design was applied in all the studies. No consensus was evident in α-diversity and β-diversity. The abundance of phyla Bacteroidetes and Firmicutes was predominant at the taxa level. Gut microbiota taxonomic differences were found between TD cases and controls, though inconsistently across studies. Further studies are needed to reveal the underlying pathophysiology of TD and correlation between TD and gut microbiota composition.

SUBMITTER: Fan F

PROVIDER: S-EPMC9452719 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Big data analytics frameworks for the influence of gut microbiota on the development of tic disorder.

Fan Fei F Bian Zhaoxiang Z Zhang Xuan X Wu Hongwei H Wang Simeng S Zhang Si S Wang Qiong Q Han Fei F

Frontiers in computational neuroscience 20220825

The association between gut microbiota and psychiatric disorders has received increasing research attention. Meanwhile, big data analysis has been utilized in many filed including business, human healthcare analysis, <i>etc</i>. The primary objective of this article was to provide insights into Big Data Analytics (BDA) to clarify the association between gut microbiota and TD (Tic disorder). Specifically, we investigated the recent studies related to gut microbiota composition differences in pati ...[more]

PMID: 36093417

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Project description:BackgroundObsessive-compulsive disorder (OCD) is characterized by recurrent intrusive thoughts, urges, or images (obsessions) and repetitive physical or mental behaviors (compulsions). Previous factor analytic and clustering studies suggest the presence of three or four subtypes of OCD symptoms. However, these studies have relied on predefined symptom checklists, which are limited in breadth and may be biased toward researchers' previous conceptualizations of OCD.ObjectiveIn this study, we examine a large data set of freely reported obsession symptoms obtained from an OCD mobile app as an alternative to uncovering potential OCD subtypes. From this, we examine data-driven clusters of obsessions based on their latent semantic relationships in the English language using word embeddings.MethodsWe extracted free-text entry words describing obsessions in a large sample of users of a mobile app, NOCD. Semantic vector space modeling was applied using the Global Vectors for Word Representation algorithm. A domain-specific extension, Mittens, was also applied to enhance the corpus with OCD-specific words. The resulting representations provided linear substructures of the word vector in a 100-dimensional space. We applied principal component analysis to the 100-dimensional vector representation of the most frequent words, followed by k-means clustering to obtain clusters of related words.ResultsWe obtained 7001 unique words representing obsessions from 25,369 individuals. Heuristics for determining the optimal number of clusters pointed to a three-cluster solution for grouping subtypes of OCD. The first had themes relating to relationship and just-right; the second had themes relating to doubt and checking; and the third had themes relating to contamination, somatic, physical harm, and sexual harm. All three clusters showed close semantic relationships with each other in the central area of convergence, with themes relating to harm. An equal-sized split-sample analysis across individuals and a split-sample analysis over time both showed overall stable cluster solutions. Words in the third cluster were the most frequently occurring words, followed by words in the first cluster.ConclusionsThe clustering of naturally acquired obsessional words resulted in three major groupings of semantic themes, which partially overlapped with predefined checklists from previous studies. Furthermore, the closeness of the overall embedded relationships across clusters and their central convergence on harm suggests that, at least at the level of self-reported obsessional thoughts, most obsessions have close semantic relationships. Harm to self or others may be an underlying organizing theme across many obsessions. Notably, relationship-themed words, not previously included in factor-analytic studies, clustered with just-right words. These novel insights have potential implications for understanding how an apparent multitude of obsessional symptoms are connected by underlying themes. This observation could aid exposure-based treatment approaches and could be used as a conceptual framework for future research.

Dataset Information

Big data analytics frameworks for the influence of gut microbiota on the development of tic disorder.

Publications

Big data analytics frameworks for the influence of gut microbiota on the development of tic disorder.

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