Statistical Analysis of Risk Factors with Overweight-Diabetes and Gut Microbiota,
a Bibliometric Analysis Using Bibliometrix
Tell-Morinson Melba* Menco-Tovar Andrea, Barrero-Jiménez Erika, Moreno-Novoa Melissa and Mendez-Ramos Maria
Statistical Research and Applied Mathematical Modeling Group (GEMMA), Department of Mathematics, Faculty of Education and Sciences, University of Sucre, Colombia
*Corresponding Author: Tell-Morinson Melba, Statistical Research and Applied Mathematical Modeling Group (GEMMA), Department of Mathematics, Faculty of Education and Sciences, University of Sucre, Colombia.
Received:
October 28, 2023; Published: December 04, 2023
Abstract
Sceintific production on chronic non-communicable diseases such as overweight, obesity and diabetes has grown in recent years. However, there is no comprehensive overview of the study designs and statistical analysis methodologies commonly used for risk assessment of research conducted on this topic. In order to evaluate risk assessment in this field, 1190 relevant documents downloaded using markers were included: ("Risk-Factors Associated with Overweight" or "Risk-Factors Associated with Obesity" or "Risk-Factors Associated with diabetes") and (microbiome or diabetes) and "multivariate analysis", in publications retrieved between 2017-2022 from the PubMed database; Specific parameters of title, journal, year of publication, authors, country of origin, institution, authors, keywords, among others, were analyzed. Data analysis was performed in three stages: 1. Descriptive; 2. Networking for Bibliographic Coupling Analysis (Network); 3. Strength of association of bibliographic coupling (Normalization). Data Visualization comprised: a. A mapping of the conceptual structure with Multiple Correspondence Analysis (MCA) for qualitative variables; b Network mapping. Grouping (Clustering) of K-means to identify groups of documents that express common concepts. To automate the data analysis and visualization stages, the open source tool (bibliometrix R-package), developed in R language, was used. 68.6% of the variability of the information was explained in the first factorial plane ACM, the cluster (cluster) of K-means identified two groups of documents that express common concepts. Cross-sectional, case-control, cohort and clinical trials are presented. "Risk" defined as the probability of occurrence of a clinical event. Use of models such as: logistic regression to relate explanatory variables of the risk of the occurrence of the event over a period of time; or, Cox proportional hazards regression, to relate explanatory variables of the conditional instantaneous risk of the event; Diagnostic tests for the detection of the clinical event (sensitivity, specificity, predictive value and likelihood ratio) and the use of the ROC curve.
Keywords: Gut Microbiota; Overweight; Obesity; Diabetes; Multivariate Analysis
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