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

References

1. Vertel-Morinson M., et al. “Rural Education in Sucre - Dissertation from Multivariate Analysis”. Editorial Universidad de Sucre (2018).
2. Kawuki J., et al. “A bibliometric analysis of childhood obesity research from China indexed in Web of Science”. Journal of Public Health and Emergency 5 (2021): 3.
3. Rousseau DM. “The Oxford handbook of evidence-based management”. Oxford University Press (2012).
4. Vertel-Morinson M., et al. “Sociodemographic and Parasitological Factors Determining Learning Capacity and Nutritional Status in Rural Schoolchildren: Data Mining for Decision Making". Acta Scientific Nutritional Health12 (2022): 15-22.
5. Trueba-Gómez R., et al. “The PubMed database and the search for scientific information”. Seminarios de la Fundación Española de Reumatología 11 (2010): 49-63.
6. Aria M and Cuccurullo C. “Bibliometrix: An R-tool for comprehensive science mapping análisis”. Journal of Informetrics 11 (2011): 959-975.
7. Newman ME. “Scientific collaboration networks. I. Network construction and fundamental results”. Physical Review E 1 (2001): 016131.
8. Cobo MJ., et al. “SciMAT: A new science mapping analysis software tool”. Journal of the American Society for Information Science and Technology8 (2012): 1609-1630.
9. Vertel M., et al. “Multivariate Data Analysis. Application: Dual Purpose Production System". Ediciones Universidad Simón Bolívar, a publishing house recognized by COLCIENCIAS. 223 Book: research result (2016).
10. Vertel M and Pardo C-E. “Comparison between canonical correspondence analysis and multiple factor analysis in continuous frequencies-variable tables”. Master's thesis, Universidad Nacional de Colombia, Bogotá (2010).
11. Dray S., et al. “Co‐inertia analysis and the linking of ecological data tables”. Ecology11 (2003): 3078-3089.
12. Moral-Muñoz JA., et al. “Software tools for conducting bibliometric analysis in science: an up-to-date review”. El Profesional de la Información1 (2020): e290103.
13. R Core Team. “R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2021).
14. Chen C. “CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literatura”. Journal of the Association for Information Science and Technology 57 (2006): 359-377.
15. Chung Y., et al. “Serum cystatin C is associated with subclinical aterosclerosis in patients with type 2 diabetes: A restrospective study” (2017).
16. Chandradas S Khalili., et al. “Does Obesity Influence the Risk of Clostridium difficile Infection Among Patients with Ulcerative Colitis?” Digestive Diseases and Sciences 9 (2018): 2445-2450.
17. Llanera D Wilmington., et al. “Clinical Characteristics of COVID-19 Patients in a Regional Population With Diabetes Mellitus: The ACCREDIT Study. Frontiers in Endocrinology 13 January 2022. Sec. Clinical Diabetes. This article is part of the Research Topic. Covid-19 and Diabetes - Volume II (2022).
18. Ávila EE., et al. “High Relative Abundance of Lactobacillus reuteri and Fructose Intake are Associated with Adiposity and Cardiometabolic Risk Factors in Children from Mexico City”. Nutrients 11 (2019): 1207.
19. Stanislawski M., et al. “Gut microbiota in the first 2 years of life and the association with body mass index at age 12 in a Norwegian birth cohort”. mBio 9 (2018): e01751-18.
20. Mirpuri J. “Evidence for maternal diet-mediated effects on the offspring microbiome and immunity: implications for public health initiatives”. Pediatric Research 2 (2020): 301-306.
21. Herrera A and López M. “Childhood obesity: current situation in Mexic”. Frontiers in Public Health 10 (2018): 949893.
22. Needell JIr., et al. “Maternal treatment with short-chain fatty acids modulates the intestinal microbiota and immunity and ameliorates type 1 diabetes in the offspring”. PLOS One (2017).
23. Fu CP Lee., et al. “Metformin as a potential protective therapy against tuberculosis in patients with diabetes mellitus: A retrospective cohort study in a single teaching hospital”. Journal of Diabetes Investigation 9 (2021): 1603-1609.
24. Ndahayo Sophonie., et al. “Risk-Factors Associated with Overweight and Obesity Among Adolescents in Selected Urban and Peri-Urban Secondary Schools in Monze, Zambia”. Acta Scientific Nutritional Health8 (2022).
25. VanWagner L., et al. “Alcohol use and cardiovascular disease risk in patients with nonalcoholic fatty liver disease”. Gastroenterology5 (2017): 1260-1272.
26. Van Eck NJ and Waltman L. “Software survey: VOSviewer, a computer program for bibliometric mapping”. Scientometrics 84 (2010): 523-538.
27. Wang Z., et al. “Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity”. Genome Biology 22 (2021): 336.
28. Yokoyama H Naga., et al. “Incidence and risk of vaginal candidiasis associated with sodium-glucose cotransporter 2 inhibitors in real-world practice for women with type 2 diabetes”. Journal of Diabetes Investigation 2 (2018): 439-445.
29. , et al. “Early carotid endarterectomy performed 2 to 5 days after the onset of neurologic symptoms leads to comparable results to carotid endarterectomy performed at later time points”. Journal of Vascular Surgery 66.5 (2017): 1719-1726.
30. , et al. “Preoperative anemia associated with adverse outcomes after infrainguinal bypass surgery in patients with chronic limb-threatening ischemia”. Journal of Vascular Surgery 66.6 (2017): 1775-1785.
31. Ricco J., et al. “Impact of angiosome- and nonangiosome-targeted peroneal bypass on limb salvage and healing in patients with chronic limb-threatening ischemia”. Journal of Vascular Surgery 5 (2017): 1479-1487.
32. Stavroulakis K., et al. “Association between statin therapy and amputation-free survival in patients with critical limb ischemia in the CRITISCH registry”. Journal of Vascular Surgery5 (2017): 1534-1542.
33. Hong X., et al. “Prevalence and clustering of cardiovascular risk factors: a crosssectional survey among Nanjing adults in China (2018).
34. Morris N., et al. “Differential Impact of Malnutrition on Health Outcomes Among Indigenous and Non-Indigenous Adults Admitted to Hospital in Regional Australia-A Prospective Cohort Study (2018).
35. Baber U., et al. “The association of postoperative glycemic control and lower extremity procedure outcomes (2018).
36. Gupta A., et al. “Real-world evidence of superiority of endovascular repair in treating ruptured abdominal aortic anerysm (2018).
37. Reed G., et al. “Hemodynamic Assessment before and after endovascular therapy for critical limb ischemia and association with clinical outcomes (2017).
38. Efron B and Hastie T. “Computer age statistical inference, student edition: algorithms, evidence, and data science (Vol. 6). Cambridge University Press (2021).
39. Jombart T., et al. “Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data”. PLoS Computational Biology1 (2014): e1003457.
40. Vertel M., et al. “Multivariate analysis of the quality of education in Sucre”. Scientia et Technica1 (2014): 96-105.

Citation

Citation: Tell-Morinson Melba., et al. “Statistical Analysis of Risk Factors with Overweight-Diabetes and Gut Microbiota, a Bibliometric Analysis Using Bibliometrix".Acta Scientific Nutritional Health 8.1 (2023): 02-11.

Copyright

Copyright: © 2023 Tell-Morinson Melba., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.