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Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes

Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes wi...

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Autores principales: Nwadiugwu, Martin C., Bastola, Dhundy R., Haas, Christian, Russell, Doug
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038275/
https://www.ncbi.nlm.nih.gov/pubmed/33918347
http://dx.doi.org/10.3390/jcm10071477
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author Nwadiugwu, Martin C.
Bastola, Dhundy R.
Haas, Christian
Russell, Doug
author_facet Nwadiugwu, Martin C.
Bastola, Dhundy R.
Haas, Christian
Russell, Doug
author_sort Nwadiugwu, Martin C.
collection PubMed
description Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher’s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal–Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid–base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus.
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spelling pubmed-80382752021-04-12 Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes Nwadiugwu, Martin C. Bastola, Dhundy R. Haas, Christian Russell, Doug J Clin Med Article Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher’s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal–Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid–base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus. MDPI 2021-04-02 /pmc/articles/PMC8038275/ /pubmed/33918347 http://dx.doi.org/10.3390/jcm10071477 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nwadiugwu, Martin C.
Bastola, Dhundy R.
Haas, Christian
Russell, Doug
Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes
title Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes
title_full Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes
title_fullStr Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes
title_full_unstemmed Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes
title_short Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes
title_sort identifying glycemic variability in diabetes patient cohorts and evaluating disease outcomes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038275/
https://www.ncbi.nlm.nih.gov/pubmed/33918347
http://dx.doi.org/10.3390/jcm10071477
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