Identifying glycemic variability in diabetes patient cohorts and evaluating disease outcomes

Martin C. Nwadiugwu, Dhundy R. Bastola, Christian Haas, Doug Russell

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Glycemic variability (GV) is an obstacle to effective blood glucose control and an au-tonomous 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 anal-ysis, 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.

Original languageEnglish (US)
Article number1477
JournalJournal of Clinical Medicine
Volume10
Issue number7
DOIs
StatePublished - Apr 1 2021

Keywords

  • Comorbidity
  • Diabetes mellitus
  • Glycemic variability
  • HbA1c

ASJC Scopus subject areas

  • Medicine(all)

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