{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:14:30Z","timestamp":1791951270087,"version":"4.3.4"},"reference-count":52,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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\u2019s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal\u2013Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p &lt; 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid\u2013base 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.<\/jats:p>","DOI":"10.3390\/jcm10071477","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T10:34:09Z","timestamp":1617359649000},"page":"1477","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-8305","authenticated-orcid":false,"given":"Martin C.","family":"Nwadiugwu","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, University of Nebraska at Omaha, Omaha, NE 68182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhundy R.","family":"Bastola","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Nebraska at Omaha, Omaha, NE 68182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Haas","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Quantitative Analysis, University of Nebraska at Omaha, Omaha, NE 68182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doug","family":"Russell","sequence":"additional","affiliation":[{"name":"Think Whole Person Healthcare, Omaha, NE 68106, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"ref_1","unstructured":"Vaisman, N., and Mordechai, K. 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