Literature DB >> 33707491

Oscillatory pattern of glycemic control in patients with diabetes mellitus.

Manuel Vasquez-Muñoz1,2, Alexis Arce-Alvarez3, Magdalena von Igel4, Carlos Veliz4, Gonzalo Ruiz-Esquide1, Rodrigo Ramirez-Campillo4,5, Cristian Alvarez5, Robinson Ramirez-Velez2,6, Fernando A Crespo7, Mikel Izquierdo2,6, Rodrigo Del Rio8,9,10, David C Andrade11,12,13.   

Abstract

Daily glucose variability is higher in diabetic mellitus (DM) patients which has been related to the severity of the disease. However, it is unclear whether glycemic variability displays a specific pattern oscillation or if it is completely random. Thus, to determine glycemic variability pattern, we measured and analyzed continuous glucose monitoring (CGM) data, in control subjects and patients with DM type-1 (T1D). CGM data was assessed for 6 days (day: 08:00-20:00-h; and night: 20:00-08:00-h). Participants (n = 172; age = 18-80 years) were assigned to T1D (n = 144, females = 65) and Control (i.e., healthy; n = 28, females = 22) groups. Anthropometry, pharmacologic treatments, glycosylated hemoglobin (HbA1c) and years of evolution were determined. T1D females displayed a higher glycemia at 10:00-14:00-h vs. T1D males and Control females. DM patients displays mainly stationary oscillations (deterministic), with circadian rhythm characteristics. The glycemia oscillated between 2 and 6 days. The predictive model of glycemia showed that it is possible to predict hyper and hypoglycemia (R2 = 0.94 and 0.98, respectively) in DM patients independent of their etiology. Our data showed that glycemic variability had a specific oscillation pattern with circadian characteristics, with episodes of hypoglycemia and hyperglycemia at day phases, which could help therapeutic action for this population.

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Year:  2021        PMID: 33707491      PMCID: PMC7970978          DOI: 10.1038/s41598-021-84822-5

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  34 in total

Review 1.  Glucose variability: An emerging target for the treatment of diabetes mellitus.

Authors:  Simona Frontoni; Paolo Di Bartolo; Angelo Avogaro; Emanuele Bosi; Giuseppe Paolisso; Antonio Ceriello
Journal:  Diabetes Res Clin Pract       Date:  2013-09-25       Impact factor: 5.602

2.  Glucocorticoid-induced hyperglycemia.

Authors:  Anitha C Litty; Susan Chaney
Journal:  Nurse Pract       Date:  2017-08-17

3.  Association Between Age at Natural Menopause and Risk of Type 2 Diabetes in Postmenopausal Women With and Without Obesity.

Authors:  Jiajia Jiang; Jia Cui; Anping Wang; Yiming Mu; Yuxiang Yan; Fen Liu; Yuesong Pan; Dongxue Li; Wei Li; Guangxu Liu; Herbert Y Gaisano; Jingtao Dou; Yan He
Journal:  J Clin Endocrinol Metab       Date:  2019-07-01       Impact factor: 5.958

Review 4.  Circadian clock control of endocrine factors.

Authors:  Karen L Gamble; Ryan Berry; Stuart J Frank; Martin E Young
Journal:  Nat Rev Endocrinol       Date:  2014-05-27       Impact factor: 43.330

5.  Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes.

Authors:  Giovanni Sartore; Nino Cristiano Chilelli; Silvia Burlina; Annunziata Lapolla
Journal:  Acta Diabetol       Date:  2013-02-16       Impact factor: 4.280

6.  Metabolic Correction in Patients Sample with Diabetes: Clinical Outcomes and Costs Reductions.

Authors:  Jorge R Miranda-Massari; José R Rodríguez-Gómez; Michael J González; Carlos Cidre; Jorge Duconge; Heriberto Marín; Kazuko Grace; Howard L McLeod
Journal:  Int J Diabetes Res       Date:  2016

7.  Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes.

Authors:  Boris Kovatchev; Claudio Cobelli
Journal:  Diabetes Care       Date:  2016-04       Impact factor: 19.112

8.  Does glucose variability influence the relationship between mean plasma glucose and HbA1c levels in type 1 and type 2 diabetic patients?

Authors:  Judith C Kuenen; Rikke Borg; Dirk J Kuik; Hui Zheng; David Schoenfeld; Michaela Diamant; David M Nathan; Robert J Heine
Journal:  Diabetes Care       Date:  2011-06-23       Impact factor: 19.112

9.  Predictive models for diabetes mellitus using machine learning techniques.

Authors:  Hang Lai; Huaxiong Huang; Karim Keshavjee; Aziz Guergachi; Xin Gao
Journal:  BMC Endocr Disord       Date:  2019-10-15       Impact factor: 2.763

Review 10.  Glycemic Variability: How Do We Measure It and Why Is It Important?

Authors:  Sunghwan Suh; Jae Hyeon Kim
Journal:  Diabetes Metab J       Date:  2015-08       Impact factor: 5.376

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  1 in total

1.  A diabetic milieu increases ACE2 expression and cellular susceptibility to SARS-CoV-2 infections in human kidney organoids and patient cells.

Authors:  Elena Garreta; Patricia Prado; Megan L Stanifer; Vanessa Monteil; Andrés Marco; Asier Ullate-Agote; Daniel Moya-Rull; Amaia Vilas-Zornoza; Carolina Tarantino; Juan Pablo Romero; Gustav Jonsson; Roger Oria; Alexandra Leopoldi; Astrid Hagelkruys; Maria Gallo; Federico González; Pere Domingo-Pedrol; Aleix Gavaldà; Carmen Hurtado Del Pozo; Omar Hasan Ali; Pedro Ventura-Aguiar; Josep María Campistol; Felipe Prosper; Ali Mirazimi; Steeve Boulant; Josef M Penninger; Nuria Montserrat
Journal:  Cell Metab       Date:  2022-05-12       Impact factor: 31.373

  1 in total

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