The relationship between glycosylated hemoglobin, time-in-range and glycemic variability in type 1 diabetes patients under flash glucose monitoring

Díaz-Soto, Gonzalo; Bahíllo‐Curieses, M. Pilar; Jimenez, Rebeca; Nieto, Maria de la O.; Gómez, E. Tamayo; Torres, Beatriz; Gómez, Juan José López; Luis, Daniel de

doi:10.1016/j.endien.2021.11.006

Cited by 8 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, to our knowledge, this is the first study that investigates the consistency of these relationships in an actual clinical practice setting using isCGM and a pediatric population. It also stratifies the GRI correlation results according to GV, whose influence has already been demonstrated in the relationship between TIR and HbA1c by different authors [ 9 , 10 ].…”

Section: Discussionmentioning

confidence: 83%

“…Due to both HbA1c and TIR being parameters of centrality, several studies have shown a strong negative relationship between the two variables [ 7 , 8 ]. However, this correlation is directly influenced by the glycemic variability (GV) measured as the coefficient of variation (CV) in both T1D and type 2 diabetes mellitus (T2D) [ 9 , 10 ]. In addition, TIR does not show sufficient sensitivity in the hypoglycemia range or in extreme glycemia values [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

Pérez-López,

Férnandez-Velasco,

Bahillo-Curieses

et al. 2023

Endocrine

View full text Add to dashboard Cite

Objective To evaluate the impact of glucose variability on the relationship between the GRI and other glycemic metrics in a cohort of pediatric and adult patients with type 1 diabetes (T1D) using intermittent scanning continuous glucose monitoring (isCGM). Methods We performed a cross-sectional study of 202 patients with T1D under intensive insulin treatment (25.2% CSII) using isCGM. Clinical, metabolic, and glycemic metrics were collected, and the GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. The correlation between the GRI and other classical glycometrics in relation to the coefficient of variation (CV) was evaluated. Results A total of 202 patients were included (53% male; 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution (TIR 59.0 ± 17.0%; CV 39.8 ± 8.0%; GMI 7.3 ± 1.1%). The mean GRI was 54.0 ± 23.3 with a CHypo and CHyper component of 5.7 ± 4.8 and 23.4 ± 14.3, respectively. A strong negative correlation was observed between the GRI and TIR (R = −0.917; R2 = 0.840; p < 0.001), showing differences when dividing patients with low glycemic variability (CV < 36%) (R = −0.974; R2 = 0.948; p < 0.001) compared to those with greater CV instability (≥36%) (R = −0.885; R2 = 0.784; p < 0.001). The relationship of GRI with its two components was strongly positive with CHyper (R = 0.801; R2 = 0.641; p < 0.001) and moderately positive with CHypo (R = 0.398; R2 = 0.158; p < 0.001). When the GRI was evaluated with the rest of the classic glycemic metrics, a strong positive correlation was observed with HbA1c (R = 0.617; R2 = 0.380; p < 0.001), mean glucose (R = 0.677; R2 = 0.458; p < 0.001), glucose standard deviation (R = 0.778; R2 = 0.605; p < 0.001), TAR > 250 (R = 0.801; R2 = 0.641; p < 0.001), and TBR < 54 (R = 0.481; R2 = 0.231; p < 0.001). Conclusions The GRI correlated significantly with all the glycemic metrics analyzed, especially with the TIR. Glycemic variability (GV) significantly affected the correlation of the GRI with other parameters and should be taken into consideration.

show abstract

Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

Pérez-López,

Férnandez-Velasco,

Bahillo-Curieses

et al. 2023

Endocrine

View full text Add to dashboard Cite

show abstract

“… In patients with DM2 || , a time on target of 70% corresponded to an HbA1c ‡ of 7.1%. Díaz-Soto, et al (2021) ( 35 ) Endocrinology , Diabetes and Nutrition (1,833) Spain Cross-sectional study 195 people with DM1* (70 <20 years and 125 >20 years) To evaluate the relationship between HbA1c ‡ , Time on Target and glycemic variability in a cohort of pediatric and adult patients with DM1* and CBGM † . CBGM † 90-day blood glucose data.…”

Section: Resultsmentioning

confidence: 99%

“…In the present systematic review, it was possible to highlight that all the studies analyzed showed a correlation between time on target (70-180 mg/dl) and HbA1c. Using Spearman ( 22 , 25 , 30 , 34 ) and Pearson ( 27 - 28 , 35 - 37 ) coefficients, the correlation ranged from -0.310 to -0.766 and from -0.623 to -0.869, respectively. Regression models also indicated a significant linear relationship between time on target and HbA1c ( 23 - 24 , 26 , 29 , 31 - 33 ) .…”

Section: Discussionmentioning

confidence: 99%

Correlation between time on target and glycated hemoglobin in people with diabetes mellitus: systematic review

Lima,

Fernandes,

Garcia

et al. 2023

Rev. Latino-Am. Enfermagem

View full text Add to dashboard Cite

Objective: to analyze the correlation between time on target and glycated hemoglobin in people living with diabetes mellitus and carrying out continuous blood glucose monitoring or self-monitoring of capillary blood glucose. Method: systematic review of etiology and risk based on JBI guidelines and reported according to Preferred Reporting Items for Systematic Reviews and Meta- Analyses, covering six databases and grey literature. The sample included 16 studies and methodological quality was assessed using JBI tools. Protocol registered in the Open Science Framework, available at https://doi.org/10.17605/OSF.IO/NKMZB. Results: time on target (70-180 mg/dl) showed a negative correlation with glycated hemoglobin, while time above target (>180 mg/dl) showed a positive correlation. Correlation coefficients ranged between -0.310 and -0.869 for time on target, and between 0.66 and 0.934 for time above target. A study was carried out on a population that performed self-monitoring. Conclusion: there is a statistically significant correlation between time on target and time above target with glycated hemoglobin. The higher the proportion in the adequate glycemic range, the closer to or less than 7% the glycated hemoglobin will be. More studies are needed to evaluate this metric with data from self-monitoring of blood glucose.

show abstract

“…The aforementioned discordance might be explained by a reduction in circulation of subcutaneous fat tissue leading to less diffusion in the sensing area [19,20]. The other hypothesis is that it is due to interstitial oedema and increased tissue inflammation associated with obesity and T2D [21,22]. Both hypotheses indicate the need to take into account excessively developed adipose tissue in patients in the context of GMI reliability.…”

Section: Adipose Tissuementioning

confidence: 99%

Glucose management indicator – potential factors affecting differences in comparison with HbA1c and clinical significance of this phenomenon

Wybrańczyk,

Brudzińska,

Jarosz-Chobot

et al. 2023

pedm

View full text Add to dashboard Cite

HbA1c and the glucose management indicator (GMI) are now widely recognized as the gold standard for assessing the effectiveness of diabetes therapy. The GMI is a result of a mathematical formula used to estimate HbA1c with continuous glucose monitoring (CGM) data in patients with diabetes. It is a useful parameter providing a good estimate of CGM metrics for a 3-month period with just 10-14 days of data. It can reflect the effectiveness of administered/modified therapy of insulin faster than traditional measurements of laboratory HbA1c and can be used as an educational tool and in telemedicine. Unfortunately, there are significant discordances between GMI and laboratory-measured HbA1c, reaching as much as 0.5-1% in many patients. It may be caused by well-known factors influencing HbA1c measurement and red blood cell turnover like anaemia, haemoglobinopathies, or certain medications, but these causes of potential errors are too rare in everyday practice to account for the amount of discrepancy reported in the literature. In this article we would like to review other new potential factors that may influence HbA1c estimation based on GMI and cause potential differences between results calculated from CGM and those measured in the laboratory. In addition, we will present clinical implications of the observed differences between GMI and HbA1c. Recognizing the factors that cause such differences is an important clinical skill. Moreover, understanding the principles of evaluating paired measures of these parameters will allow us to individualize the treatment of each person with diabetes.

show abstract

The relationship between glycosylated hemoglobin, time-in-range and glycemic variability in type 1 diabetes patients under flash glucose monitoring

Cited by 8 publications

References 14 publications

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

Correlation between time on target and glycated hemoglobin in people with diabetes mellitus: systematic review

Glucose management indicator – potential factors affecting differences in comparison with HbA1c and clinical significance of this phenomenon

Contact Info

Product

Resources

About