Association between glycaemia risk index (<scp>GRI</scp>) and diabetic retinopathy in type 2 diabetes: A cohort study

Wang, Yaxin; Lu, Jingyi; Ni, Jiaying; Wang, Ming; Shen, Yun; Lü, Wei; Zhu, Wei; Bao, Yuqian; Rodbard, David; Vigersky, Robert A.; Jia, Weiping; Zhou, Jian

doi:10.1111/dom.15068

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Consistent with these findings, a correlation between the GRI and long‐term diabetic microvascular complications has been demonstrated. In a cohort study involving 1,204 adults with type 2 diabetes, a higher glycemia risk index was associated with an increased risk of incident diabetic retinopathy 13 . Moreover, another study showed that the GRI was strongly associated with albuminuria and was an independent risk factor for macroalbuminuria 14 .…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, researchers also explored the association between the glycemia risk index and diabetes‐related complications. A higher GRI was shown to be associated with an increased risk of incident diabetic retinopathy 13 and albuminuria 14 . However, these studies focused primarily on microvascular complications, and no previous research has investigated the connection between glycemia risk index and macrovascular complications.…”

Section: Introductionmentioning

confidence: 97%

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Association between glycemia risk index and arterial stiffness in type 2 diabetes

Cai,

Shen,

et al. 2024

J of Diabetes Invest

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AimThis study aims to investigate the association of glycemia risk index (GRI), a novel composite metric derived from continuous glucose monitoring (CGM), with arterial stiffness in patients with type 2 diabetes.Materials and MethodsA total of 342 adults with type 2 diabetes were enrolled between April and June 2023 from 11 communities in Shanghai, China. Medical examinations, including measurements of anthropometric parameters, blood pressure, and venous blood samples were conducted. Brachial‐ankle pulse wave velocity (baPWV) was examined to evaluate arterial stiffness. All the participants underwent a 14 day CGM recording and GRI was calculated from the CGM data.ResultsThe mean age was 70.3 ± 6.8 years, and 162 (47.4%) were male. Participants with a higher baPWV had significantly higher levels of GRI and hyperglycemia component (both P for trend < 0.05). Linear regression revealed the significant positive linear associations of the GRI with baPWV in unadjusted or adjusted models (All P < 0.05). In the multivariable logistic analysis, each increase in the GRI quartile was associated with a 1.30‐fold (95% CI 1.01–1.68, P for trend < 0.05) higher prevalence of increased arterial stiffness after adjustment for age, sex, BMI, diabetes duration, current smoking status, blood pressure, and lipid profile. Subgroup analyses showed that the association between the GRI quartiles and increased arterial stiffness was stronger among participants with a diabetes duration ≥15 years (P for interaction = 0.014).ConclusionGlycemia risk index assessed by continuous glucose monitoring is associated with increased arterial stiffness in type 2 diabetes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Association between glycemia risk index and arterial stiffness in type 2 diabetes

Cai,

Shen,

et al. 2024

J of Diabetes Invest

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“…Also, the association between GRI and risk of developing long-term diabetes-related complications remains to be fully explored in people with type 1 diabetes though early studies have demonstrated that GRI correlated with the risk of microvascular complications of albuminuria and diabetic retinopathy in patients with type 2 diabetes. 17,18…”

Section: Discussionmentioning

confidence: 99%

Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index

Lee,

Vogrin,

Jones

et al. 2024

J Diabetes Sci Technol

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Background: Glycemia risk index (GRI) is a novel composite metric assessing overall glycemic risk, accounting for both hypoglycemia and hyperglycemia and weighted toward extremes. Data assessing GRI as an outcome measure in closed-loop studies and its relation with conventional key continuous glucose monitoring (CGM) metrics are limited. Methods: A post hoc analysis was performed to evaluate the sensitivity of GRI in assessing glycemic quality in adults with type 1 diabetes randomized to 26 weeks hybrid closed-loop (HCL) or manual insulin delivery (control). The primary outcome was GRI comparing HCL with control. Comparisons were made with changes in other CGM metrics including time in range (TIR), time above range (TAR), time below range (TBR), and glycemic variability (standard deviation [SD] and coefficient of variation [CV]). Results: GRI with HCL (N = 61) compared with control (N = 59) was significantly lower (mean [SD] 33.5 [11.7] vs 56.1 [14.4], respectively; mean difference −22.8 [−27.2, −18.3], P = .001). The mean increase in TIR was +14.8 (11.0, 18.5)%. GRI negatively correlated with TIR for combined arms ( r = −.954; P = .001), and positively with TAR >250 mg/dL ( r = .901; P = .001), TBR < 54 mg/dL ( r = .416; P = .001), and glycemic variability (SD [ r = .916] and CV [ r = .732]; P = .001 for both). Conclusions: Twenty-six weeks of HCL improved GRI, in addition to other CGM metrics, compared with standard insulin therapy. The improvement in GRI was proportionally greater than the change in TIR, and GRI correlated with all CGM metrics. We suggest that GRI may be an appropriate primary outcome for closed-loop trials.

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“…Limitations of the present study include the relatively small sample size compared to large data studies; however, this is a real-life cohort with stable control and comprehensive knowledge of glycometric and clinical variables with a single CGM system. The non-incorporation of TIR in the GRI calculation can be seen as a potential limitation, being the only CGM parameter related to long-term complications at present [ 11 ]; however, the high correlation between TIR and GRI suggests that a similar correlation exists between GRI and the existence of long-term complications as recent studies support [ 20 , 21 ]. More studies are needed to relate this parameter to future complications, its effect on the quality of life of patients with T1D, and its evaluation in other subpopulations (T2D, hospitalized patients).…”

Section: Discussionmentioning

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

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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.

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Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study

Cited by 6 publications

References 35 publications

Association between glycemia risk index and arterial stiffness in type 2 diabetes

Association between glycemia risk index and arterial stiffness in type 2 diabetes

Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults With Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index

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

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