Search

Aims: Diabetic retinopathy (DR) can occur even in well-controlled type 2 diabetes, suggesting residual risks of DR in this population. In particular, we investigated the combined effect of thyroid function and glycaemic control assessed by an emerging metric, time in range (TIR) with DR., Materials and Methods: In this cross-sectional study, a total of 2740 euthyroid patients with type 2 diabetes were included. Thyroid indicators, including thyroid-stimulating hormone (TSH), free triiodothyronine, free thyroxine, thyroid peroxidase antibody and thyroglobulin antibody, were measured. TIR was measured using continuous glucose monitoring data., Results: Overall, the multivariable-adjusted odds ratios (ORs) for DR across ascending tertiles of TSH were 1.00 (reference), 1.06 (95% confidence interval [CI] 0.85-1.32), and 1.48 (95% CI 1.19-1.85). Even in well-controlled participants who achieved a TIR target of >70% (n = 1449), the prevalence of DR was 23.8%, which was significantly related to TSH (OR = 1.54, 95% CI 1.12-2.12, highest vs. lowest TSH tertile). Participants were then classified into 6 groups by the joint categories of TIR (>70%, ≤70%) and TSH (tertiles), and the multivariable-adjusted ORs for DR were highest in TIR ≤70% and the highest TSH tertile group (OR = 1.96, 95% CI 1.41-2.71) when compared with the TIR >70% and the lowest TSH tertile group., Conclusions: In type 2 diabetic patients with well-controlled glycaemic status, higher TSH within the normal range was associated with an increased risk of DR. The combination of suboptimal TSH and TIR further increased the risk of DR., (© 2023 The Authors. Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.)

Aim: To investigate the association between a new composite metric, glycaemia risk index (GRI), and incident diabetic retinopathy (DR)., Methods: A total of 1204 adults with type 2 diabetes without DR at baseline were included between 2005 and 2019 from a single centre in Shanghai, China. GRI was obtained from continuous glucose monitoring data at baseline. Cox proportion hazard regression analysis was used to assess the association between GRI and the risk of incident DR., Results: During a median follow-up of 8.4 years, 301 patients developed DR. The multivariable-adjusted hazard ratios (HRs) for incident DR across ascending GRI quartiles (≤14 [reference], 15 ~ 28, 29 ~ 47 and > 47) were 1.00, 1.05 (95% CI 0.74-1.48), 1.33 (95% confidence interval [CI] 0.96-1.84) and 1.53 (95% CI 1.11-2.11), respectively. For each 1-SD increase in GRI, the risk of DR was increased by 20% (HR 1.20, 95% CI 1.07-1.33) after adjustment for confounders., Conclusions: In patients with type 2 diabetes, higher GRI is associated with an increased risk of incident DR. GRI has the potential to be a valuable clinical measure, which needs to be further explored in future studies., (© 2023 John Wiley & Sons Ltd.)

Aims/introduction: We proposed a novel continuous glucose monitoring (CGM)-based metric, area under the curve in range (AucIR), for integrating both the amplitude and duration of dysglycemia, and further compared AucIR with the emerging key CGM-derived metric, time in range (TIR)., Materials and Methods: A total of 2,030 adult patients with type 2 diabetes were enrolled during May 2020 to October 2021. AucIR and TIR were measured with 7-day CGM data. Logistic regression analysis and the C-statistic was carried out to assess the association of AucIR and TIR with diabetic retinopathy (DR)., Results: Both AucIR (r = -0.89) and TIR (r = -0.95) were strongly correlated with mean glucose levels. Compared with TIR, AucIR showed a tighter relationship with parameters of glycemic variability, including the coefficient of variation (r = -0.56), standard deviation (r = -0.89) and mean amplitude of glycemic excursions (r = -0.70). For each absolute 10% decrease in AucIR, the risk of DR was increased by 7% (95% confidence interval 1.02-1.13) after adjustment for confounders. With respect to TIR, each absolute 10% decrease was associated with an 8% (95% confidence interval 1.03-1.14) increased risk of DR. The model discrimination for DR, as measured by C-statistic, did not differ significantly between the two metrics (P > 0.05)., Conclusions: AucIR did not provide added benefit over TIR in the assessment of DR risk among patients with type 2 diabetes. The potential value of AucIR needs to be explored in future studies., (© 2022 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.)

Aim: There is a paucity of studies regarding the association between long-term glycemic variability with the risk of diabetic retinopathy (DR) in patients with type 2 diabetes. Therefore, the purpose of this study is to explore the association of glycated albumin (GA) variability and HbA1c variability with the risk of DR in patients with type 2 diabetes., Methods: This prospective cohort study included 315 inpatients with type 2 diabetes (191 males and 124 females) with at least 3 measurements of GA and HbA1c within 2years prior to the baseline investigation. Different GA and HbA1c variability markers were calculated, including CV, variability independent of the mean (VIM), and the average real variability (ARV). Cox proportional hazard regression models were used to explore the association between visit-to-visit variability of GA and HbA1c and the risk of DR., Results: After an average follow-up of 3.42years, 81 patients developed incident DR. Multivariable-adjusted (diabetes duration, smoking status, systolic blood pressure, albumin to creatinine ratio, triglycerides, using fibrates, and mean HbA1c) hazard ratios of DR associated with each unit increase in GA-CV, GA-VIM, and GA-ARV were 1.05 (95% CI 1.02-1.09), 1.69 (95% CI 1.24-2.32), and 1.13 (95%CI 1.04-1.23), respectively. However, there was no significant association between visit-to-visit HbA1c variability and the risk of DR., Conclusions: The present study indicated that visit-to-visit variability of GA can predict the risk of incident DR in patients with type 2 diabetes, and the prediction ability is independent of the average HbA1c levels., (Copyright © 2021 Elsevier Inc. All rights reserved.)