The Association Between Mean Glycated Haemoglobin or Glycaemic Variability and the Development of Retinopathy in People with Diabetes: A Retrospective Observational Cohort Study.
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ABSTRACT: Introduction
To determine the association between mean glycated haemoglobin (HbA1c) or glycaemic variability and the development of diabetic retinopathy (DR) in people with diabetes.Methods
An observational cohort study with people registered with a DR eye screening service between October 2012 and October 2017. Those who had no DR at the start of the study were followed for a maximum of 5 years. HbA1c measures were used to calculate HbA1c variability and mean HbA1c to assess any relationship between these and the risk of developing new onset DR.Results
A total of 2511 individuals were followed up for up to 5 years. Of these, 542 (21.6%) developed DR. After adjustment, HbA1c variability was not significantly associated with the development of DR (p = 0.3435). However, the mean HbA1c was (p < 0.0001). Those with type 1 diabetes had an odds of 1.63 (95% CI 1.11-2.40) of a retinopathy diagnosis compared to those with type 2 diabetes.Conclusions
We have shown that mean HbA1c is associated with an increased risk of developing diabetic retinopathy. However, after adjustment for sex, age, diabetes type and the mean, the HbA1c variability no longer remained significant. Our data suggest that optimizing long-term glycaemic control remains paramount.
SUBMITTER: Dhatariya K
PROVIDER: S-EPMC8479058 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundContinuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear.ObjectivesTo characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability.DesignGlycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored.ResultsData from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01).ConclusionsA stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.
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