We analyzed data from 2253 veterans with type 1 (34%) or type 2 (66%) diabetes who initiated Dexcom continuous glucose monitoring (CGM) between 2015 and 2022, all with ≥10 days of data over a 6-month landmark period. CGM data were merged with electronic health records (EHR). Diabetic retinopathy (DR) was defined from EHR diagnoses. Incident DR was assessed up to 4 years from CGM initiation. Cox models evaluated associations between CGM metrics (mean glucose [MG], time in range [TIR], time above range [TAR], glycemia risk index [GRI], coefficient of variation, and week-to-week average real variability [ARV]) and DR. There were 99 incident DR events. In separate multivariable models, higher MG, TAR, GRI, and ARV, and lower TIR were associated with DR risk; these associations (except ARV) remained significant after landmark HbA1c adjustment and were stronger than for landmark HbA1c itself, suggesting that CGM metrics provide more informative risk stratification than HbA1c alone.
LeeR, WongTY, SabanayagamC. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis (Lond), 2015; 2:17.
2.
NathanDM, GenuthS, LachinJ, et al.; Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med, 1993; 329(14):977–986.
3.
KilpatrickES, RigbyAS, AtkinSL. A1C variability and the risk of microvascular complications in type 1 diabetes: Data from the diabetes control and complications trial. Diabetes Care, 2008; 31(11):2198–2202.
4.
MaoY, ZhongW. HbA1c variability as an independent risk factor for microvascular complications in type 1 diabetes. J Diabetes Sci Technol, 2024; 18(2):380–388.
5.
LiuL, WuJ, YueS, et al.Incidence density and risk factors of diabetic retinopathy within type 2 diabetes: A five-year cohort study in China (Report 1). Int J Environ Res Public Health, 2015; 12(7):7899–7909.
6.
YangCY, SuPF, HungJY, et al.Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes. Cardiovasc Diabetol, 2020; 19(1):105.
7.
SofizadehS, Eeg-OlofssonK, LindM. Prevalence and risk factors for diabetic retinopathy at diagnosis of type 2 diabetes: An observational study of 77 681 patients from the Swedish National Diabetes Registry. BMJ Open Diabetes Res Care, 2024; 12(3):e003976.
8.
WrightLAC, HirschIB. Metrics beyond hemoglobin A1C in diabetes management: Time in range, hypoglycemia, and other parameters. Diabetes Technol Ther, 2017; 19(S2):S16–S26.
9.
OkunoT, VansomphoneA, ZhangE, et al.Association of both short-term and long-term glycemic variability with the development of microalbuminuria in the ACCORD trial. Diabetes, 2023; 72(12):1864–1869.
10.
YapanisM, JamesS, CraigME, et al.Complications of diabetes and metrics of glycemic management derived from continuous glucose monitoring. J Clin Endocrinol Metab, 2022; 107(6):e2221–e2236.
11.
RajR, MishraR, JhaN, et al.Time in range, as measured by continuous glucose monitor, as a predictor of microvascular complications in type 2 diabetes: A systematic review. BMJ Open Diabetes Res Care, 2022; 10(1):e002573.
12.
SartoreG, ChilelliNC, BurlinaS, LapollaA. Association between glucose variability as assessed by Continuous Glucose Monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes. Acta Diabetol, 2013; 50(3):437–442.
13.
LuJ, MaX, ZhouJ, et al.Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care, 2018; 41(11):2370–2376.
14.
LuJ, MaX, ZhangL, et al.Glycemic variability assessed by continuous glucose monitoring and the risk of diabetic retinopathy in latent autoimmune diabetes of the adult and type 2 diabetes. J Diabetes Investig, 2019; 10(3):753–759.
15.
WakasugiS, MitaT, KatakamiN, et al.Associations between continuous glucose monitoring-derived metrics and diabetic retinopathy and albuminuria in patients with type 2 diabetes. BMJ Open Diabetes Res Care, 2021; 9(1):e001923.
16.
WangY, LuJ, ShenY, et al.Comparison of glucose time in range and area under curve in range in relation to risk of diabetic retinopathy in type 2 diabetes patients. J Diabetes Investig, 2022; 13(9):1543–1550.
17.
HayashiA, ShimizuN, SuzukiA, et al.Novel clinical associations between time in range and microangiopathies in people with type 2 diabetes mellitus on hemodialysis. J Diabetes Complications, 2023; 37(5):108470.
18.
ShahVN, KanapkaLG, AkturkHK, et al.Time in range is associated with incident diabetic retinopathy in adults with type 1 diabetes: A Longitudinal Study. Diabetes Technol Ther, 2024; 26(4):246–251.
19.
WangY, LuJ, YuJ, et al.Association between time in tight range and incident diabetic retinopathy in adults with type 2 diabetes. Diabetes Obes Metab, 2025; 27(3):1415–1422.
20.
BeckRW, BergenstalRM, ChengP, et al.The relationships between time in range, hyperglycemia metrics, and HbA1c. J Diabetes Sci Technol, 2019; 13(4):614–626.
21.
OkunoT, MacwanSA, MillerD, et al.Assessing patterns of continuous glucose monitoring use and metrics of glycemic control in type 1 diabetes and type 2 diabetes patients in the veterans health care system: Integrating continuous glucose monitoring device data with electronic health records data. Diabetes Technol Ther, 2024; 26(11):806–813.
22.
ReavenPD, MacwanS, NewellM, et al.Initiation of continuous glucose monitoring and mortality in type 2 diabetes. Diabetes Technol Ther, 2025; 27(10):778–789.
23.
BreeyearJH, MitchellSL, NealonCL, et al.Development of electronic health record based algorithms to identify individuals with diabetic retinopathy. J Am Med Inform Assoc, 2024; 31(11):2560–2570.
24.
KlonoffDC, WangJ, RodbardD, et al.A Glycemia Risk Index (GRI) of hypoglycemia and hyperglycemia for continuous glucose monitoring validated by clinician ratings. J Diabetes Sci Technol, 2023; 17(5):1226–1242.
25.
MenaL, PintosS, QueipoNV, et al.A reliable index for the prognostic significance of blood pressure variability. J Hypertens, 2005; 23(3):505–511.
26.
ZhouJJ, KoskaJ, BahnG, ReavenP. Glycaemic variation is a predictor of all-cause mortality in the Veteran Affairs Diabetes Trial. Diab Vasc Dis Res, 2019; 16(2):178–185.
27.
OkunoT, MacwanSA, NormanGJ, et al.Continuous glucose monitoring metrics predict all-cause mortality in diabetes: A real-world long-term study. Diabetes Care, 2025; 48(10):1794–1802.
28.
GrambschPM, TherneauTM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika, 1994; 81(3):515–526.
29.
BuurenS, Groothuis-OudshoornC. MICE: Multivariate Imputation by Chained Equations in R. J Stat Soft, 2011; 45(3).
30.
FineJP, GrayRJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc, 1999; 94(446):496–509.
31.
WangL, PorterB, MaynardC, et al. Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care, 2013; 51(4):368–373.
32.
MiechEJ, FreitagMB, Evans RR, et al. Facility-level conditions leading to higher reach: A configurational analysis of national VA weight management programming. BMC Health Serv Res, 2021; 21(1):797.
ShinES, SorensonCM, SheibaniN. Diabetes and retinal vascular dysfunction. J Ophthalmic Vis Res, 2014; 9(3):362–373.
35.
KangQ, YangC. Oxidative stress and diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Redox Biol, 2020; 37:101799.
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