ABSTRACT
Aims/Introduction
This study aimed to clarify the relationship between glucose metrics measured by continuous glucose monitoring (CGM) and diabetic retinopathy (DR) and albuminuria among Japanese individuals with type 1 diabetes mellitus.
Materials and Methods
The study included 294 individuals with type 1 diabetes (68.7% women) who underwent intermittent scanned CGM between March and April 2023. Multivariable logistic regression analysis was performed to examine the cross‐sectional association of each glucose metric (time in range [TIR], time above range [TAR], time below range, glucose management indicator [GMI], and coefficient of variation [CV]) with DR or albuminuria.
Results
The prevalence of DR and albuminuria was 27.6 and 13.6%, respectively. CGM metrics did not differ between individuals with DR and those without. However, individuals with albuminuria had significantly lower TIR and higher TAR than those without. The presence of DR was significantly associated with higher levels of TAR (odds ratio [OR] = 1.04, P < 0.001), GMI (OR = 2.13, P < 0.001), and HbA1c (OR = 2.07, P < 0.001), and with a lower level of TIR (OR = 0.97, P = 0.005). The presence of albuminuria was significantly associated with higher levels of TAR (OR = 1.05, P = 0.002), GMI (OR = 2.28, P = 0.005), and HbA1c (OR = 2.28, P < 0.001), and with a lower level of TIR (OR = 0.95, P = 0.007).
Conclusion
Decreased TIR and increased TAR and GMI were independently associated with a higher prevalence of DR and albuminuria in Japanese individuals with type 1 diabetes.
Keywords: CGM metrics, Microvascular complications, Type 1 diabetes mellitus
INTRODUCTION
Continuous glucose monitoring (CGM) has recently been recognized as a useful tool for managing blood glucose levels among individuals with diabetes. CGM metrics, such as time in range (TIR, glucose concentration 70–180 mg/dL), time above range (TAR, glucose concentration > 180 mg/dL), and time below range (TBR, glucose concentration < 70 mg/dL), have been widely used in clinical practice. 1
Recently, an increasing number of studies have demonstrated a significant relationship between the percentage of CGM‐derived TIR and vascular complications. In type 1 diabetes mellitus, reanalyzed data from the Diabetes Control and Complications Trial (DCCT) showed that the risk for progression of diabetic retinopathy and microalbuminuria increased by 64 and 40%, respectively, for every 10% decrease in TIR derived from seven‐point self‐monitoring of blood glucose (SMBG) measurements. 2 In two studies of Belgian individuals with type 1 diabetes receiving sensor‐augmented pump therapy or multiple daily insulin injections, 3 , 4 CGM‐derived TIR showed an independent cross‐sectional association with microvascular complications 3 , 4 and cerebrovascular accident. 4 In a study of 152 Spanish individuals with type 1 diabetes without cardiovascular disease, TAR was positively associated with the presence of composite microvascular complications, whereas TIR showed an inverse association. 5 A more recent study of adults with type 1 diabetes in the United States showed that TIR and TAR, as well as HbA1c, were significantly associated with the incidence of retinopathy. 6 Moreover, significant associations have been reported between various metrics of glycemic variability and subclinical atherosclerosis 7 as well as peripheral or autonomic neuropathies 8 , 9 , 10 , 11 in individuals with type 1 diabetes. Although glucose fluctuations are more pronounced and CGM use is more common in individuals with type 1 diabetes than in those with type 2 diabetes mellitus, data from Japanese individuals with type 1 diabetes remain scarce. Therefore, this study aimed to clarify the cross‐sectional associations between CGM metrics and diabetic retinopathy and albuminuria in Japanese individuals with type 1 diabetes.
MATERIALS AND METHODS
Participants
This cross‐sectional study included 318 individuals with type 1 diabetes who visited the Division of Diabetology and Metabolism, Department of Internal Medicine, Tokyo Women's Medical University Hospital, and underwent intermittent scanned CGM using the FreeStyle Libre Flash glucose monitoring system (Abbott, Tokyo, Japan) between March and April 2023. Of these, individuals with sensor glucose coverage <70%, those undergoing dialysis, kidney transplant recipients, pregnant women, and those with missing data were excluded. The remaining 294 individuals with 28 days of CGM data were included in the analysis.
Measurements
TIR, TAR, TBR, coefficient of variation (CV), and glucose management indicator (GMI) for each individual were calculated and reported as CGM metrics. GMI, an index that estimates HbA1c based on average glucose levels measured by CGM, reflects more real‐time blood glucose fluctuations than HbA1c does and is calculated using the formula: GMI (%) = 3.31 + 0.02392 × mean glucose in mg/dL. Relative glucose variability is expressed as the CV, calculated as standard deviation divided by the average blood glucose level.
Data on age, sex, duration of diabetes, body mass index (BMI), blood pressure, lipid parameters (low‐density lipoprotein cholesterol [LDL‐C] and triglycerides), creatinine, HbA1c, and treatments of diabetes, hypertension, and dyslipidemia in March or April 2023 were extracted from the electronic medical record system. All data were obtained for each individual on the same day. The median (interquartile range [IQR]) interval (months) between the start date of CGM and the date of clinical data collection was −0.5 (−1.1, −0.1). LDL‐C was measured using the direct method (Hitachi Automated Analyzer Labospect 008α; Hitachi High‐Tech, Tokyo, Japan). HbA1c levels were measured using high‐performance liquid chromatography (HPLC) (Adams A1c HA‐8190 V; Arkray, Kyoto, Japan). Estimated glomerular filtration rate (eGFR) was calculated using the formula proposed by the Japanese Society for Nephrology: eGFR (mL/min/1.73 m2) = 194 × age (years)−0.287 × serum creatinine level (mg/dL)−1.094 × (0.739 for women). 12 In four individuals younger than 18 years, equations developed for calculating eGFR in Japanese children 13 were applied.
Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the use of antihypertensive medications, including angiotensin‐converting enzyme inhibitors or angiotensin II receptor blockers. 14 Dyslipidemia was defined as LDL‐C ≥ 140 mg/dL, triglyceride ≥175 mg/dL, or the use of lipid‐lowering medications. 15
To determine diabetic retinopathy status, ophthalmologist‐diagnosed data obtained at our hospital within a window of 12 months before and after the start of CGM data extraction were considered, and the closest assessment to the CGM start date was selected. The median (IQR) interval (months) between the start of CGM and the diabetic retinopathy assessment was 2.2 (−0.5, 5.0). Similarly, albuminuria data were obtained within a 12‐month window before and after the start of CGM data extraction, and the closest assessment to the CGM start date was selected. The median (IQR) interval (months) between the start of CGM and the collection of early‐morning first urine samples was 3.3 (−0.3, 5.7).
Outcome
The outcome was the presence of microvascular complications (diabetic retinopathy and albuminuria). Diabetic retinopathy was classified as no diabetic retinopathy, simple diabetic retinopathy (microaneurysm, retinal dot, blot, or flame‐shaped hemorrhage, hard or soft exudates, or retinal nonperfusion area), and proliferative retinopathy (neovascularization, vitreous hemorrhage, or preretinal hemorrhages). Albuminuria was defined as a urinary albumin‐to‐creatinine ratio (UACR) ≥30 mg/gCr 16 based on a single early‐morning first urine sample.
Statistical analysis
Data are expressed as means ± standard deviations (SDs), medians (IQRs), or proportions. The Student t‐test or the Mann–Whitney U test was used to compare continuous variables between groups, and the Chi‐squared test was used to compare categorical variables between groups.
Pearson's correlation coefficient was used to examine the correlation between two sets of glucose indicator data.
Multivariable logistic regression analysis was performed to examine the cross‐sectional associations between TIR and the presence of diabetic retinopathy or albuminuria, adjusting for sex (women), duration of diabetes (per 1 year), hypertension (yes), dyslipidemia (yes), and eGFR (1 mL/min/1.73 m2). Results are expressed as odds ratios (ORs) with 95% CIs. Similar analyses were conducted to evaluate associations between the presence of complications and other CGM metrics, including TAR, TBR, CV, and GMI, as well as HbA1c measured by the HPLC method. Sensitivity analyses were performed using models in which glucose metrics divided into tertiles were treated as categorical variables to examine non‐linear relationships. The cut‐off values for tertiles of each glucose indicator are shown in Table S1. Additional sensitivity analyses were performed by further adjusting the models for insulin dose (per 1 unit/day) or insulin treatment type (continuous subcutaneous insulin infusion [CSII] vs. multiple daily injections [MDI]).
Statistical analyses were performed using SPSS (Statistical Package for the Social Sciences for Windows) (IBM, Chicago, Illinois, USA) version 24.0. A two‐tailed test was applied, and a P‐value <0.05 was considered statistically significant.
Ethical considerations
This study was approved by the Ethical Review Committee of Tokyo Women's Medical University (Approval No. 2023‐0126, Date of Approval: December 4, 2023). All clinical studies were conducted in accordance with the principles of the Declaration of Helsinki.
RESULTS
Clinical characteristics of the participants
Clinical characteristics of the 294 participants (202 women) included in the analysis are shown in Table 1. The mean values for age, diabetes duration, HbA1c, BMI, and eGFR were 44 ± 13 years, 22 ± 12 years, 7.6 ± 1.0 %, 23.7 ± 3.6 kg/m2, and 81.8 ± 20.9 mL/min/1.73 m2, respectively. The median UACR was 4.5 (IQR, 3.1–7.6) mg/gCr. The proportions of individuals with hypertension and dyslipidemia were 35.7 and 33.3%, respectively. All individuals were receiving insulin therapy, with 82.0% treated with MDI and 18.0% with CSII. The median administered insulin dose was 46.5 (IQR, 36.4–60.0) units/day.
Table 1.
Clinical characteristics of the study participants
| Number (women) | 294 (202) |
| Age (years) | 44 ± 13 |
| Duration of diabetes (years) | 22 ± 12 |
| HbA1c (%) | 7.6 ± 1.0 |
| BMI (kg/m2) | 23.7 ± 3.6 |
| Systolic blood pressure (mmHg) | 127 ± 17 |
| Diastolic blood pressure (mmHg) | 74 ± 11 |
| LDL‐cholesterol (mg/dL) | 108 ± 28 |
| Triglyceride (mg/dL) | 76 (54, 102) |
| UACR (mg/gCr) | 4.5 (3.1, 7.6) |
| eGFR (mL/min/1.73 m2) | 81.8 ± 20.9 |
| Antihypertensive medications (%) | 21.1 |
| ACE or ARB inhibitor (%) | 18.0 |
| Lipid‐lowering medications (%) | 21.8 |
| Statin (%) | 21.4 |
| Hypertension (%) | 35.7 |
| Dyslipidemia (%) | 33.3 |
| Insulin treatment type, MDI (%), CSII (%) | 82.0, 18.0 |
| Insulin (units/day) | 46.5 (36.4, 60.0) |
| Diabetic retinopathy (%) | 27.6 |
| Albuminuria (%) | 13.6 |
| CGM metrics | |
| TIR (%) | 60.8 (50.8, 70.7) |
| TAR (%) | 30.9 (19.5, 44.1) |
| TBR (%) | 5.7 (2.4, 9.8) |
| CV (%) | 39.8 (35.2, 44.4) |
| GMI (%) | 7.0 (6.6, 7.4) |
Data are means ± SDs, median (IQR), or proportions. Albuminuria indicates UACR ≥30 mg/gCr. Hypertension indicates systolic blood pressure ≥ 140 mmHg, diastolic pressure ≥ 90 mmHg, or on antihypertensive medications. Dyslipidemia indicates LDL‐C ≥ 140 mg/dL, triglyceride ≥175 mg/dL, or on lipid‐lowering medications. ACE, angiotensin‐converting enzyme; ARB, angiotensin II receptor blocker; BMI, body mass index; CGM, continuous glucose monitoring; CSII, continuous subcutaneous insulin infusion; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; GMI, glucose management indicator; IQR, interquartile range; LDL, low‐density lipoprotein; MDI, multiple daily injections; TAR, time above range; TBR, time below range; TIR, time in range; UACR, urinary albumin‐creatinine ratio.
Eighty‐one individuals (27.6%) had diabetic retinopathy, with a prevalence of simple and proliferative retinopathy of 18.7% (55/294) and 8.8% (26/294), respectively. Albuminuria was present in 40 participants (13.6%), including 28 with microalbuminuria (9.5%) and 12 with macroalbuminuria (4.1%).
The mean sensor wear‐time rate over the 28‐day period was 93.1% (SD, 20.4). The median TIR, TAR, TBR, CV, and GMI were 60.8% (IQR, 50.8–70.7), 30.9% (IQR, 19.5–44.1), 5.7% (IQR, 2.4–9.8), 39.8% (IQR. 35.2–44.4), 7.0% (IQR, 6.6–7.4), respectively.
Correlation of glucose indicators
Most glucose indices were significantly inter‐correlated (Table 2). HbA1c showed significant negative correlations with TIR and TBR, and positive correlations with TAR and GMI. TIR showed significant negative correlations with TAR, CV, and GMI. TAR showed a significant negative correlation with TBR and a positive correlation with GMI. TBR showed a significant negative correlation with GMI and a positive correlation with CV. GMI showed a significant negative correlation with CV.
Table 2.
Pearson's correlation coefficients among various glucose indices in 294 individuals with type 1 diabetes
| HbA1c | TIR | TAR | TBR | CV | ||
|---|---|---|---|---|---|---|
| TIR | Pearson's correlation coefficient | −0.676 | – | – | – | – |
| P‐value | <0.001 | – | – | – | – | |
| TAR | Pearson's correlation coefficient | +0.719 | −0.909 | – | – | – |
| P‐value | <0.001 | <0.001 | – | – | – | |
| TBR | Pearson's correlation coefficient | −0.310 | +0.089 | −0.497 | – | – |
| P‐value | <0.001 | 0.131 | <0.001 | – | – | |
| CV | Pearson's correlation coefficient | −0.026 | −0.207 | −0.010 | +0.454 | – |
| P‐value | 0.294 | <0.001 | 0.865 | <0.001 | – | |
| GMI | Pearson's correlation coefficient | +0.701 | −0.675 | +0.766 | −0.423 | −0.123 |
| P‐value | <0.001 | <0.001 | <0.001 | <0.001 | 0.035 |
CV, coefficient of variation; GMI, glucose management indicator; TAR, time above range; TBR, time below range; TIR, time in range.
Differences in CGM metrics, HbA1c, and clinical characteristics according to microvascular complications status
Individuals with diabetic retinopathy were significantly older, had a higher BMI, and had a longer duration of diabetes, higher HbA1c levels, a higher prevalence of hypertension, and lower eGFR levels than those without diabetic retinopathy (Table 3). The proportion of individuals treated with CSII and the median daily insulin dose did not differ between the two groups (Table 3). The percentages of the five CGM‐derived glucose metrics did not differ between the two groups (Table 3).
Table 3.
Difference in glucose metrics based on a continuous glucose monitoring system, HbA1c, and clinical characteristics according to microvascular complications status in 294 individuals with type 1 diabetes
| Diabetic retinopathy | Albuminuria | |||||
|---|---|---|---|---|---|---|
| Absent (n = 213) | Present (n = 81) | P‐value | Absent (n = 254) | Present (n = 40) | P‐value | |
| HbA1c (%) | 7.3 (6.8, 8.0) | 7.7 (7.0, 8.5) | 0.003 | 7.4 (6.8, 8.0) | 8.4 (7.4, 9.4) | <0.001 |
| CGM metrics | ||||||
| TIR (%) | 61.8 (52.8, 71.7) | 57.2 (48.4, 68.8) | 0.069 | 60.9 (52.0, 71.7) | 54.7 (42.1, 66.9) | 0.031 |
| TAR (%) | 29.7 (18.7, 42.5) | 33.5 (22.1, 48.6) | 0.071 | 30.6 (19.0, 43.6) | 42.5 (25.9, 56.4) | 0.010 |
| TBR (%) | 6.2 (2.4, 10.3) | 5.0 (1.8, 8.5) | 0.236 | 5.8 (2.4, 10.1) | 4.3 (1.2, 7.4) | 0.076 |
| CV (%) | 40.0 (36.1, 44.6) | 39.1 (34.6, 43.4) | 0.268 | 39.8 (35.2, 44.5) | 38.7 (35.0, 45.3) | 0.975 |
| GMI (%) | 7.0 (6.6, 7.4) | 7.1 (6.7, 7.5) | 0.188 | 7.0 (6.6, 7.4) | 7.3 (6.9, 8.0) | 0.038 |
| Age (years) | 42 (31, 52) | 49 (44, 54) | <0.001 | 44 (33, 52) | 52 (46, 58) | 0.001 |
| Women (%) | 70.0 | 65.4 | 0.455 | 71.4 | 40.0 | 0.001 |
| Duration of diabetes (years) | 16.8 (10.7, 25.2) | 31.2 (25.2, 39.1) | 0.001 | 20 (12, 30) | 34 (22, 39) | <0.001 |
| BMI (kg/m2) | 22.9 (20.7, 25.5) | 24.0 (22.6, 26.4) | 0.004 | 23.1 (20.9, 25.4) | 26.9 (22.8, 30.0) | <0.001 |
| eGFR (mL/min/1.73 m2) | 81.5 (70.0, 95.9) | 74.8 (60.9, 89.2) | <0.001 | 80.6 (69.7, 93.7) | 63.1 (44.4, 80.0) | <0.001 |
| Insulin treatment type, CSII (%) | 19.7 | 13.6 | 0.221 | 17.8 | 20.0 | 0.789 |
| Insulin (unit/day) | 48.0 (38.0, 60.5) | 42.5 (35.0, 57.5) | 0.140 | 45.5 (36.0, 60.0) | 52.0 (41.3, 81.0) | 0.082 |
| Hypertension (%) | 29.6 | 51.9 | <0.001 | 31.6 | 80.0 | <0.001 |
| Dyslipidemia (%) | 31.5 | 38.3 | 0.268 | 30.5 | 64.0 | <0.001 |
Hypertension indicates systolic blood pressure ≥ 140 mmHg, diastolic pressure ≥ 90 mmHg, or on antihypertensive medications. Dyslipidemia indicates LDL‐C ≥ 140 mg/dL, triglyceride ≥ 175 mg/dL, or on lipid‐lowering medications. Data of continuous variables between groups was compared by the Mann–Whitney U test, and data of categorical variables between the groups was compared by the Chi‐squared test. Statistical significance was defined as P < 0.05. BMI, body mass index; CGM, continuous glucose monitoring; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; GMI, glucose management indicator; TAR, time above range; TBR, time below range; TIR, time in range.
Individuals with albuminuria were significantly older, had a higher BMI, and had a longer duration of diabetes, higher HbA1c levels, a higher proportion of women, hypertension, and dyslipidemia, and lower eGFR levels than those without albuminuria (Table 3). The proportion of individuals treated with CSII and the median daily insulin dose did not differ between the two groups (Table 3). Individuals with albuminuria had significantly lower TIR and higher TAR than those without albuminuria (Table 3).
Relationship between CGM metrics, HbA1c, and diabetic retinopathy
Table 4 presents the multivariable‐adjusted associations between each of the six glucose indices (CGM metrics and HbA1c) and diabetic retinopathy when glucose indices were treated as continuous variables. The presence of diabetic retinopathy was significantly associated with higher levels of TAR (OR = 1.04, P < 0.001), GMI (OR = 2.13, P < 0.001), and HbA1c (OR = 2.07, P < 0.001), and with a lower level of TIR (OR = 0.97, P = 0.005) (Table 3).
Table 4.
Adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from various glucose indices, including glucose metrics based on a continuous glucose monitoring system, in 294 individuals with type 1 diabetes
| Independent variables | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||
| Sex (women) | 0.72 (0.37–1.40) | 0.74 (0.38–1.46) | 0.82 (0.42–1.59) | |||
| Duration of diabetes (1 year) | 1.13 (1.09–1.17) | 1.13 (1.09–1.17) | 1.12 (1.09–1.16) | |||
| Hypertension (yes) | 1.13 (0.58–2.17) | 1.11 (0.57–2.15) | 1.12 (0.58–2.14) | |||
| Dyslipidemia (yes) | 0.94 (0.48–1.84) | 0.85 (0.42–1.71) | 0.99 (0.50–1.95) | |||
| eGFR (1 mL/min/1.73 m2) | 0.99 (0.98–1.01) | 0.99 (0.97–1.01) | 0.99 (0.98–1.01) | |||
| Glucose indices | TIR (1%) | 0.97 (0.95–0.99) | TAR (1%) | 1.04 (1.01–1.06) | TBR (1%) | 0.95 (0.91–1.00) |
| Model 4 | Model 5 | Model 6 | ||||
| Independent variables | OR (95% CI) | OR (95% CI) | OR (95% CI) | |||
| Sex (women) | 0.76 (0.39–1.47) | 0.77 (0.39–1.52) | 0.75 (0.38–1.50) | |||
| Duration of diabetes (1 year) | 1.12 (1.08–1.16) | 1.14 (1.10–1.18) | 1.14 (1.10–1.18) | |||
| Hypertension (yes) | 1.20 (0.63–2.29) | 1.13 (0.58–2.19) | 1.01 (0.51–1.99) | |||
| Dyslipidemia (yes) | 1.04 (0.53–2.02) | 0.87 (0.43–1.73) | 0.80 (0.39–1.61) | |||
| eGFR (1 mL/min/1.73 m2) | 1.00 (0.98–1.01) | 0.99 (0.97–1.01) | 0.99 (0.97–1.01) | |||
| Glucose indices | CV (1%) | 0.97 (0.92–1.01) | GMI (1%) | 2.13 (1.42–3.21) | HbA1c (1%) | 2.07 (1.46–2.93) |
Hypertension indicates systolic blood pressure ≥ 140 mmHg, diastolic pressure ≥ 90 mmHg, or on antihypertensive medications. Dyslipidemia indicates LDL‐C ≥ 140 mg/dL, triglyceride ≥175 mg/dL, or on lipid‐lowering medications. CI, confidence interval; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; GMI, glucose management indicator; OR, odds ratio; TAR, time above range; TBR, time below range; TIR, time in range.
Sensitivity analyses in which glucose indices were stratified into tertiles yielded results consistent with those of the models treating glucose indices as continuous variables (Table S2). Higher TBR and CV categories were not associated with increased odds of diabetic retinopathy (Table S2).
Relationship between CGM metrics, HbA1c, and albuminuria
Table 5 presents the multivariable‐adjusted associations between glucose indices and albuminuria when glucose indices were treated as continuous variables. The presence of albuminuria was significantly associated with higher levels of TAR (OR = 1.05, P = 0.002), GMI (OR = 2.28, P = 0.005), and HbA1c (OR = 2.28, P < 0.001), and with a lower level of TIR (OR = 0.95, P = 0.007) (Table 4).
Table 5.
Adjusted odds ratios (95% confidence intervals) for albuminuria from various glucose indices, including glucose metrics based on a continuous glucose monitoring system, in 294 individuals with type 1 diabetes
| Independent variables | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||
| Sex (women) | 0.26 (0.10–0.71) | 0.26 (0.10–0.71) | 0.29 (0.11–0.75) | |||
| Duration of diabetes (1 year) | 1.03 (0.99–1.08) | 1.03 (0.99–1.08) | 1.03 (0.99–1.08) | |||
| Hypertension (yes) | 4.70 (1.51–14.63) | 4.92 (1.54–15.75) | 4.65 (1.51–14.26) | |||
| Dyslipidemia (yes) | 2.26 (0.82–6.25) | 1.98 (0.70–5.62) | 2.31 (0.85–6.27) | |||
| eGFR (1 mL/min/1.73 m2) | 0.96 (0.93–0.99) | 0.96 (0.93–0.99) | 0.96 (0.94–0.99) | |||
| Glucose indices | TIR (1%) | 0.95 (0.92–0.99) | TAR (1%) | 1.05 (1.02–1.09) | TBR (1%) | 0.92 (0.84–1.01) |
| Model 4 | Model 5 | Model 6 | ||||
| Independent variables | OR (95% CI) | OR (95% CI) | OR (95% CI) | |||
| Sex (women) | 0.30 (0.12–0.76) | 0.28 (0.11–0.76) | 0.27 (0.10–0.75) | |||
| Duration of diabetes (1 year) | 1.03 (0.99–1.08) | 1.04 (0.99–1.09) | 1.04 (0.99–1.09) | |||
| Hypertension (yes) | 4.67 (1.55–14.04) | 4.72 (1.51–14.72) | 3.81 (1.19–12.13) | |||
| Dyslipidemia (yes) | 2.72 (1.03–7.18) | 2.24 (0.83–6.09) | 1.99 (0.71–5.57) | |||
| eGFR (1 mL/min/1.73 m2) | 0.96 (0.94–0.99) | 0.96 (0.93–0.99) | 0.96 (0.93–0.99) | |||
| Glucose indices | CV (1%) | 1.00 (0.94–1.07) | GMI (1%) | 2.28 (1.28–4.07) | HbA1c (1%) | 2.28 (1.41–3.68) |
Hypertension indicates systolic blood pressure ≥ 140 mmHg, diastolic pressure ≥ 90 mmHg, or on antihypertensive medications. Dyslipidemia indicates LDL‐C ≥ 140 mg/dL, triglyceride ≥ 175 mg/dL, or on lipid‐lowering medications. CI, confidence interval; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; GMI, glucose management indicator; OR, odds ratio; TAR, time above range; TBR, time below range; TIR, time in range.
Sensitivity analyses for albuminuria using tertile‐stratified glucose indices were consistent with the results of the continuous variable models (Table S3). Higher TBR and CV categories were not associated with increased odds of albuminuria (Table S3). Although higher TIR and GMI categories did not show statistically significant associations with albuminuria, the direction of the associations was consistent with the trends observed in the continuous variable models (Table S3).
Additional analyses
Sensitivity analyses further adjusting for insulin dose or insulin treatment type showed similar results (Tables S4–S7). Accordingly, neither insulin dose nor insulin treatment type was independently associated with the presence of diabetic retinopathy or albuminuria.
DISCUSSION
The present study of Japanese individuals with type 1 diabetes demonstrated that decreased TIR and increased TAR and GMI were significantly and independently associated with higher odds of microvascular complications (diabetic retinopathy and albuminuria), whereas no significant relationship was observed between TBR or CV and these complications. These findings indicate that hyperglycemia is strongly associated with the presence of microvascular complications in Japanese individuals with type 1 diabetes.
In individuals with type 1 diabetes, TIR calculated from DCCT data has been shown to be significantly associated with the development of retinopathy and microalbuminuria. 2 , 17 Our results, showing a cross‐sectional inverse association of TIR and the positive associations of TAR and GMI with microvascular complications, are consistent with several previous studies of individuals with type 1 diabetes. 3 , 4 , 5 Although hypoglycemia is an important consideration in clinical diabetes management, TBR was not associated with microvascular complications in our study or a Spanish cohort, 5 although in the latter study, TBR was associated with the presence of carotid plaque after adjusting for 5‐year mean HbA1c. 5 These results support the consensus that hyperglycemia, including chronic hyperglycemia reflected by HbA1c, is a major determinant of microangiopathy in individuals with type 1 diabetes.
In recent years, blood glucose fluctuations have been increasingly recognized as relevant to vascular complications. In type 1 diabetes, CGM‐derived glucose variability 7 , 8 , 9 , 10 , 11 has been associated with subclinical atherosclerosis 7 and with peripheral or autonomic neuropathies. 8 , 9 , 10 , 11 In contrast, glucose variability derived from SMBG showed no association with the progression of diabetic retinopathy and albuminuria 18 or with autonomic and peripheral neuropathy 19 in the DCCT and its follow‐up studies. In a Czechoslovakian type 1 diabetes study, CGM‐derived glucose variability indices, SD, mean amplitude of glucose excursions (MAGE), and CV, were significantly higher in individuals with microvascular complications than in those without, and SD showed a significant cross‐sectional association with diabetic microvascular complications. 20 In terms of pathophysiology, blood glucose fluctuations promote reactive oxygen species production, leading to oxidative stress, vascular endothelial damage, inflammation, and accelerated arteriosclerosis, including the development of diabetic microvascular complications. 21 In our study, no significant cross‐sectional associations were observed between CGM‐derived CV or SD (data not shown) and microvascular complications. The reason for this is unclear; however, our patients with type 1 diabetes may have had better HbA1c and CGM profiles than those in previous studies, 20 , 22 which could have influenced the results. It has been hypothesized that exogenous insulin may inhibit pathways associated with oxidative stress and thereby attenuate microvascular damage. 23 However, exogenous insulin was not associated with microvascular complications in our cohort. This may reflect the patient background, which included both cases in which treatment intensification was initiated in patients already progressing without complications and cases in which intensification was applied to patients already experiencing progression. This was considered a limitation of the cross‐sectional study. Blood glucose variability indices vary widely across studies. Future research should clarify which measure of glucose variability, short‐term variability (e.g., SD, MAGE, CGM metrics), visit‐to‐visit variability (e.g., HbA1c), or other indices, is most strongly related to the risk of vascular complications in individuals with type 1 diabetes with diverse clinical backgrounds.
As shown in Table 2, CGM‐derived glucose metrics show high collinearity with HbA1c, making it challenging to determine the additive effect of these metrics to identify the presence of these complications. Nevertheless, our results do not imply that CGM lacks application in routine clinical practice when HbA1c is simultaneously measured by HPLC. The use of CGM improves HbA1c levels, 24 , 25 reduces fear of hypoglycemia, 26 increases treatment satisfaction, 25 , 27 and enhances confidence in managing hypoglycemia 28 among individuals with diabetes. Consequently, CGM may help reduce future complications in individuals with diabetes. On the other hand, HbA1c measured by the HPLC method is known to diverge from mean blood glucose levels. 29 Its reliability can be compromised by individual differences in red blood cell lifespan and glucose uptake, leading to potential misrepresentation of a person's true glycemic exposure. 30 Therefore, CGM's blood glucose metrics appear to be useful in complementing the limitations of HbA1c measured by HPLC.
This study had some limitations. First, participants were Japanese individuals with type 1 diabetes from a single university hospital. The prevalence of retinopathy in this study was higher, and the prevalence of albuminuria was similar, compared with a large Japanese type 1 diabetes cohort in 2014, which had a shorter duration of diabetes (15 years) but a similar age (44 years) and HbA1c (7.7%). 31 Since large CGM datasets for Japanese individuals with type 1 diabetes are currently unavailable, the CGM metrics (TIR, TAR, and TBR) in the present study showed better profiles than those reported in a large type 1 diabetes cohort from the United Kingdom, 22 limiting the generalizability of our findings. Second, in the current study, the prevalence of retinopathy was lower, and the prevalence of albuminuria was similar, compared with a previous report from our department of 1,619 individuals with type 1 diabetes (retinopathy: 41%, albuminuria: 13.8%) with a nearly identical disease duration (21.7 years) and a slightly higher mean HbA1c (7.8%). 32 In this prior report, the proportions of CGM and insulin pump users were 24 and 0.7%, respectively, whereas in the present study, these were 100 and 18.0%, suggesting that participants in the current study may have received more intensive and comprehensive care. Thus, selection bias may exist. Third, this study had a cross‐sectional design based on CGM data collected over a limited time period; therefore, causal interpretation of the observed relationships is limited. Fourth, fundus examinations were routinely interpreted by multiple ophthalmologists in clinical practice, and observer bias may have occurred. Albuminuria was determined using a single urine specimen, which may introduce measurement bias. Fifth, the accuracy of the FreeStyle Libre sensor reportedly declines during the last 4 days of wear, 33 which was not accounted for in our analyses. Finally, the small number of events compared with the number of independent glucose variables may have increased the risk of Type I errors. To avoid overfitting of the models, in the main analyses, we limited the number of independent variables, selected variables without multicollinearity, and performed sensitivity analyses. Despite these limitations, the results consistently demonstrated a significant cross‐sectional association between hyperglycemia, not glucose variability (CV), and the presence of diabetic retinopathy or albuminuria in Japanese individuals with type 1 diabetes.
In conclusion, our Japanese individuals with type 1 diabetes have shown that the decreased levels of TIR and increased levels of TAR and GMI were independently associated with the presence of diabetic retinopathy and albuminuria.
DISCLOSURE
YK, RK, RS, YH, JO, ST, and HT state that they have no conflict of interest (COI). JM received lecture fees (Novo Nordisk and Terumo) and research grants (Terumo, Dexcom, and Medtronic) during the term of this study. TN received the lecture fees (Ely Lily Japan K.K. and Nippon Boehringer Ingelheim Co., Ltd.) and research grants (Sanwa Kagaku Kenkyusho Co., LTD., Chugai Pharmaceutical Co., LTD., Abbott Japan LLC, Terumo Corporation, and Arkley Industry, Inc.) during the term of this study.
Approval of the research protocol: This study was approved by the Ethical Review Committee of Tokyo Women's Medical University (Approval No. 2023–0126, Date of Approval: December 4, 2023). All clinical studies were conducted in accordance with the Declaration of Helsinki.
Informed consent: Informed consent was obtained from all study participants.
Registry and registration no. of the study/trial: N/A.
Animal studies: N/A.
Supporting information
Table S1. The cut‐off points for tertiles of each glucose index.
Table S2. Adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from various glucose indices stratified by tertiles, including glucose metrics based on a continuous glucose monitoring system, in 294 individuals with type 1 diabetes.
Table S3. Adjusted odds ratios (95% confidence intervals) for albuminuria from various glucose indices stratified by tertiles, including glucose metrics based on a continuous glucose monitoring system, in 294 individuals with type 1 diabetes.
Table S4. Multivariable‐adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from glucose indices, with additional adjustment for insulin dose, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Table S5. Multivariable‐adjusted odds ratios (95% confidence intervals) for albuminuria from glucose indices, with additional adjustment for insulin dose, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Table S6. Multivariable‐adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from glucose indices, with additional adjustment for insulin treatment type, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Table S7. Multivariable‐adjusted odds ratios (95% confidence intervals) for albuminuria from glucose indices, with additional adjustment for insulin treatment type, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Contributor Information
Yuichiro Kondo, Email: kondo.yuichiro1002@gmail.com.
Ryo Kubota, Email: kubota.ryo@twmu.ac.jp.
Rika Suda, Email: suda.rika@twmu.ac.jp.
Yukiko Hasegawa, Email: yukikoh.dmc@twmu.ac.jp.
Junko Oya, Email: johya.dmc@twmu.ac.jp.
Satoshi Takagi, Email: takagi.satoshi.dmc@twmu.ac.jp.
Hiroko Takaike, Email: kobayashi.hiroko@twmu.ac.jp.
Tomoko Nakagami, Email: nakagami.dmc@twmu.ac.jp.
DATA AVAILABILITY STATEMENT
The dataset generated and analyzed in this current study is not publicly available owing to the institutional regulations and the need to protect patient privacy.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. The cut‐off points for tertiles of each glucose index.
Table S2. Adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from various glucose indices stratified by tertiles, including glucose metrics based on a continuous glucose monitoring system, in 294 individuals with type 1 diabetes.
Table S3. Adjusted odds ratios (95% confidence intervals) for albuminuria from various glucose indices stratified by tertiles, including glucose metrics based on a continuous glucose monitoring system, in 294 individuals with type 1 diabetes.
Table S4. Multivariable‐adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from glucose indices, with additional adjustment for insulin dose, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Table S5. Multivariable‐adjusted odds ratios (95% confidence intervals) for albuminuria from glucose indices, with additional adjustment for insulin dose, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Table S6. Multivariable‐adjusted odds ratios (95% confidence intervals) for diabetic retinopathy from glucose indices, with additional adjustment for insulin treatment type, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Table S7. Multivariable‐adjusted odds ratios (95% confidence intervals) for albuminuria from glucose indices, with additional adjustment for insulin treatment type, including glucose metrics based on a continuous glucose monitoring system in 294 individuals with type 1 diabetes.
Data Availability Statement
The dataset generated and analyzed in this current study is not publicly available owing to the institutional regulations and the need to protect patient privacy.