ABSTRACT
Aims
The potential benefit of integrating continuous glucose monitoring (CGM) data with conventional glycaemic indicators in diabetes care remains to be determined. This study aimed to investigate the association between 1,5‐anhydroglucitol (1,5‐AG), a 1–2 weeks glycaemic marker with monitoring duration comparable to CGM, and diabetic retinopathy (DR).
Materials and Methods
The cross‐sectional study included 5491 adults with type 2 diabetes. Participants were stratified by serum 1,5‐AG levels (< 6.0 μg/mL, 6.0–10.0 μg/mL, and ≥ 10.0 μg/mL). Time in range (TIR) and glucose variability measures, including standard deviation (SD), coefficient of variation (CV), and mean amplitude of glycaemic excursions (MAGE), were calculated from CGM data.
Results
Overall, the median serum 1,5‐AG level was 3.4 (1.7–7.1) μg/mL, with a DR prevalence of 25.3% (n = 1387). The prevalence of DR increased with descending 1,5‐AG levels (p for trend < 0.001). Compared to patients with 1,5‐AG levels ≥ 10.0 μg/mL (reference), the adjusted odds ratio (OR) for DR was 1.64 (95% CI 1.35–2.01) in individuals with 1,5‐AG < 6.0 μg/mL. In subgroup analyses stratified by TIR tertiles (< 58%, 58%–80%, and ≥ 80%), low 1,5‐AG levels remained significantly associated with an elevated risk of DR in both the highest (OR = 1.44, 95% CI 1.09–1.91) and middle TIR tertiles (OR = 1.64, 95% CI 1.13–2.42). Regarding CGM‐derived glucose variability metrics, while CV and SD were associated with DR in the overall population, none of MAGE, CV, or SD was significantly associated with DR in the highest TIR tertile.
Conclusions
Serum 1,5‐AG is significantly associated with the prevalence of DR in type 2 diabetes. For patients with TIR ≥ 80%, 1,5‐AG may reveal additional risk of DR.
Keywords: 1,5‐anhydroglucitol; continuous glucose monitoring; diabetic retinopathy; time in range; type 2 diabetes
Abbreviations
- 1,5‐AG
1,5‐anhydroglucitol
- BMI
body mass index
- CGM
continuous glucose monitoring
- CV
coefficient of variation
- DR
diabetic retinopathy
- eGFR
estimated glomerular filtration rate
- GRI
glycaemia risk index
- HbA1c
glycated haemoglobin A1c
- HDL‐C
high‐density lipoprotein cholesterol
- LDL‐C
low‐density lipoprotein cholesterol
- MAGE
mean amplitude of glycaemic excursions
- MSG
mean sensor glucose
- SD
standard deviation
- SGLT2i
sodium‐glucose cotransporter‐2 inhibitors
- TAR
time above range
- TBR
time below range
- TIR
time in range
- TITR
time in tight range
1. Introduction
Glycated haemoglobin A1c (HbA1c) has for decades served as the gold standard for glycaemic control monitoring in diabetes [1]. However, large‐scale clinical trials have consistently demonstrated the onset and progression of diabetic retinopathy (DR) even among patients achieving optimal HbA1c targets [2, 3, 4]. Continuous glucose monitoring (CGM) represents a major advance in glycaemic assessment by extending evaluation beyond HbA1c to detailed dynamic glucose profiles [5]. Time in range (TIR), a cornerstone CGM‐derived metric, is now widely recommended in clinical guidelines [6]. Accumulating data indicate that TIR is independently associated with diabetes‐related outcomes [7, 8, 9, 10], and our previous work was among the first to demonstrate a significant association between TIR and all stages of DR in patients with type 2 diabetes [10].
Despite the recognized advantages of CGM, conventional glycaemic markers remain widely used for glucose monitoring in routine care. Accordingly, identifying clinical scenarios in which adjunctive glucose indicators may provide additional value beyond CGM is an important consideration [11, 12]. As an intermediate‐term indicator of glycaemic control [13], 1,5‐anhydroglucitol (1,5‐AG) is informative for assessing glycaemic variability, particularly among individuals with relatively well‐controlled diabetes [14]. Emerging research further links 1,5‐AG to chronic diabetic complications [15, 16]; even in patients achieving near‐target glycaemic control, low 1,5‐AG levels have been associated with long‐term cardiovascular mortality [17]. Given the comparable monitoring duration of 1,5‐AG and CGM (approximately 1–2 weeks), it is of interest to determine whether measurement of 1,5‐AG provides additional clinical value beyond CGM. Although prior work has explored correlations between 1,5‐AG and CGM‐derived glucose excursions [14], the potential added benefit of incorporating 1,5‐AG alongside CGM for diabetes management has not been evaluated.
Therefore, this large‐scale study aimed to examine the combined associations of serum 1,5‐AG and TIR with DR in patients with type 2 diabetes, and to assess whether 1,5‐AG identifies additional DR risk among individuals with well‐controlled TIR.
2. Research Design and Methods
2.1. Study Population
This cross‐sectional study consecutively enroled in patients undergoing complications screening from the Department of Endocrinology and Metabolism of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, between January 2015 and January 2020. Type 2 diabetes was diagnosed according to the 2010 American Diabetes Association criteria [18]. Eligible participants were adults (≥ 18 years) with a confirmed diagnosis of type 2 diabetes and complete clinical records, and no recent changes in their glucose‐lowering therapy within the past 3 months. Exclusion criteria included acute diabetic complications (e.g., ketoacidosis), current glucocorticoid or sodium‐glucose cotransporter‐2 inhibitor (SGLT2i) use, pregnancy, malignancies, psychiatric disorders, hepatic insufficiency, or advanced renal impairment (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2). Following screening procedures, 5491 participants were included in the final analysis. The institutional ethics committee of Shanghai Sixth People's Hospital approved all study protocols in compliance with the Declaration of Helsinki principles, and written informed consent was obtained from each participant prior to study inclusion.
2.2. Clinical and Biological Parameters
All study participants completed standardized medical questionnaires capturing comprehensive clinical data, including medical history and current medications. Physical examinations were then performed, comprising anthropometric measurements (height, weight, and blood pressure) and body mass index (BMI) calculations. Following an overnight fast of at least 10 h, venous blood samples were collected for laboratory analyses, including HbA1c, serum 1,5‐AG, total cholesterol, triglycerides, high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), and serum creatinine. All assays were conducted using established laboratory methods consistent with the prior study [13]. Serum 1,5‐AG was measured using the GlycoMark assay (GlycoMark Inc., USA). Values below the limit of detection (0.2 μg/mL) were recorded as the limit of detection/√2. Renal function was assessed via eGFR calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) equation [19].
2.3. CGM
All enroled participants wore retrospective CGM devices (iPro 2, Medtronic or CGMS GOLD, Medtronic) for subcutaneous interstitial glucose monitoring. Consistent with our previous work [7, 10], all participants had the CGM sensor inserted on the first day of admission and wore it for an average of 3 ± 1 day during hospitalisation. The CGM devices collected glucose values at 5‐min intervals (288 measurements daily), with capillary blood glucose measured at least twice daily using the SureStep glucometer (LifeScan) for calibration. Mean sensor glucose (MSG) was calculated alongside standard deviation (SD), coefficient of variation (CV), and mean amplitude of glycaemic excursions (MAGE), with the latter three used to evaluate glucose variability [5]. TIR was defined as the percentage of time over 24 h with glucose levels between 3.9 and 10.0 mmol/L, while time in tight range (TITR) was defined as the percentage of time with glucose levels between 3.9 and 7.8 mmol/L. Additional glycaemic metrics included time above range (TAR) and time below range (TBR), specifically: TAR> 13.9 (glucose > 13.9 mmol/L), TAR> 10.0 (glucose > 10.0 mmol/L), TAR> 7.8 (glucose > 7.8 mmol/L), TBR< 3.9 (glucose < 3.9 mmol/L), and TBR< 3.0 (glucose < 3.0 mmol/L). The glycaemia risk index (GRI) evaluated low‐ and high‐glucose patterns to rank glucose control using a model consistent with the international consensus statement [20]. The metrics from each day were subsequently averaged over the CGM period to reduce day‐to‐day fluctuations. All participants continued their usual glucose‐lowering treatments during the CGM period, and were instructed to follow a standardized diet throughout monitoring [7, 10].
2.4. Assessment of DR
Fundus photography was performed using a 45° nonmydriatic digital camera (CR6‐45NM; Canon) with 6.3‐megapixel resolution. A qualified ophthalmologist, blinded to all clinical characteristics of participants, conducted the examinations following established protocols [10]. Initial DR diagnosis was based on the International Classification of Diabetic Retinopathy [21], with subsequent severity grading as: (1) no DR, (2) mild non‐proliferative DR (NPDR), (3) moderate NPDR, or (4) VTDR (vision‐threatening DR, defined as severe NPDR with concomitant proliferative DR). Uninterpretable images or detected abnormalities triggered immediate referral to a retinal specialist for confirmation.
2.5. Statistical Analysis
Statistical analyses were performed using R software (version 4.2.2, R Studio, Posit PBC., Boston, MA, USA). Participants were stratified based on serum 1,5‐AG levels (< 6.0 μg/mL, 6.0–10.0 μg/mL, and ≥ 10.0 μg/mL), with the ≥ 10.0 μg/mL group serving as the reference [15, 16]. This classification was based on previous evidence and clinically applied thresholds indicating that serum 1,5‐AG levels ≤ 6.0 μg/mL reflect meaningful short‐term hyperglycemia, typically corresponding to glucose peaks approaching 11.1 mmol/L within the preceding 1–2 weeks. In contrast, levels ≥ 10.0 μg/mL are regarded as indicating the absence of significant glucose excursions, with peak glucose generally below 10.0 mmol/L [14]. Prior studies indicate that 1,5‐AG cutoffs of 6.0 μg/mL and 10.0 μg/mL are linked to microvascular complications, cardiovascular events, mortality, and incident diabetes [15, 16, 22]. These cut‐points have been widely applied in large population studies and are consistent with the thresholds recommended by the assay manufacturer [22]. CGM‐derived metrics were analysed as tertile‐stratified categorical variables.
All continuous variables were tested for normality. Non‐normally distributed continuous variables were expressed as medians (interquartile ranges), while categorical variables were presented as percentages (%). For continuous variables, between‐group trend analysis was performed using the Jonckheere‐Terpstra test, whereas the Cochran‐Armitage trend test was employed for categorical variables. The restricted cubic splines were utilised to examine both the pattern of association and threshold effects between serum 1,5‐AG levels and the risk of DR. To evaluate independent associations of 1,5‐AG with DR (yes/no) and its severity stages (mild NPDR/moderate NPDR/VTDR), we performed binary and multinomial logistic regression, with 1,5‐AG analysed as both categorical and continuous variables (per 1‐standard deviation decrease). The analyses were conducted first unadjusted and then adjusted for sex, age, diabetes duration, BMI, systolic blood pressure, triglycerides, HDL‐C, LDL‐C, current smoking status, and eGFR. Additionally, we analysed the prevalence of DR across combined strata of serum 1,5‐AG levels and TIR tertiles, while simultaneously assessing the relationships of CGM‐derived metrics with the risk of DR. To ensure the robustness of the results, sensitivity and subgroup analyses were performed, including: (1) examining TIR using the guideline‐recommended target of > 70% [20]; (2) limiting analyses to participants with at least four consecutive CGM wear days; and (3) stratifying by HbA1c levels to explore the potential interaction between HbA1c and 1,5‐AG. All tests were two‐tailed with statistical significance set at p < 0.05.
3. Results
3.1. Clinical Characteristics of the Study Population
The final analysis included 5491 patients with type 2 diabetes stratified by serum 1,5‐AG levels (< 6.0, 6.0–10.0, and ≥ 10.0 μg/mL), with baseline characteristics presented in Table 1. The population demonstrated median values of 61.0 (52.0–67.0) years for age, 11.0 (5.5–17.0) years for diabetes duration, 8.4% (7.2–9.9)% for HbA1c, and 3.4 (1.7–7.1) μg/mL for serum 1,5‐AG, with 60.7% males. Patients with lower 1,5‐AG levels exhibited significantly poorer glycaemic control, as evidenced by higher HbA1c, lower TIR, and elevated DR prevalence (all trend p < 0.001). Among the total population, DR was diagnosed in 1387 individuals (25.3%), with affected patients showing lower serum 1,5‐AG levels and TIR compared to those without DR (both p < 0.001).
TABLE 1.
Characteristics of participants stratified by 1,5‐AG levels.
| Total | 1,5‐AG < 6.0 μg/mL | 1,5‐AG 6.0–10.0 μg/mL | 1,5‐AG ≥ 10.0 μg/mL | p for trend | |
|---|---|---|---|---|---|
| Clinical characteristics | |||||
| Participants, n | 5491 | 3813 | 814 | 864 | |
| Male, n (%) | 3332 (60.7%) | 2411 (63.2%) | 446 (54.8%) | 475 (55.0%) | < 0.001 |
| Age, years | 61.0 (52.0, 67.0) | 60.0 (51.0, 67.0) | 62.0 (55.0, 69.0) | 62.0 (56.0, 68.0) | < 0.001 |
| Diabetes duration, years | 11.0 (5.5, 17.0) | 11.0 (6.0, 17.0) | 12.0 (6.0, 17.0) | 10.0 (5.0, 16.0) | 0.057 |
| Systolic blood pressure, mmHg | 130.0 (120.0, 140.0) | 130.0 (120.0, 140.0) | 130.0 (120.0, 140.0) | 130.0 (120.0, 140.0) | 0.001 |
| Diastolic blood pressure, mmHg | 80.0 (70.0, 85.0) | 80.0 (70.0, 86.0) | 80.0 (70.0, 84.0) | 80.0 (70.0, 82.2) | < 0.001 |
| Body mass index, kg/m2 | 25.0 (22.9, 27.3) | 25.0 (22.9, 27.4) | 25.0 (22.9, 27.4) | 24.6 (22.4, 27.2) | 0.009 |
| eGFR, mL/min/1.73 m2 | 96.9 (86.2, 106.1) | 98.3 (87.9, 107.9) | 94.6 (84.8, 102.4) | 93.5 (81.6, 101.6) | < 0.001 |
| Total cholesterol, mmol/L | 4.5 (3.8, 5.3) | 4.6 (3.8, 5.3) | 4.3 (3.6, 5.1) | 4.3 (3.6, 5.0) | < 0.001 |
| Triglycerides, mmol/L | 1.5 (1.0, 2.1) | 1.5 (1.0, 2.2) | 1.4 (1.0, 2.0) | 1.3 (0.9, 1.9) | < 0.001 |
| HDL cholesterol, mmol/L | 1.0 (0.9, 1.2) | 1.0 (0.9, 1.2) | 1.0 (0.9, 1.2) | 1.1 (0.9, 1.3) | < 0.001 |
| LDL cholesterol, mmol/L | 2.7 (2.0, 3.3) | 2.8 (2.1, 3.4) | 2.5 (1.9, 3.2) | 2.5 (1.9, 3.1) | < 0.001 |
| HbA1c, % | 8.4 (7.2, 9.9) | 9.1 (8.0, 10.5) | 7.2 (6.7, 8.0) | 6.6 (6.1, 7.2) | < 0.001 |
| HbA1c, mmol/mol | 68 (55, 85) | 76 (64, 91) | 55 (50, 64) | 49 (43, 55) | < 0.001 |
| 1,5‐AG, μg/mL | 3.4 (1.7, 7.1) | 2.3 (1.4, 3.5) | 7.5 (6.7, 8.6) | 14.2 (11.6, 18.5) | < 0.001 |
| Current smoker, n (%) | 1388 (25.3%) | 1048 (27.5%) | 175 (21.5%) | 165 (19.1%) | < 0.001 |
| Anti‐diabetic agents, n (%) | |||||
| Oral hypoglycemic drugs | 3419 (62.3%) | 2163 (56.7%) | 614 (75.4%) | 642 (74.3%) | < 0.001 |
| Insulin | 3759 (68.5%) | 3011 (79.0%) | 423 (52.0%) | 325 (37.6%) | < 0.001 |
| GLP‐1 receptor agonists | 42 (0.8%) | 24 (0.6%) | 13 (1.6%) | 5 (0.6%) | 0.463 |
| Anti‐hypertension agents, n (%) | 2862 (52.1%) | 1948 (51.1%) | 449 (55.2%) | 465 (53.8%) | 0.052 |
| Lipid‐lowering agents, n (%) | 3677 (67.0%) | 2583 (67.7%) | 541 (66.5%) | 553 (64.0%) | 0.035 |
| Antiplatelet drugs, n (%) | 2214 (40.3%) | 1517 (39.8%) | 351 (43.1%) | 346 (40.0%) | 0.508 |
| DR | |||||
| With DR, n (%) | 1387 (25.3%) | 1055 (27.7%) | 181 (22.2%) | 151 (17.5%) | < 0.001 |
| CGM metrics | |||||
| Number of days CGM worn, days | 4.0 (2.0, 4.0) | 4.0 (2.0, 4.0) | 4.0 (2.0, 4.0) | 4.0 (2.0, 4.0) | 0.008 |
| Mean sensor glucose, mmol/L | 9.0 (7.8, 10.2) | 9.5 (8.4, 10.7) | 8.2 (7.4, 9.2) | 7.6 (6.9, 8.4) | < 0.001 |
| SD of mean glucose, mmol/L | 2.2 (1.6, 2.9) | 2.4 (1.8, 3.1) | 2.0 (1.5, 2.5) | 1.7 (1.3, 2.2) | < 0.001 |
| CV, % | 24.7 (19.1, 30.6) | 25.4 (20.0, 31.6) | 23.6 (18.3, 29.6) | 22.4 (17.0, 27.8) | < 0.001 |
| MAGE, mmol/L | 5.1 (3.6, 6.8) | 5.5 (3.9, 7.2) | 4.5 (3.2, 6.0) | 4.1 (2.9, 5.3) | < 0.001 |
| GRI | 30.4 (13.6, 51.2) | 37.6 (21.4, 58.4) | 20.0 (7.4, 34.4) | 12.0 (4.0, 24.7) | < 0.001 |
| TAR> 13.9, % | 3.0 (0.0, 11.0) | 5.0 (0.0, 15.0) | 0.0 (0.0, 4.0) | 0.0 (0.0, 1.0) | < 0.001 |
| TAR> 10.0, % | 29.0 (12.0, 48.0) | 37.0 (21.0, 55.0) | 17.0 (6.2, 32.0) | 9.0 (2.0, 20.0) | < 0.001 |
| TAR> 7.8, % | 65.0 (43.0, 83.0) | 72.0 (54.0, 87.0) | 52.0 (35.0, 71.8) | 37.0 (23.0, 57.0) | < 0.001 |
| TITR, % | 34.0 (17.0, 54.0) | 27.0 (13.0, 44.0) | 44.5 (27.0, 63.0) | 59.0 (42.0, 75.0) | < 0.001 |
| TIR, % | 70.0 (51.0, 86.0) | 62.0 (44.0, 78.0) | 81.0 (66.0, 92.0) | 89.0 (78.0, 96.0) | < 0.001 |
| TBR< 3.9, % | 0.0 (0.0, 1.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 2.0) | 0.0 (0.0, 2.0) | < 0.001 |
| TBR< 3.0, % | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | < 0.001 |
Note: Data are presented as median (interquartile range) or number (percentage) unless otherwise indicated.
Abbreviations: 1,5‐AG, 1,5‐anhydroglucitol; CGM, continuous glucose monitoring; CV, coefficient of variation; DR, diabetic retinopathy; eGFR, estimated glomerular filtration rate; GLP‐1, glucagon‐like peptide‐1; GRI, glycaemia risk index; HDL, high‐density lipoprotein; HbA1c, glycated haemoglobin A1c; LDL, low‐density lipoprotein; MAGE, mean amplitude of glycaemic excursions; SD, standard deviation; TAR> 13.9, time above range > 13.9 mmol/L; TAR> 10.0, time above range > 10.0 mmol/L; TAR> 7.8, time above range > 7.8 mmol/L; TBR< 3.9, time below range < 3.9 mmol/L; TBR< 3.0, time below range < 3.0 mmol/L; TIR, time in range; TITR, time in tight range.
3.2. Association of 1,5‐AG With DR
Restricted cubic spline analysis revealed a nonlinear inverse relationship between 1,5‐AG and the risk of DR (p for nonlinear = 0.0149; Figure 1). The relationship reached a plateau threshold at 1,5‐AG levels of around 10.0 μg/mL, establishing this cutoff as the reference category for further analyses.
FIGURE 1.
Odds ratios (ORs) for diabetic retinopathy by different levels of serum 1,5‐AG among patients with type 2 diabetes. The model was adjusted for age, sex, diabetes duration, BMI, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, current smoking status and eGFR. 1,5‐AG, 1,5‐anhydroglucitol; BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein.
Overall, binary logistic regression was performed with full adjustment for age, sex, diabetes duration, BMI, systolic blood pressure, lipid profile, current smoking status, and eGFR (Model 2, Table 2). Using the ≥ 10.0 μg/mL group as reference, serum 1,5‐AG levels of 6.0–10.0 μg/mL and < 6.0 μg/mL showed adjusted odds ratios (ORs) of 1.25 (95% CI 0.98–1.61) and 1.64 (95% CI 1.35–2.01) for the risk of DR, respectively. When analysed as a continuous variable, each standard deviation decrease in serum 1,5‐AG levels was associated with a 22% increased risk of DR (OR 1.22, 95% CI 1.13–1.31).
TABLE 2.
Odd ratios for diabetic retinopathy by serum 1,5‐AG among patients with type 2 diabetes.
| 1,5‐AG (μg/mL) | Per SD decrease | ||||
|---|---|---|---|---|---|
| < 6.0 | 6.0–10.0 | ≥ 10.0 | p for trend | ||
| Total | |||||
| Unadjusted model | 1.81 (1.50–2.19) | 1.35 (1.06–1.72) | 1.00 | < 0.001 | 1.25 (1.17–1.35) |
| Model 1 | 1.87 (1.55–2.27) | 1.35 (1.06–1.72) | 1.00 | < 0.001 | 1.28 (1.19–1.38) |
| Model 2 | 1.64 (1.35–2.01) | 1.25 (0.98–1.61) | 1.00 | < 0.001 | 1.22 (1.13–1.31) |
| TIR < 58% | |||||
| Unadjusted model | 1.66 (0.83–3.69) | 1.31 (0.58–3.18) | 1.00 | 0.092 | 1.22 (0.97–1.55) |
| Model 1 | 1.72 (0.85–3.83) | 1.31 (0.58–3.17) | 1.00 | 0.065 | 1.27 (1.01–1.63) |
| Model 2 | 1.86 (0.90–4.26) | 1.50 (0.64–3.75) | 1.00 | 0.072 | 1.29 (1.01–1.68) |
| TIR 58%–80% | |||||
| Unadjusted model | 1.54 (1.08–2.24) | 1.06 (0.67–1.68) | 1.00 | 0.004 | 1.18 (1.03–1.37) |
| Model 1 | 1.63 (1.14–2.40) | 1.07 (0.68–1.70) | 1.00 | 0.001 | 1.23 (1.07–1.44) |
| Model 2 | 1.64 (1.13–2.42) | 1.03 (0.65–1.65) | 1.00 | 0.001 | 1.24 (1.07–1.46) |
| TIR ≥ 80% | |||||
| Unadjusted model | 1.56 (1.20–2.04) | 1.45 (1.06–1.98) | 1.00 | 0.001 | 1.18 (1.07–1.31) |
| Model 1 | 1.63 (1.25–2.14) | 1.47 (1.08–2.01) | 1.00 | < 0.001 | 1.20 (1.09–1.33) |
| Model 2 | 1.44 (1.09–1.91) | 1.34 (0.97–1.85) | 1.00 | 0.011 | 1.14 (1.03–1.27) |
Note: Model 1 adjusted for age and sex; Model 2 further adjusted for diabetes duration, BMI, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, current smoking status and eGFR. TIR was stratified by tertiles.
Abbreviations: 1,5‐AG, 1,5‐anhydroglucitol; BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; SD, standard deviation; TIR, time in range.
Additional multivariable models evaluating DR severity (Supporting Information Table S1) demonstrated significant associations between lower 1,5‐AG levels and all stages of DR: mild NPDR (OR = 1.54, 95% CI 1.23–1.93), moderate NPDR (OR = 1.95, 95% CI 1.19–3.19), and VTDR (OR = 1.91, 95% CI 1.17–3.12).
3.3. Association Between 1,5‐AG and the Risk of DR Stratified by TIR
We further analysed the combined effect of 1,5‐AG and TIR on the risk of DR. As shown in Figure 2, participants stratified by joint 1,5‐AG and TIR showed significantly increasing prevalence of DR with decreasing serum 1,5‐AG levels, a trend consistent across all TIR strata (tertiles: < 58%, 58%–80%, and ≥ 80%). Patients with serum 1,5‐AG < 6.0 μg/mL and the lowest TIR tertile exhibited the highest DR prevalence (29.9%), while those with serum 1,5‐AG ≥ 10.0 μg/mL and the highest TIR tertile had the lowest prevalence (16.5%).
FIGURE 2.
Prevalence of diabetic retinopathy stratified by serum 1,5‐AG categories and TIR tertiles. 1,5‐AG, 1,5‐anhydroglucitol; TIR, time in range.
In subgroup analyses stratified by TIR tertiles, low 1,5‐AG levels (< 6.0 μg/mL) remained robustly associated with an elevated risk of DR in both the highest (OR = 1.44, 95% CI 1.09–1.91) and middle TIR tertiles (OR = 1.64, 95% CI 1.13–2.42) compared with high 1,5‐AG levels (≥ 10.0 μg/mL) (Table 2, Model 2). In subjects with the lowest TIR tertile, we observed a trend towards higher risk of DR related to low 1,5‐AG levels, though not statistically significant. Serum 1,5‐AG consistently demonstrated an independent association with DR across TIR subgroups when analysed as a continuous variable. While linked solely to mild NPDR at TIR ≤ 70%, serum 1,5‐AG displayed significant associations with all DR stages in subjects with TIR > 70% (Supporting Information Table S1, p for trend < 0.05, fully adjusted Model 2).
3.4. Sensitivity and Subgroup Analyses
In the subgroup classified by a TIR threshold of 70%, lower 1,5‐AG levels showed a consistent increasing trend in DR risk in the fully adjusted model (Supporting Information Table S1, p for trend < 0.001), and individuals with low 1,5‐AG had a significantly higher risk of DR when TIR was > 70% (OR 1.60, 95% CI 1.27–2.04). Similar results were also observed in participants with at least four consecutive CGM wear days (n = 3306), as shown in Supporting Information Table S2.
Likelihood ratio testing showed a significant interaction between 1,5‐AG and HbA1c (p for interaction < 0.001). Accordingly, analyses stratified by HbA1c tertiles demonstrated consistent effects of 1,5‐AG. In the lowest HbA1c tertile (< 7.5%), low 1,5‐AG levels were associated with a higher risk of DR (OR 1.38, 95% CI 1.03–1.87; Model 2, Supporting Information Table S3).
3.5. Other CGM‐Derived Metrics in Relation to DR
We also evaluated the association between other CGM‐derived metrics and the risk of DR. For glucose variability metrics, including CV, SD, and MAGE, both CV and SD showed significant associations with DR risk in the fully adjusted model for the overall population, whereas MAGE did not reach statistical significance (Supporting Information Tables S4–S6). However, among individuals in the highest tertile of TIR (≥ 80%), neither CV nor SD was significantly associated with DR (Figure 3). Similarly, other CGM‐derived metrics such as TAR> 10.0, TITR, and GRI did not show significant associations with DR in this subgroup (Supporting Information Figure S1).
FIGURE 3.
Odds ratios (ORs) for diabetic retinopathy according to 1,5‐AG categories, CV tertiles, SD tertiles and MAGE tertiles among patients with type 2 diabetes: total and highest TIR tertile The model was adjusted for age, sex, diabetes duration, BMI, systolic blood pressure, triglycerides, HDL cholesterol, LDL cholesterol, current smoking status and eGFR. CV tertiles: < 21.0%, 21.0%–28.3%, and ≥ 28.3%; SD tertiles: < 1.8 mmol/L, 1.8–2.6 mmol/L, and ≥ 2.6 mmol/L; MAGE tertiles: < 4.1 mmol/L, 4.1–6.1 mmol/L, and ≥ 6.1 mmol/L. 1,5‐AG, 1,5‐anhydroglucitol; BMI, body mass index; CV, coefficient of variation; eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; MAGE, mean amplitude of glycaemic excursions; SD, standard deviation; TIR, time in range.
4. Discussion
In a large sample of patients with type 2 diabetes, this study is the first to evaluate the combined effect of serum 1,5‐AG and TIR on the risk of DR. The results demonstrate a significant association between serum 1,5‐AG and the prevalence of DR. Notably, even in individuals with adequate TIR control (≥ 80%), those with 1,5‐AG levels < 6.0 μg/mL still exhibited a substantially increased risk of DR, while CGM‐derived glycaemic variability metrics showed no significant association in these patients. The findings indicate that serum 1,5‐AG may help identify the additional risk of DR in patients with TIR ≥ 80%.
DR is a diabetes‐specific microvascular complication and remains a leading cause of adult blindness. Several landmark large‐scale clinical trials have demonstrated that intensive glycaemic control (target HbA1c < 6.0% or 6.5%) fails to reduce either the incidence or progression of DR in type 2 diabetes [2, 23, 24]. This study provides further evidence of the residual risk of diabetic complications despite stabilised glycaemic control. One of the possible explanations may be related to postprandial glucose fluctuations, which could induce oxidative stress and vascular endothelial damage independent of average glucose levels [25].
Our findings prompt several considerations. Earlier research has emphasised that HbA1c reflects long‐term glucose levels but does not capture glycaemic variability. TIR therefore provides complementary dynamic information, as identical HbA1c levels may correspond to different TIR values depending on underlying glucose fluctuation patterns [26]. In our analysis, the association between 1,5‐AG and DR did not reach statistical significance in the lowest TIR tertile, suggesting that individuals with low TIR already exhibit poor glycaemic control and uniformly high DR risk. Beyond this, the results show that increased risk of DR persists even with optimal TIR, indicating that TIR also cannot fully characterise all aspects of glycaemic variability [27]. Patients may exhibit good TIR but large fluctuations or poor TIR with minimal variability [28]. Interestingly, we found that the additional risk revealed by serum 1,5‐AG was not captured by CGM‐derived metrics of glycaemic variability. Several explanations may account for this divergence. First, CV and SD are mathematical metrics sharing the same CGM data source with TIR, thereby offering limited additional information. Second, short‐term glycaemic variability comprises not only downward glucose fluctuations and hypoglycemia but also upward postprandial excursions [29]. Whereas CGM‐derived metrics represent the overall composite of these excursions, 1,5‐AG is particularly sensitive to postprandial glucose spikes, which are robustly linked to diabetic complications [30]. It is worth noting that the short CGM wear time in our study may have led to an underestimation of CV and SD, yet the overall findings still underscore the importance of multidimensional glucose assessment in capturing diverse glycaemic characteristics.
1,5‐AG is a six‐carbon polyol that maintains stable serum levels in healthy individuals but shows significant reduction in diabetes, providing rapid reflection of glycaemic control over approximately 1–2 weeks [31]. Physiologically, 1,5‐AG is freely filtered at the glomerulus and is normally almost completely reabsorbed in the renal tubules; however, once plasma glucose exceeds the renal threshold (approximately 10.0 mmol/L or 180 mg/dL), glucose competes with 1,5‐AG for tubular reabsorption, leading to its urinary loss and a corresponding decline in circulating levels [32]. Consistent with this mechanism, previous studies have reported a clear threshold effect of 1,5‐AG around 10.0 μg/mL [33], a pattern also evident in our spline analysis of its association with DR.
Since receiving Food and Drug Administration (FDA) approval in 2003, 1,5‐AG has attracted increasing attention. Evidence shows that 1,5‐AG is useful for diabetes screening and is independently associated with diabetic complications and mortality [15, 16, 22, 33, 34]. Owing to its sensitivity to short‐term hyperglycaemic excursions in individuals with mild‐to‐moderate dysglycaemia, prior studies support its combined use with HbA1c in clinical practice [14, 35, 36]. In patients with HbA1c below thresholds such as 7.5% or 8.0%, 1,5‐AG can reveal postprandial hyperglycemia and glucose variability not captured by HbA1c. The development of CGM has further provoked renewed consideration of 1,5‐AG [37]. Previous investigations have shown that 1,5‐AG correlates strongly with CGM‐defined metrics of glycaemic control, supporting its use as a complementary indicator to CGM and offering a practical alternative when CGM is not accessible [12, 31]. The present study extends this notion by demonstrating that 1,5‐AG identifies additional risk of DR beyond TIR, particularly in patients who appear to have adequate TIR but still develop DR.
Because the mechanism by which 1,5‐AG reflects glycaemic control is driven by competitive inhibition of its tubular reabsorption, two clinical considerations warrant attention. First, regarding its relationship with renal function, although a weak inverse association between 1,5‐AG and renal function has been observed, a recent large‐sample study [13] using dynamic renal scintigraphy to precisely evaluate renal function further demonstrated that 1,5‐AG remains a valid marker of glucose control in individuals with mild or moderate renal impairment (eGFR ≥ 30 mL/min/1.73 m2). Second, the use of SGLT2i indeed lowers circulating 1,5‐AG concentrations because pharmacologic glucosuria further suppresses tubular reabsorption. Notably, recent findings from the Atherosclerosis Risk in Communities (ARIC) cohort suggest that 1,5‐AG may serve as a potential biomarker of SGLT2i exposure and adherence, offering a possible direction for its use in this population [38]. However, despite some potential advantages, the clinical use of 1,5‐AG remains largely concentrated in countries such as Japan, the United States, and China, where it serves as an alternative glycaemic indicator. Broader clinical adoption remains limited by cost variability, nonstandardised reference ranges, and susceptibility to confounding factors (e.g., assays, diet, and treatments). Large prospective studies evaluating hard clinical endpoints across populations are essential to better define its clinical value.
Several studies have examined the association between 1,5‐AG and DR [22, 39, 40, 41]. The ARIC cohort found significantly increased risk of DR with low 1,5‐AG levels [22], consistent with findings by Kim et al. showing 1,5‐AG predicted risk of DR especially when HbA1c < 8.0% [40]. A Japanese study of 2681 community participants established 12.1 μg/mL as the optimal 1,5‐AG cutoff for DR detection [41]. Thresholds for 1,5‐AG may differ by ethnicity and dietary patterns, with prior literature reporting that healthy Japanese individuals have approximately 0.5 μg/mL lower concentrations than age‐matched U.S. populations [35]. Dietary intake, including plant‐derived foods rich in 1,5‐AG and variations in sugar consumption patterns, can also influence circulating levels [32]. In addition, assay‐related differences may introduce minor variability [42].
This study represents the first investigation combining 1,5‐AG with CGM for assessing the risk of DR, with key strengths including its large sample size and comprehensive clinical characterisation. However, several limitations should be acknowledged. First, as a single‐centre observational study, the findings require validation through larger cohort studies. Second, as noted above, the duration of CGM monitoring represents an important limitation. Although 14‐day CGM is preferable for capturing typical glycaemic profiles, our findings should be interpreted with caution, and longer monitoring would help validate the results. Nonetheless, this limitation may be partially offset by the large sample size of the study, as several CGM metrics can be reliably assessed with much shorter durations [43]. In addition, prior evidence [44] shows that TIR estimated for 3 days of CGM data aligns closely with values derived from 3‐month reference monitoring. Extending the monitoring duration yielded modest improvements, with R 2 increasing from 0.77 (3 days) to 0.84 (7 days) and 0.88 (14 days). Third, CGM data were collected during hospitalisation, where individuals followed a standard diet and routine inpatient care; thus, the glucose profiles obtained under these conditions may not fully reflect glucose control in daily life. In addition, the reliance on a single inpatient measurement of 1,5‐AG may limit its ability to represent usual glycaemic status. These factors may affect the generalisability of our findings to outpatient or community settings. Fourth, individuals treated with SGLT2i were excluded from this study. Therefore, the findings may have limited applicability to patients receiving SGLT2i. Finally, the absence of adjustment for antidiabetic medication use may have influenced the results, and future studies with more detailed treatment information are warranted.
In conclusion, this large‐scale study of patients with type 2 diabetes confirms that serum 1,5‐AG is significantly associated with the prevalence of DR. Notably, our study demonstrates for the first time that 1,5‐AG may help identify additional risk of DR even among patients with well‐controlled TIR. These findings support the clinical value of monitoring 1,5‐AG alongside CGM to provide complementary risk stratification. Glycaemic management may benefit from the combination of multidimensional indicators, including both CGM metrics and circulating biomarkers.
Author Contributions
D.P. and J.Z. conceived and designed the study. H.S., J.N., and J.L. contributed to data collection, data analysis, and writing the manuscript. W.L., W.Z., and Y.W. contributed to data collection and analysis. W.L. and W.Z. contributed to conducting the study and data collection. X.M., D.P., and J.Z. contributed to interpretation of data and revision of the manuscript. D.P. and J.Z. are the guarantors of this work, have full access to all the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript.
Funding
This work was funded by Noncommunicable Chronic Diseases‐National Science and Technology Major Project (2024ZD0532000, 2024ZD0532001), Shanghai Leading Talent Programme of Eastern Talent Plan (LJ2024121), Shenzhen Medical Research Fund (C2406002), and Sanming Project of Medicine in Shenzhen (SZSM202311019).
Ethics Statement
This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and was in accordance with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Consent
Informed consent was obtained from all the individual participants included in the study.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting Information S1
Acknowledgements
We would like to thank all the involved clinicians, nurses, and technicians for dedicating their time and skills to the completion of this study. We would like to thank all participants for their dedication to data collection and laboratory measurements.
Contributor Information
Danfeng Peng, Email: pengdanfeng@shsmu.edu.cn.
Jian Zhou, Email: zhoujian@sjtu.edu.cn.
Data Availability Statement
Restrictions apply to the availability of data generated or analysed during this study to preserve patient confidentiality, or because they were used under licence. Data are however available from the authors upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information S1
Data Availability Statement
Restrictions apply to the availability of data generated or analysed during this study to preserve patient confidentiality, or because they were used under licence. Data are however available from the authors upon reasonable request.