Association of glycation gap with hypoglycemia CGM indices in Japanese patients with type 2 diabetes mellitus

Association of glycation gap with hypoglycemia CGM indices in Japanese patients with type 2 diabetes mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of glycation gap with hypoglycemia CGM indices in Japanese patients with type 2 diabetes mellitus Satomi Sonoda, Yosuke Okada, Tomoya Mita, Keiichi Torimoto, Kenichi Tanaka, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5739052/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The glycation gap (GGap), defined as the discrepancy between glycated hemoglobin (HbA1c) and the value estimated from actual blood glucose level, is associated with diabetic complications, but its association with hypoglycemia remains unclear. We evaluated the association between GGap and continuous glucose monitoring (CGM)-based hypoglycemic indices in patients with type 2 diabetes mellitus (T2DM). Baseline data from a multicenter cohort of 999 T2DM patients without cardiovascular disease were analyzed. The difference between HbA1c and estimated A1c (eA1c) was defined as the GGap, and various CGM indices were compared among low (≤0.16), medium (0.60) GGap tertile groups. In the high GGap group, the average blood glucose was lower, while the Time Below Range <3.9 mmol/L (TBR <3.9 ) and <3.0 mmol/L (TBR <3.0 ), and low blood glucose index (LBGI) were higher than the low and middle GGap groups. Patients with minimum blood glucose levels of <3.9, <3.0 mmol/L, and TBR <3.9 ≥4%, and TBR <3.0 ≥1% had significantly higher GGap values. This is the first study to show the strong association of high GGap with CGM-based hypoglycemic indices with T2DM. To achieve diabetes treatment that effectively prevents the progression of diabetic complications, it is essential to assess the GGap of the individual patient before intensifying diabetes management. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Diabetes complications glycation gap hypoglycemia continuous glucose monitoring glucose variability Figures Figure 1 Figure 2 Figure 3 Introduction The level of glycated hemoglobin (HbA1c) reflects the glycation rate of hemoglobin in red blood cells over the past 2 to 4 months and is used as a standard indicator of glycemic control. It is associated with the onset and progression of diabetic microvascular complications and mortality risk [1–6]. However, even with the same HbA1c level, differences in the risk of developing diabetic complications are sometimes observed. This is because HbA1c levels are not a direct measure of blood glucose levels; hence, they may be higher or lower than actual blood glucose levels. The discrepancy between actual blood glucose level and HbA1c is termed the glycation gap (GGap), which has been found to be associated with retinopathy [7], nephropathy [8], as well as cardiovascular events and overall mortality [9]. This makes it an important index for the long-term management and personalized care of patients with diabetes. Hypoglycemia has been suggested as a reason for the association between cardiovascular events, mortality, and GGap [10]. A sub-analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial reported a higher frequency of severe hypoglycemia in patients with large differences between HbA1c levels estimated from fasting blood glucose levels and actual HbA1c levels [11]. A large GGap indicates that the HbA1c level is higher than actual blood glucose levels, which could increase the risk of hypoglycemia if treatment is intensified based on the HbA1c level, although this remains unclear. Until recently, the GGap was quantified using indices calculated from HbA1c levels and fructosamine [12–14]. Nowadays, with continuous glucose monitoring (CGM) becoming widely used for direct evaluation of blood glucose levels, it is easy to calculate the estimated A1c (eA1c) from the CGM-derived average blood glucose levels. This has enabled a more accurate assessment of the GGap. In addition, CGM allows detailed evaluation of hypoglycemia. To the best of our knowledge, there have been no reports that evaluated the relationship between GGap and hypoglycemia. Furthermore, it is not clear whether patients with a large GGap experience increased hypoglycemia. The present study was designed to determine the relationship between GGap and CGM hypoglycemic indices in patients with type 2 diabetes mellitus (T2DM). Results Baseline clinical characteristics The study included 999 patients (608 men and 391 women), and the clinical background of all patients has been reported previously [15]. The mean age was 64.6 ± 9.6 years, body mass index (BMI) 24.6 ± 3.9 kg/m², HbA1c 7.1 ± 0.8%, and eGFR 73.4 ± 20.6 mL/min/1.73m², with a median duration of T2DM of 11.0 (6.0–18.0) years. The overall eA1c was 6.7 ± 0.8%, and the GGap was 0.38 ± 0.57%. Diabetes medications were used by 89.5% of the patients, with dipeptidyl peptidase 4 inhibitors in 57.8%, metformin in 54.4%, sodium-glucose transport protein 2 (SGLT2) inhibitors in 23.1%, sulfonylureas (SUs) in 12.7%, and insulin in 15.8%. DR was detected in 22.2% of the patients, with the following stage distribution in the right/left eyes: SDR 12.6/12.3%, PPDR 4.9/4.8%, and PDR 3.7/3.6%. Furthermore, 27.0% of the patients had diabetic nephropathy, with the following stage distribution: normoalbuminuria 68.0%, microalbuminuria 24.7%, and macroalbuminuria 7.3%. Comparison of clinical characteristics by GGap tertiles The participants were divided into three groups based on their GGap values (Table 1 ). The GGap > 0.60 group included more women (43.9%, compared with 33.4% for GGap ≤ 0.16 and 39.9% for GGap 0.16–0.60 , p = 0.02), patients with longer duration of diabetes (13.9 (8.0–20.0) years, compared with 10.2 (5.0–16.0) and 11.0 (6.0–17.0), respectively, p < 0.001), higher BMI (25.0 ± 4.0 kg/m², compared with 24.2 ± 3.8 and 24.6 ± 3.8, respectively, p = 0.023), and higher HbA1c levels (7.5 ± 0.9%, compared with 6.8 ± 0.7 and 6.9 ± 0.6, respectively, p 0.60 group included a larger proportion of patients with DR and more advanced stages of retinopathy. Furthermore, patients of the GGap > 0.60 group showed higher uses of SGLT2 inhibitor (p = 0.040), metformin (p = 0.013), glucagon-like peptide-1 (GLP-1) receptor agonist (p < 0.001), and insulin (p 0.60 group, compared with only 10.6% of the GGap ≤ 0.16 and 11.4% of the GGap 0.16–0.60 groups. Furthermore, the daily insulin dose was also higher in the GGap > 0.60 group (18.0 (11.0–26.0) units/day, compared with 15.0 (9.0–27.0) and 9.5 (6.0–20.0), respectively, p = 0.016). Table 1 Patient demographic and background characteristics by glycation gap tertile. Low (≤ 0.16) n = 329 Moderate (> 0.16 to ≤ 0.60) n = 333 High (> 0.60) n = 337 P value Age (years) 65.1 ± 9.8 64.8 ± 9.1 63.9 ± 10.0 0.253 Female gender (%) 110 (33.4) 133 (39.9) 148 (43.9) 0.020 Estimated duration of diabetes (years) 10.2 (5.0–16.0) 11.0 (6.0–17.0) 13.9 (8.0–20.0) < 0.001 Body mass index (kg/m 2 ) 24.2 ± 3.8 24.6 ± 3.8 25.0 ± 4.0 0.023 Systolic blood pressure (mmHg) 130 ± 16 132 ± 15 132 ± 14 0.264 Diastolic blood pressure (mmHg) 75 ± 11 76 ± 11 76 ± 11 0.532 Diabetic complications (%) Diabetic neuropathy 96 (29.2) 86 (25.8) 104 (30.9) 0.341 Diabetic retinopathy 64 (19.5) 67 (20.1) 91 (27.0) 0.034 Rt. NDR/SDR/PPDR/PDR 267 (81)/42 (13)/11 (3)/9 (3) 272 (82)/41 (12)/14 (4)/6 (2) 248 (74)/43 (13)/24 (7)/22 (6) 0.005 Lt. NDR/SDR/PPDR/PDR 269 (82)/41 (12)/11 (3)/8 (2) 271 (81)/42 (13)/14 (4)/6 (2) 252 (75)/40 (12)/23 (7)/22 (6) 0.006 Diabetic nephropathy 93 (28) 73 (21.9) 104 (30.9) 0.028 Normo-/micro-/macro-albuminuria (%) 217 (67)/77 (24)/30 (9) 233 (71)/81 (24)/16 (5) 220 (66)/86 (26)/26 (8) 0.250 HbA1c (%) 6.8 ± 0.7 6.9 ± 0.6 7.5 ± 0.9 < 0.001 HbA1c (mmol/mol) 50.6 ± 8.1 51.8 ± 6.3 58.6 ± 9.6 < 0.001 Estimated glomerular filtration rate (mL/min/ 1.73 m 2 ) 74.9 ± 21.4 73.0 ± 17.8 72.4 ± 22.4 0.262 Urinary albumin excretion (mg/g Creatine) 14.9 (6.0–43.6) 13.1 (6.0–35.0) 15.5 (7.4–50.1) 0.077 Use of oral glucose-lowering agents (%) Sodium-glucose cotransporter 2 inhibitors (%) 64 (19.5) 74 (22.2) 93(27.6) 0.040 Dipeptidyl peptidase-4 inhibitors (%) 183 (55.6) 193 (58.0) 201 (59.6) 0.574 Metformin (%) 158 (48.0) 186 (55.9) 199 (59.1) 0.013 Sulfonylureas (%) 39 (11.9) 46 (13.8) 42 (12.5) 0.740 Glinides (%) 20 (6.1) 19 (5.7) 29 (8.6) 0.269 α-glucosidase inhibitors (%) 47 (14.3) 59 (17.7) 66 (19.6) 0.186 Thiazolidinediones (%) 41 (12.5) 46 (13.8) 56 (16.6) 0.294 Glucagon-like peptide-1 antagonists (%) 16 (4.9) 17 (5.1) 41 (12.2) < 0.001 Use of insulin (%) 35 (10.6) 38 (11.4) 85 (25.2) < 0.001 Dose of insulin (unit/day) 15.0 (9.0–27.0) 9.5 (6.0–20.0) 18.0 (11.0–26.0) 0.016 Data are mean ± SD or number (%) of patients. Kruskal-Wallis test was used for continuous variables. The chi-squared test was used for categorical data. NDR, no diabetic retinopathy; SDR, simple diabetic retinopathy; PPDR, preproliferative diabetic retinopathy; PDR, proliferative diabetic retinopathy Comparison of FLP-CGM–derived metrics by GGap tertiles Table 2 shows the differences in CGM metrics among the three GGap groups. In the GGap > 0.60 group, blood glucose level was lower (7.47 ± 1.74 mmol/L, compared with 8.52 ± 2.03 and 7.42 ± 1.34, respectively, p < 0.001), and the CV, a marker of glucose variability, was higher (26.81 ± 0.40%, compared with 25.78 ± 5.70 and 26.02 ± 5.15, respectively, p = 0.054). Additionally, the hypoglycemic indicators, both the TBR < 3.9 and TBR 0.60 group than the other two groups. Table 2 FLP-CGM–derived metrics by glycation gap tertile. Low (≤ 0.16) n = 329 Moderate (> 0.16 to ≤ 0.60) n = 333 High (> 0.60) n = 337 P value Mean glucose (mmol/L) 8.52 ± 2.03 7.42 ± 1.34 7.47 ± 1.74 < 0.001 CV (%) 25.78 ± 5.70 26.02 ± 5.15 26.81 ± 6.40 0.054 MAGE (mmol/L) 5.70 ± 2.10 5.25 ± 1.80 5.43 ± 2.08 0.013 SD (mmol/L) 2.19 ± 0.69 1.93 ± 0.53 1.99 ± 0.62 < 0.001 TIR (%) 72.96 ± 21.71 83.59 ± 13.76 79.98 ± 17.94 13.9 mmol/L (%) 6.44 ± 12.12 1.91 ± 5.26 3.23 ± 8.73 10 mmol/L (%) 25.99 ± 22.04 14.59 ± 14.33 16.44 ± 18.43 < 0.001 TBR < 3.9 mmol/L (%) 1.06 ± 3.75 1.81 ± 3.62 3.58 ± 5.99 < 0.001 TBR < 3.0 mmol/L (%) 0.23 ± 1.38 0.20 ± 0.78 0.55 ± 2.09 < 0.001 HBGI 7.05 ± 5.71 4.58 ± 3.05 5.14 ± 4.42 < 0.001 LBGI 1.04 ± 1.49 1.51 ± 1.30 2.11 ± 1.96 < 0.001 MODD (mmol/L) 1.84 ± 0.71 1.61 ± 0.47 1.76 ± 0.69 < 0.001 IQR (mmol/L) 2.27 ± 0.95 1.97 ± 0.58 2.19 ± 0.85 < 0.001 eA1c (%) 6.98 ± 0.86 6.51 ± 0.56 6.54 ± 0.73 < 0.001 GMI (%) 6.98 ± 0.87 6.51 ± 0.58 6.53 ± 0.75 < 0.001 Data are mean ± SD or number (%) of patients. Kruskal-Wallis test was used for continuous variables. FLP-CGM, FreeStyle Libre Pro continuous glucose monitoring; CV, coefficient of variation; MAGE, mean amplitude of glycemic excursion; SD, standard deviation; TIR, time in range; TAR, time above range; TBR, time below range; HBGI, high blood glucose index; LBGI, low blood glucose index; MODD, mean of daily differences, IQR, interquartile range; eA1c, estimates A1c; GMI, glucose management indicator. Comparison of GGap with FLP-CGM–derived hypoglycemic metrics For this analysis, we divided the participants into two groups in order to assess the effect of severity of hypoglycemia. First, patients were divided into those with minimum blood glucose level of < 70 and ≥ 70 mg/dL. Second, patients were divided into those with minimum blood glucose of < 54 and ≥ 54 mg/dL. Third, patients were divided into those with TBR < 3.9 of ≥ 4 and < 4%, and TBR < 3.0 of ≥ 1% and < 1 (Fig. 1 A-D). In all these groups, the GGap was significantly higher in the group with severe hypoglycemia. Our analysis also showed that the GGap cutoff value to achieve a TBR < 3.9 mmol/L at < 4% was 0.36%, and the cutoff value for achieving a TBR < 3.0 mmol/L at < 1% was 0.39% (Fig. 2 ). Analysis of GGap in patients with DR and diabetic nephropathy The relationship between DR and GGap was analyzed by comparing the GGap among patients free of retinopathy (n = 777), with SDR (n = 133), PPDR (n = 50), and PDR (n = 39). The GGap values for the above respective groups were 0.36 ± 0.51, 0.41 ± 0.67, 0.57 ± 0.75, and 0.61 ± 0.79, with a significantly larger GGap observed in patients with more advanced diabetic complications (p = 0.005) (Fig. 3 A). In contrast, analysis of the relationship between DR and GGap based on the severity of renal complications [which included patients with normoalbuminuria (n = 670), microalbuminuria (n = 244), and macroalbuminuria (n = 72)], showed no significant differences in GGap values among the three groups (normoalbuminuria: 0.39 ± 0.53, microalbuminuria: 0.41 ± 0.60, macroalbuminuria: 0.32 ± 0.77) (Fig. 3 B). Relationship of GGap with DTR-QOL and lifestyle factors With regard to the relationship between GGap and lifestyle factors, 11.6% of subjects of the GGap > 0.60 group reported experiencing hypoglycemic symptoms in the past month, which was significantly higher than that in the GGap ≤ 0.16 (4.3%) and GGap 0.16–0.60 groups (4.2%) (p 0.60 group than the other two groups (Supplementary Table 1). Discussion This is the first study to describe the association of high GGap with CGM-derived indicators of hypoglycemia in patients with T2DM. The study also determined the GGap cutoff value associated with a higher risk of hypoglycemia based on CGM-derived indicators of glucose management, and demonstrated the presence of a relationship between GGap increase and progression of retinopathy. Based on these findings, we believe it is essential to fully understand the GGap in each individual case before intensifying diabetes treatment, in order to avoid hypoglycemia and achieve treatment that prevents the progression of diabetic complications. In this study, we demonstrated the association of high GGap with CGM-derived hypoglycemic indicators. Previous studies reported that GGap is associated with cardiovascular events and mortality, with hypoglycemia being suggested as a contributing factor [10]. A sub-analysis of the ACCORD trial reported higher frequency of severe hypoglycemia in patients with large discrepancy between fasting glucose-estimated HbA1c and actual HbA1c levels [11]. Our study is the first to accurately assess the GGap using CGM and identified the association of GGap with CGM markers of hypoglycemia. The rate of hemoglobin glycation varies among individuals, leading to the occurrence of GGap [25–27]. This gap differs between individuals. Therefore, the reliance of clinicians on HbA1c only in the management of T2DM may increase the incidence of hypoglycemia. In fact, in this study, the high GGap group included a significantly higher number of users of SGLT2 inhibitors, metformin, GLP-1 receptor agonists, and insulin, suggesting clinicians' attempt to intensify pharmacotherapy in this group. Particularly in the GGap > 0.60 group, both the proportion of insulin users and the daily insulin dosage were higher, suggesting that intensified treatment might contribute to an increase in hypoglycemia. Previous studies using the hemoglobin glycation index (HGI), which represents the difference between HbA1c and fasting glucose-predicted HbA1c levels, reported an increase in hypoglycemic events in insulin-treated patients with T2DM who had high HGI values [28]. In this study, we identified the GGap cutoff value required to achieve hypoglycemia management goals using CGM. While there is not yet sufficient evidence regarding the standard for the GGap, Gu et al. [10] examined the relationship between GGap calculated from GA and mortality, and reported that patients with GGap exceeding 0.38% were at high risk of all-cause mortality and cardiovascular death. Patients who experience hypoglycemic episodes are reported to have a two- to four-fold higher risk of cardiovascular events and mortality [29–31], suggesting that this relationship may be due to hypoglycemia. However, Gu et al. [10] did not examine thoroughly the relationship of GGap with hypoglycemia. Our study is the first to demonstrate that a GGap of 0.36–0.38% or higher is associated with increased risk of hypoglycemia. We also demonstrated the association of progression of DR and GGap. Significantly higher proportions of DR and advanced-stage retinopathy were noted in subjects of the GGap > 0.60 group. The extent of the GGap remains consistent over long periods of time [32], and it seems to be associated with the progression of DR [7, 33], nephropathy [7, 34, 35], and the prevalence of macroalbuminuria [8]. When the GGap is high-meaning that the actual HbA1c level is higher-cells in various organs and tissues, not just red blood cells, which are susceptible to diabetic complications, are exposed to similar glycation. Therefore, individuals with high GGap values are likely to experience more cellular damage than those with a lower GGap, even at the same level of glucose exposure. Consequently, the risk of various complications is expected to increase in patients with high GGap. One of the strengths of this study is its large-scale, multicenter collaborative design. To the best of our knowledge, this study of 999 patients with T2DM is the largest to date examining the utility of CGM-derived GGap. However, this study also has a few limitations. First, it included only Japanese patients with T2DM. Since it has been reported that the average blood glucose and HbA1c levels differ among ethnic groups [36, 37], the GGap cutoff values may vary between populations. Second, although this study found the association of CGM-derived GGap with HbA1c values and indices of hypoglycemia, it was a cross-sectional study; therefore, a causal relationship remains unclear. To address this, we are currently conducting a long-term follow-up study in the same cohort [15]. Third, the CGM measurements in this study were obtained from a maximum of 8 days of CGM data. Therefore, the study period may be insufficient for full evaluation of the overall glycemic control of the participants. Furthermore, while we used a blinded CGM system to prevent participants from altering their behavior based on glucose measurements, it is impossible to completely eliminate the placebo effect. Fourth, it cannot be ruled out that there may be discrepancies between actual blood glucose levels and those measured by the FLP-CGM. Studies on the mean absolute relative difference (MARD) in T2DM have reported that, within the blood glucose range of 70–250 mg/dL, the MARD ranges from 11.4–16.7%, with slightly lower accuracy in the hypoglycemic range [38]. In our study, we also examined patients' self-reported hypoglycemic symptoms, and the results showed that the GGap > 0.60 group experienced a higher frequency of hypoglycemic symptoms. Conclusion There is substantial evidence in support of the use of HbA1c as a treatment target for glycemic control and the ultimate goal of prevention of diabetic complications. However, this study suggests that in patients with large GGap values, relying solely on HbA1c values during treatment may increase the risk of hypoglycemia. The increase in hypoglycemia among patients with high GGap values may lead to further progression of glycation in tissues and consequently, increased T2DM-related complications. To achieve diabetes treatment that effectively prevents the progression of complications, it is important to understand the value of GGap of each individual before intensifying diabetes management. Future large-scale prospective studies of other ethnic groups may be required to validate our findings. Materials and methods Study design This study is an exploratory sub-analysis of an ongoing prospective observational study [15] designed to determine the relationship between glucose variability, as assessed by CGM, and the incidence of composite cardiovascular events over a 5-year follow-up period in Japanese patients with T2DM. Using baseline data from this prospective observational study, we evaluated the relationship between GGap (assessed by CGM and HbA1c levels) and hypoglycemic indices. This study is registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), a non-profit organization in Japan, and meets the requirements of the International Committee of Medical Journal Editors (UMIN000032325). Study population The study population consisted of Japanese patients with T2DM who were seen regularly at the Outpatient clinic of the Departments of Diabetes of 34 medical facilities in Japan. The study design, inclusion criteria, and exclusion criteria have been published previously [15]. The study targeted outpatients aged between 30 and 80 years with stable glycemic control, excluding those with history of cardiovascular events. Among the screened participants, those who met the eligibility criteria were invited to participate in the study. A total of 1,000 eligible participants were recruited between May 2018 and March 2019; however, one individual withdrew consent, leaving 999 participants in the analysis. All experimental were approved by the ethics committees of the representative institution, Juntendo University Hospital, and all other participating medical facilities in accordance with the Declaration of Helsinki and current Japanese regulations. Written informed consent was obtained from each participant after receiving a full explanation of the study. Definitions of various types of diabetic complications Diabetic neuropathy was defined as meeting two of the following three criteria: the presence of subjective symptoms believed to be due to diabetic polyneuropathy, decreased or absent Achilles tendon reflexes bilaterally, and reduced vibration sensation at both medial malleoli. Diabetic retinopathy (DR) was diagnosed by trained ophthalmologists and classified into four stages: no diabetic retinopathy (NDR), simple diabetic retinopathy (SDR), pre-proliferative diabetic retinopathy (PPDR), and proliferative diabetic retinopathy (PDR). Diabetic nephropathy was classified according to the level of urinary albumin excretion (UAE) into normal albuminuria (< 30 mg/gCr), microalbuminuria (≥ 30 to < 300 mg/gCr) and macroalbuminuria (≥ 300 mg/gCr). Biochemical tests Fasting blood samples were collected, and renal function tests, lipids, and HbA1c (National Glycohemoglobin Standardization Program) levels were determined using standard methods. UAE was measured using the latex agglutination method with spot urine samples. The estimated glomerular filtration rate (eGFR) was calculated using a previously defined formula [16]. Glucose metrics by CGM and calculation of GGap The FreeStyle Libre Pro (FLP) (Abbott Japan, Tokyo, Japan) CGM device (FLP-CGM), was used at baseline to measure blood glucose levels every 15 min over a period of 14 days [15, 17]. Apart from wearing the FLP-CGM, no restrictions were imposed on the daily life activities of the participants. Previous studies showed that the accuracy of the FLP-CGM decreases during the first 24 h after attachment (Day 1 to Day 2) and during the last 4 days of the 14-day monitoring period [18]. Accordingly, data from the intermediate 8-day period were used for analysis. The downloaded dataset was analyzed, and the average blood glucose level was estimated using the FLP-CGM data. Glucose variability was assessed using standard deviation (SD), coefficient of variation (CV) [19], and mean amplitude of glycemic excursions (MAGE). The CV (%) was calculated by dividing the standard deviation (SD) by the corresponding mean blood glucose level. MAGE was calculated using the arithmetic mean of the differences between consecutive peaks and nadirs when the difference exceeded 1 SD of the mean blood glucose level [20]. Time in Range (TIR) was defined as the percentage of time spent with blood glucose level between 3.9 to 10.0 mmol/L, Time Above Range (TAR) above 10 mmol/L and TAR above 13.9 mmol/L were defined as the percentage of time spent above these corresponding glucose levels, and Time Below Range (TBR) below 3.9 mmol/L (TBR < 3.9 ) and TBR below 3.0 mmol/L (TBR < 3.0 ) were defined as the percentages of time below the corresponding glucose levels, as described previously [19]. The average glucose levels, CV, TIR, TAR, and TBR are major outcomes measurable by CGM [21]. The low blood glucose index (LBGI) and high blood glucose index (HBGI) were calculated by converting blood glucose levels into risk scores [22]. In addition, the mean of daily differences (MODD) [23] and interquartile ranges (IQRs) were calculated to assess day-to-day glucose variability. MODD was calculated as the mean of the absolute differences in blood glucose levels measured at the same time on consecutive days, and the IQR was calculated using the values at the same time points during the observation period. The eA1c has been developed to derive an estimated HbA1c level based on the average blood glucose levels [24]. The eA1c was calculated using the following formula: eA1c (%) = 3.38 + 0.02345 × (average glucose level [mg/dL]). In this study, GGap was defined as the difference between HbA1c and eA1c level (GGap = HbA1c – eA1c). Statistical analysis Continuous variables were presented as mean ± SD values or median (interquartile range), and categorical variables as numbers (percentages) of patients. Participants were divided into three groups based on the tertiles of the GGap values: low group (GGap ≤ 0.16%, GGap ≤ 0.16 ), middle group (GGap of > 0.16 to ≤ 0.60%, GGap 0.16–0.60 ); and high group (GGap > 0.60%, GGap > 0.60 ). Continuous data were compared using analysis of variance, Kruskal-Wallis test or Student's t-test as appropriate, and categorical data were compared using the chi-squared test. The relationship between various parameters measured by the FLP-CGM and GGap, as well as patient background factors, were compared among the three GGap groups. In addition, participants were classified according to the stage of diabetic nephropathy and DR, and also divided into groups that met or did not meet the following criteria: minimum blood glucose level < 3.9 mmol/L, minimum blood glucose level < 3.0 mmol/L, TBR < 4%, and TBR < 1%, and the GGap was compared among these groups. Finally, receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff value of the GGap for predicting the achievement of TBR of < 4% and TBR of < 1%. The cutoff value was determined based on the Youden index (sensitivity + specificity − 1). All statistical tests were two-sided with a significance level of 5%. All analyses were performed using SAS software version 9.4 (SAS Institute, Cary, NC). Declarations Acknowledgments We thank the study investigators (Supplementary Table S1) and participants for their contribution to this study. The authors also acknowledge the assistance of D. Takayama and H. Yamada (Soiken Holdings, Inc., Tokyo, Japan) and N. Sakaguchi (University of Occupational and Environmental Health, Japan, Kitakyushu, Japan). Author contributions All authors contributed to the article’s validation, writing, review, and editing. They read and approved the final article. S.S., Y.O., K.T., T.M, K.T., S.W., N.K., H.Y., K.M., K.N., and N.I. collected the data. M.G. analyzed the data. H.W. received funding for the study. S.W. investigated the data. Y.O., T.M., and H.W. were responsible for project administration. Y.T., I.S., and H.W. supervised the article. S.S. wrote the original draft. Data availability The datasets used and analyzed during the current study are available from the corresponding author on rea- sonable request. Additional information Author disclosure statement H.W. has received research funds from Abbott Japan and is a member of the Advisory Board of Abbott Japan. N. K. received lecture fees from Abbott Japan Co., Ltd. The other authors declare no conflicts of interest. Funding Information This study was funded by the Japan Agency for Medical Research and Development (AMED) under Grant Number JP20ek0210105 (to H.W.) and by the Manpei Suzuki Diabetes Foundation (to H.W.). References Stratton IM, et al. 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Diabetes Care. 38 , 1067-1074 (2015). Cohen RM, et al. Evidence for independent heritability of the glycation gap (glycosylation gap) fraction of HbA1c in nondiabetic twins. Diabetes Care. 29 , 1739-1743 (2006). Cohen RM, et al. Discordance between HbA1c and fructosamine: evidence for a glycosylation gap and its relation to diabetic nephropathy. Diabetes Care. 26 , 163-167 (2003). Nayak AU, et al. Evidence for consistency of the glycation gap in diabetes. Diabetes Care. 34 , 1712-1716 (2011). Mita T, et al. Protocol of a prospective observational study on the relationship between glucose fluctuation and cardiovascular events in patients with type 2 diabetes. Diabetes Ther. 10 , 1565-1575 (2019). Matsuo S, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am. J. Kidney Dis. 53 , 982-992 (2009). Wakasugi S, et al. Associations between continuous glucose monitoring-derived metrics and arterial stiffness in Japanese patients with type 2 diabetes. Cardiovasc. Diabetol . 20 , (2021). Boscari F, et al. Head-to-head comparison of the accuracy of Abbott FreeStyle Libre and Dexcom G5 mobile. Nutri. Metabol. Cardiovasc. Dis. 28 , 425-427 (2018). ElSayed NA, et al. Glycemic targets: Standards of care in diabetes-2023. Diabetes Care. 46 , S97-S110 (2023). Service FJ, et al. Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 19 , 644-655 (1970). Battelino T, et al. Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care. 42 , 1593-1603 (2019). Kovatchev BP, et al. Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data. Diabetes Technol. Ther. 5 , 817-828 (2003). Hill NR, et al. Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups. Diabetes Technol. Ther. 13 , 921-928 (2011). Beck RW, et al. The fallacy of average: How using HbA1c alone to assess glycemic control can be misleading. Diabetes Care. 40 , 994-999 (2017). Hudson PR, et al. Differences in rates of glycation (glycation index) may significantly affect individual HbA1c results in type 1 diabetes. Ann. Clin. Biochem. 36 , 451-459 (1999). Nayak AU, Holland MR, Macdonald DR, Nevill A, Singh BM. Evidence for consistency of the glycation gap in diabetes. Diabetes Care. 34 , 1712-1716 (2011). Christidis G, et al. Skin advanced glycation end-products as indicators of the metabolic profile in diabetes mellitus: Correlations with glycemic control, liver phenotypes and metabolic biomarkers. BMC Endocr. Disord, 24 , 31 (2024). Klein KR, et al. Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in DEVOTE. BMJ Open Diabetes Res. Care. 9 , e002339 (2021). Hsu PF, et al. Association of clinical symptomatic hypoglycemia with cardiovascular events and total mortality in type 2 diabetes: A nationwide population-based study. Diabetes Care. 36 , 894-900 (2013). Desouza CV, Bolli GB, Fonseca V. Hypoglycemia, diabetes, and cardiovascular events. Diabetes Care. 33 , 1389-1394 (2010). Lee AK, et al. The association of severe hypoglycemia with incident cardiovascular events and mortality in adults with type 2 diabetes. Diabetes Care. 41 , 104-111 (2018). Kim MK, Yun KJ, Kwon HS, Baek KH, Song KH. Discordance in the levels of hemoglobin A1C and glycated albumin: calculation of the glycation gap based on glycated albumin level. J. Diabetes Complications. 30 , 477-481 (2016). Cohen RM, LeCaire TJ, Lindsell CJ, Smith EP, D'Alessio DJ. Relationship of prospective GHb to glycated serum proteins in incident diabetic retinopathy: implications of the glycation gap for mechanism of risk prediction. Diabetes Care. 31 , 151-153 (2008). Rodríguez-Segade S, Rodríguez J, Cabezas-Agricola JM, Casanueva FF, Camina F. Progression of nephropathy in type 2 diabetes: The glycation gap is a significant predictor after adjustment for glycohemoglobin (Hb A1c). Clin. Chem. 57 , 264-271 (2011). Cohen RM, Holmes YR, Chenier TC, Joiner CH. Discordance between HbA1c and fructosamine: Evidence for a glycosylation gap and its relation to diabetic nephropathy. Diabetes Care. 26 , 163-167 (2003). Bergenstal RM, et al. Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels. Ann. Intern. Med. 167 , 95-102 (2017). Grimsmann JM, et al. Glucose management indicator based on sensor data and laboratory HbA1c in people with type 1 diabetes from the DPV database: differences by sensor type. Diabetes Care. 43 , e111-112 (2020). Galindo RJ, et al. Comparison of the FreeStyle Libre Pro Flash continuous glucose monitoring (CGM) system and point-of-care capillary glucose testing in hospitalized patients with type 2 diabetes treated with basal-bolus insulin regimen. Diabetes Care. 43 (2020) Additional Declarations Competing interest reported. H.W. has received research funds from Abbott Japan and is a member of the Advisory Board of Abbott Japan. N. K. received lecture fees from Abbott Japan Co., Ltd. The other authors declare no conflicts of interest. Supplementary Files Supplementarytable1.docx Supplementary Material Supplementary Table S1, S2 Supplementarytable2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5739052","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400483794,"identity":"f1bfda99-657b-4e4a-be95-0901505e3f5d","order_by":0,"name":"Satomi Sonoda","email":"","orcid":"","institution":"University of Occupational and Environmental Health","correspondingAuthor":false,"prefix":"","firstName":"Satomi","middleName":"","lastName":"Sonoda","suffix":""},{"id":400483795,"identity":"40485153-b6ec-4ab3-8fc1-1f52791237f7","order_by":1,"name":"Yosuke 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04:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5739052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5739052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73787925,"identity":"74d45918-43b9-4b6b-bf0a-af84f245c902","added_by":"auto","created_at":"2025-01-14 16:32:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGGap by FLP-CGM–derived metrics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Comparison of GGap of patients with minimum blood glucose level of \u0026lt;70 and ≥70 mg/dL. (B) Comparison of GGap of patients with minimum blood glucose level of \u0026lt;54 and ≥70 mg/dL. (C) Comparison of GGap of patients with TBR\u003csup\u003e\u0026lt;3.9 mmol/L\u003c/sup\u003e of \u0026lt;4 and ≥4%. (D) Comparison of GGap of patients with TBR\u003csup\u003e\u0026lt;3.0 mmol/L\u003c/sup\u003e of \u0026lt;1 and ≥1%.\u003c/p\u003e\n\u003cp\u003eData are mean±SD. *p\u0026lt;0.001, by the Student's t-test.\u003c/p\u003e\n\u003cp\u003eGGap, glycation gap; FLP-CGM, FreeStyle Libre Pro continuous glucose monitoring; TBR: time below range.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5739052/v1/fa3815981dffb7dd5eed9004.png"},{"id":73789901,"identity":"6600c888-8957-4874-9cb6-28c738f03504","added_by":"auto","created_at":"2025-01-14 16:48:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTBR\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026lt;3.9 mmol/L \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e(%) and TBR\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026lt;3.0 mmol/L \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e(%) predicted\u003c/strong\u003e GGap \u003cstrong\u003ecutoff values based on ROC curve analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The cutoff value of GGap for achieving TBR\u003csup\u003e\u0026lt;3.9 mmol/L\u003c/sup\u003e at \u0026lt;4% was 0.36% (area under the ROC curve: 0.70; 95% CI: 0.66-0.74).\u003cbr\u003e\n(B) The cutoff value of GGap for achieving TBR\u003csup\u003e\u0026lt;3.0 mmol/L\u003c/sup\u003e at \u0026lt;1% was 0.39% (area under the ROC curve: 0.65; 95% CI: 0.59-0.89).\u003cbr\u003e\nGGap, glycation gap; %CV: % coefficient of variation; AG: average glucose; CI: confidence interval; ROC: receiver operating characteristic; TBR: time below range.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5739052/v1/8e42a44178130bd39bb2f9c1.png"},{"id":73787932,"identity":"a86206bc-0296-46bb-8b01-f06266aef729","added_by":"auto","created_at":"2025-01-14 16:32:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGGap by diabetic retinopathy and nephropathy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Relationship between severity of diabetic retinopathy and GGap. The GGap for each stage of DR was as follows: NDR (n=777): 0.363±0.515, SDR (n=133): 0.407±0.665, PPDR (n=50): 0.571±0.750, and PDR (n=39): 0.608±0.789, with significant differences among the four groups (p=0.006).\u003c/p\u003e\n\u003cp\u003e(B) Relationship between severity of diabetic nephropathy and GGap. The GGap for each stage of diabetic nephropathy was as follows: normoalbuminuria (n=670): 0.388±0.526, microalbuminuria (n=244): 0.411±0.603, macroalbuminuria (n=72): 0.324±0.770, with no significant differences the three groups.\u003c/p\u003e\n\u003cp\u003eData are mean±SD. *p\u0026lt;0.001, by analysis of variance.\u003c/p\u003e\n\u003cp\u003eGGap, glycation gap; NDR: no diabetic retinopathy; SDR: simple diabetic retinopathy; PPDR: preproliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5739052/v1/2e6644dcd1175020a82da7f6.png"},{"id":90639797,"identity":"ca438b53-1390-4f87-8b8a-c254b776e781","added_by":"auto","created_at":"2025-09-05 06:18:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1450844,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5739052/v1/968afe55-c62a-461d-a054-40e1aaaeef25.pdf"},{"id":73787927,"identity":"49e98788-050f-452a-9b56-bc0adb495753","added_by":"auto","created_at":"2025-01-14 16:32:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table S1, S2\u003c/p\u003e","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5739052/v1/4353ddccde3b70b475c02a03.docx"},{"id":73787930,"identity":"80823467-02be-49c0-984c-2f7b1418252c","added_by":"auto","created_at":"2025-01-14 16:32:33","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38676,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5739052/v1/2fbd5f533c3fca56d34c960b.docx"}],"financialInterests":"Competing interest reported. H.W. has received research funds from Abbott Japan and is a member of the Advisory Board of Abbott Japan. N. K. received lecture fees from Abbott Japan Co., Ltd. The other authors declare no conflicts of interest.","formattedTitle":"Association of glycation gap with hypoglycemia CGM indices in Japanese patients with type 2 diabetes mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe level of glycated hemoglobin (HbA1c) reflects the glycation rate of hemoglobin in red blood cells over the past 2 to 4 months and is used as a standard indicator of glycemic control. It is associated with the onset and progression of diabetic microvascular complications and mortality risk [1\u0026ndash;6]. However, even with the same HbA1c level, differences in the risk of developing diabetic complications are sometimes observed. This is because HbA1c levels are not a direct measure of blood glucose levels; hence, they may be higher or lower than actual blood glucose levels. The discrepancy between actual blood glucose level and HbA1c is termed the glycation gap (GGap), which has been found to be associated with retinopathy [7], nephropathy [8], as well as cardiovascular events and overall mortality [9]. This makes it an important index for the long-term management and personalized care of patients with diabetes.\u003c/p\u003e \u003cp\u003eHypoglycemia has been suggested as a reason for the association between cardiovascular events, mortality, and GGap [10]. A sub-analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial reported a higher frequency of severe hypoglycemia in patients with large differences between HbA1c levels estimated from fasting blood glucose levels and actual HbA1c levels [11]. A large GGap indicates that the HbA1c level is higher than actual blood glucose levels, which could increase the risk of hypoglycemia if treatment is intensified based on the HbA1c level, although this remains unclear.\u003c/p\u003e \u003cp\u003eUntil recently, the GGap was quantified using indices calculated from HbA1c levels and fructosamine [12\u0026ndash;14]. Nowadays, with continuous glucose monitoring (CGM) becoming widely used for direct evaluation of blood glucose levels, it is easy to calculate the estimated A1c (eA1c) from the CGM-derived average blood glucose levels. This has enabled a more accurate assessment of the GGap. In addition, CGM allows detailed evaluation of hypoglycemia.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, there have been no reports that evaluated the relationship between GGap and hypoglycemia. Furthermore, it is not clear whether patients with a large GGap experience increased hypoglycemia. The present study was designed to determine the relationship between GGap and CGM hypoglycemic indices in patients with type 2 diabetes mellitus (T2DM).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBaseline clinical characteristics\u003c/h2\u003e \u003cp\u003eThe study included 999 patients (608 men and 391 women), and the clinical background of all patients has been reported previously [15]. The mean age was 64.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years, body mass index (BMI) 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 kg/m\u0026sup2;, HbA1c 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8%, and eGFR 73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 mL/min/1.73m\u0026sup2;, with a median duration of T2DM of 11.0 (6.0\u0026ndash;18.0) years. The overall eA1c was 6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8%, and the GGap was 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57%. Diabetes medications were used by 89.5% of the patients, with dipeptidyl peptidase 4 inhibitors in 57.8%, metformin in 54.4%, sodium-glucose transport protein 2 (SGLT2) inhibitors in 23.1%, sulfonylureas (SUs) in 12.7%, and insulin in 15.8%. DR was detected in 22.2% of the patients, with the following stage distribution in the right/left eyes: SDR 12.6/12.3%, PPDR 4.9/4.8%, and PDR 3.7/3.6%. Furthermore, 27.0% of the patients had diabetic nephropathy, with the following stage distribution: normoalbuminuria 68.0%, microalbuminuria 24.7%, and macroalbuminuria 7.3%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison of clinical characteristics by GGap tertiles\u003c/h3\u003e\n\u003cp\u003eThe participants were divided into three groups based on their GGap values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group included more women (43.9%, compared with 33.4% for GGap\u003csup\u003e\u0026le;\u0026thinsp;0.16\u003c/sup\u003e and 39.9% for GGap\u003csup\u003e0.16\u0026ndash;0.60\u003c/sup\u003e, p\u0026thinsp;=\u0026thinsp;0.02), patients with longer duration of diabetes (13.9 (8.0\u0026ndash;20.0) years, compared with 10.2 (5.0\u0026ndash;16.0) and 11.0 (6.0\u0026ndash;17.0), respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher BMI (25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 kg/m\u0026sup2;, compared with 24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 and 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8, respectively, p\u0026thinsp;=\u0026thinsp;0.023), and higher HbA1c levels (7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9%, compared with 6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 and 6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), than the other two GGap groups. In addition, the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group included a larger proportion of patients with DR and more advanced stages of retinopathy. Furthermore, patients of the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group showed higher uses of SGLT2 inhibitor (p\u0026thinsp;=\u0026thinsp;0.040), metformin (p\u0026thinsp;=\u0026thinsp;0.013), glucagon-like peptide-1 (GLP-1) receptor agonist (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and insulin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the other groups. Insulin therapy was used in 25.2% of the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group, compared with only 10.6% of the GGap\u003csup\u003e\u0026le;\u0026thinsp;0.16\u003c/sup\u003e and 11.4% of the GGap\u003csup\u003e0.16\u0026ndash;0.60\u003c/sup\u003e groups. Furthermore, the daily insulin dose was also higher in the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group (18.0 (11.0\u0026ndash;26.0) units/day, compared with 15.0 (9.0\u0026ndash;27.0) and 9.5 (6.0\u0026ndash;20.0), respectively, p\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographic and background characteristics by glycation gap tertile.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026le;\u0026thinsp;0.16) n\u0026thinsp;=\u0026thinsp;329\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate (\u0026gt;\u0026thinsp;0.16 to \u0026le;\u0026thinsp;0.60) n\u0026thinsp;=\u0026thinsp;333\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;0.60) n\u0026thinsp;=\u0026thinsp;337\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale gender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated duration of diabetes (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2 (5.0\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (6.0\u0026ndash;17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9 (8.0\u0026ndash;20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic complications (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRt. NDR/SDR/PPDR/PDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (81)/42 (13)/11 (3)/9 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (82)/41 (12)/14 (4)/6 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (74)/43 (13)/24 (7)/22 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLt. NDR/SDR/PPDR/PDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 (82)/41 (12)/11 (3)/8 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271 (81)/42 (13)/14 (4)/6 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252 (75)/40 (12)/23 (7)/22 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormo-/micro-/macro-albuminuria (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (67)/77 (24)/30 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (71)/81 (24)/16 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220 (66)/86 (26)/26 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (mmol/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(mL/min/ 1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary albumin excretion (mg/g Creatine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9 (6.0\u0026ndash;43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1 (6.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.5 (7.4\u0026ndash;50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of oral glucose-lowering agents (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium-glucose cotransporter 2 inhibitors (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDipeptidyl peptidase-4 inhibitors (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlinides (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-glucosidase inhibitors (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiazolidinediones (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucagon-like peptide-1 antagonists (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of insulin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDose of insulin (unit/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 (9.0\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5 (6.0\u0026ndash;20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0 (11.0\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or number (%) of patients.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eKruskal-Wallis test was used for continuous variables. The chi-squared test was used for categorical data.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNDR, no diabetic retinopathy; SDR, simple diabetic retinopathy; PPDR, preproliferative diabetic retinopathy; PDR, proliferative diabetic retinopathy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of FLP-CGM–derived metrics by GGap tertiles\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the differences in CGM metrics among the three GGap groups. In the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group, blood glucose level was lower (7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74 mmol/L, compared with 8.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03 and 7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the CV, a marker of glucose variability, was higher (26.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40%, compared with 25.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70 and 26.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.15, respectively, p\u0026thinsp;=\u0026thinsp;0.054). Additionally, the hypoglycemic indicators, both the TBR\u003csup\u003e\u0026lt;\u0026thinsp;3.9\u003c/sup\u003e and TBR\u003csup\u003e\u0026lt;\u0026thinsp;3.0\u003c/sup\u003e were higher, and the LBGI was significantly greater, in the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group than the other two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFLP-CGM\u0026ndash;derived metrics by glycation gap tertile.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (\u0026le;\u0026thinsp;0.16) n\u0026thinsp;=\u0026thinsp;329\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate (\u0026gt;\u0026thinsp;0.16 to \u0026le;\u0026thinsp;0.60) n\u0026thinsp;=\u0026thinsp;333\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;0.60) n\u0026thinsp;=\u0026thinsp;337\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e26.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAGE (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e72.96\u0026thinsp;\u0026plusmn;\u0026thinsp;21.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e83.59\u0026thinsp;\u0026plusmn;\u0026thinsp;13.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e79.98\u0026thinsp;\u0026plusmn;\u0026thinsp;17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAR\u003csup\u003e\u0026gt;\u0026thinsp;13.9 mmol/L\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAR\u003csup\u003e\u0026gt;\u0026thinsp;10 mmol/L\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.99\u0026thinsp;\u0026plusmn;\u0026thinsp;22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e16.44\u0026thinsp;\u0026plusmn;\u0026thinsp;18.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csup\u003e\u0026lt;\u0026thinsp;3.9 mmol/L\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csup\u003e\u0026lt;\u0026thinsp;3.0 mmol/L\u003c/sup\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMODD (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQR (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or number (%) of patients.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eKruskal-Wallis test was used for continuous variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFLP-CGM, FreeStyle Libre Pro continuous glucose monitoring; CV, coefficient of variation; MAGE, mean amplitude of glycemic excursion; SD, standard deviation; TIR, time in range; TAR, time above range; TBR, time below range; HBGI, high blood glucose index; LBGI, low blood glucose index; MODD, mean of daily differences, IQR, interquartile range; eA1c, estimates A1c; GMI, glucose management indicator.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of GGap with FLP-CGM–derived hypoglycemic metrics\u003c/h3\u003e\n\u003cp\u003eFor this analysis, we divided the participants into two groups in order to assess the effect of severity of hypoglycemia. First, patients were divided into those with minimum blood glucose level of \u0026lt;\u0026thinsp;70 and \u0026ge;\u0026thinsp;70 mg/dL. Second, patients were divided into those with minimum blood glucose of \u0026lt;\u0026thinsp;54 and \u0026ge;\u0026thinsp;54 mg/dL. Third, patients were divided into those with TBR\u003csup\u003e\u0026lt;\u0026thinsp;3.9\u003c/sup\u003e of \u0026ge;\u0026thinsp;4 and \u0026lt;\u0026thinsp;4%, and TBR\u003csup\u003e\u0026lt;\u0026thinsp;3.0\u003c/sup\u003e of \u0026ge;\u0026thinsp;1% and \u0026lt;\u0026thinsp;1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-D). In all these groups, the GGap was significantly higher in the group with severe hypoglycemia. Our analysis also showed that the GGap cutoff value to achieve a TBR\u0026thinsp;\u0026lt;\u0026thinsp;3.9 mmol/L at \u0026lt;\u0026thinsp;4% was 0.36%, and the cutoff value for achieving a TBR\u0026thinsp;\u0026lt;\u0026thinsp;3.0 mmol/L at \u0026lt;\u0026thinsp;1% was 0.39% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis of GGap in patients with DR and diabetic nephropathy\u003c/h3\u003e\n\u003cp\u003eThe relationship between DR and GGap was analyzed by comparing the GGap among patients free of retinopathy (n\u0026thinsp;=\u0026thinsp;777), with SDR (n\u0026thinsp;=\u0026thinsp;133), PPDR (n\u0026thinsp;=\u0026thinsp;50), and PDR (n\u0026thinsp;=\u0026thinsp;39). The GGap values for the above respective groups were 0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51, 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67, 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75, and 0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79, with a significantly larger GGap observed in patients with more advanced diabetic complications (p\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, analysis of the relationship between DR and GGap based on the severity of renal complications [which included patients with normoalbuminuria (n\u0026thinsp;=\u0026thinsp;670), microalbuminuria (n\u0026thinsp;=\u0026thinsp;244), and macroalbuminuria (n\u0026thinsp;=\u0026thinsp;72)], showed no significant differences in GGap values among the three groups (normoalbuminuria: 0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53, microalbuminuria: 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60, macroalbuminuria: 0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of GGap with DTR-QOL and lifestyle factors\u003c/h2\u003e \u003cp\u003eWith regard to the relationship between GGap and lifestyle factors, 11.6% of subjects of the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group reported experiencing hypoglycemic symptoms in the past month, which was significantly higher than that in the GGap\u003csup\u003e\u0026le;\u0026thinsp;0.16\u003c/sup\u003e (4.3%) and GGap\u003csup\u003e0.16\u0026ndash;0.60\u003c/sup\u003e groups (4.2%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, for the DTR-QOL scores, both the average scores for all items and the scores for each individual item were significantly lower in the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group than the other two groups (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to describe the association of high GGap with CGM-derived indicators of hypoglycemia in patients with T2DM. The study also determined the GGap cutoff value associated with a higher risk of hypoglycemia based on CGM-derived indicators of glucose management, and demonstrated the presence of a relationship between GGap increase and progression of retinopathy. Based on these findings, we believe it is essential to fully understand the GGap in each individual case before intensifying diabetes treatment, in order to avoid hypoglycemia and achieve treatment that prevents the progression of diabetic complications.\u003c/p\u003e \u003cp\u003eIn this study, we demonstrated the association of high GGap with CGM-derived hypoglycemic indicators. Previous studies reported that GGap is associated with cardiovascular events and mortality, with hypoglycemia being suggested as a contributing factor [10]. A sub-analysis of the ACCORD trial reported higher frequency of severe hypoglycemia in patients with large discrepancy between fasting glucose-estimated HbA1c and actual HbA1c levels [11]. Our study is the first to accurately assess the GGap using CGM and identified the association of GGap with CGM markers of hypoglycemia.\u003c/p\u003e \u003cp\u003eThe rate of hemoglobin glycation varies among individuals, leading to the occurrence of GGap [25\u0026ndash;27]. This gap differs between individuals. Therefore, the reliance of clinicians on HbA1c only in the management of T2DM may increase the incidence of hypoglycemia. In fact, in this study, the high GGap group included a significantly higher number of users of SGLT2 inhibitors, metformin, GLP-1 receptor agonists, and insulin, suggesting clinicians' attempt to intensify pharmacotherapy in this group. Particularly in the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group, both the proportion of insulin users and the daily insulin dosage were higher, suggesting that intensified treatment might contribute to an increase in hypoglycemia. Previous studies using the hemoglobin glycation index (HGI), which represents the difference between HbA1c and fasting glucose-predicted HbA1c levels, reported an increase in hypoglycemic events in insulin-treated patients with T2DM who had high HGI values [28].\u003c/p\u003e \u003cp\u003eIn this study, we identified the GGap cutoff value required to achieve hypoglycemia management goals using CGM. While there is not yet sufficient evidence regarding the standard for the GGap, Gu et al. [10] examined the relationship between GGap calculated from GA and mortality, and reported that patients with GGap exceeding 0.38% were at high risk of all-cause mortality and cardiovascular death. Patients who experience hypoglycemic episodes are reported to have a two- to four-fold higher risk of cardiovascular events and mortality [29\u0026ndash;31], suggesting that this relationship may be due to hypoglycemia. However, Gu et al. [10] did not examine thoroughly the relationship of GGap with hypoglycemia. Our study is the first to demonstrate that a GGap of 0.36\u0026ndash;0.38% or higher is associated with increased risk of hypoglycemia.\u003c/p\u003e \u003cp\u003eWe also demonstrated the association of progression of DR and GGap. Significantly higher proportions of DR and advanced-stage retinopathy were noted in subjects of the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group. The extent of the GGap remains consistent over long periods of time [32], and it seems to be associated with the progression of DR [7, 33], nephropathy [7, 34, 35], and the prevalence of macroalbuminuria [8]. When the GGap is high-meaning that the actual HbA1c level is higher-cells in various organs and tissues, not just red blood cells, which are susceptible to diabetic complications, are exposed to similar glycation. Therefore, individuals with high GGap values are likely to experience more cellular damage than those with a lower GGap, even at the same level of glucose exposure. Consequently, the risk of various complications is expected to increase in patients with high GGap.\u003c/p\u003e \u003cp\u003eOne of the strengths of this study is its large-scale, multicenter collaborative design. To the best of our knowledge, this study of 999 patients with T2DM is the largest to date examining the utility of CGM-derived GGap. However, this study also has a few limitations. First, it included only Japanese patients with T2DM. Since it has been reported that the average blood glucose and HbA1c levels differ among ethnic groups [36, 37], the GGap cutoff values may vary between populations. Second, although this study found the association of CGM-derived GGap with HbA1c values and indices of hypoglycemia, it was a cross-sectional study; therefore, a causal relationship remains unclear. To address this, we are currently conducting a long-term follow-up study in the same cohort [15]. Third, the CGM measurements in this study were obtained from a maximum of 8 days of CGM data. Therefore, the study period may be insufficient for full evaluation of the overall glycemic control of the participants. Furthermore, while we used a blinded CGM system to prevent participants from altering their behavior based on glucose measurements, it is impossible to completely eliminate the placebo effect. Fourth, it cannot be ruled out that there may be discrepancies between actual blood glucose levels and those measured by the FLP-CGM. Studies on the mean absolute relative difference (MARD) in T2DM have reported that, within the blood glucose range of 70\u0026ndash;250 mg/dL, the MARD ranges from 11.4\u0026ndash;16.7%, with slightly lower accuracy in the hypoglycemic range [38]. In our study, we also examined patients' self-reported hypoglycemic symptoms, and the results showed that the GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e group experienced a higher frequency of hypoglycemic symptoms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere is substantial evidence in support of the use of HbA1c as a treatment target for glycemic control and the ultimate goal of prevention of diabetic complications. However, this study suggests that in patients with large GGap values, relying solely on HbA1c values during treatment may increase the risk of hypoglycemia. The increase in hypoglycemia among patients with high GGap values may lead to further progression of glycation in tissues and consequently, increased T2DM-related complications. To achieve diabetes treatment that effectively prevents the progression of complications, it is important to understand the value of GGap of each individual before intensifying diabetes management. Future large-scale prospective studies of other ethnic groups may be required to validate our findings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study is an exploratory sub-analysis of an ongoing prospective observational study [15] designed to determine the relationship between glucose variability, as assessed by CGM, and the incidence of composite cardiovascular events over a 5-year follow-up period in Japanese patients with T2DM. Using baseline data from this prospective observational study, we evaluated the relationship between GGap (assessed by CGM and HbA1c levels) and hypoglycemic indices.\u003c/p\u003e \u003cp\u003e This study is registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), a non-profit organization in Japan, and meets the requirements of the International Committee of Medical Journal Editors (UMIN000032325).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe study population consisted of Japanese patients with T2DM who were seen regularly at the Outpatient clinic of the Departments of Diabetes of 34 medical facilities in Japan. The study design, inclusion criteria, and exclusion criteria have been published previously [15]. The study targeted outpatients aged between 30 and 80 years with stable glycemic control, excluding those with history of cardiovascular events. Among the screened participants, those who met the eligibility criteria were invited to participate in the study. A total of 1,000 eligible participants were recruited between May 2018 and March 2019; however, one individual withdrew consent, leaving 999 participants in the analysis.\u003c/p\u003e \u003cp\u003e All experimental were approved by the ethics committees of the representative institution, Juntendo University Hospital, and all other participating medical facilities in accordance with the Declaration of Helsinki and current Japanese regulations. Written informed consent was obtained from each participant after receiving a full explanation of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions of various types of diabetic complications\u003c/h2\u003e \u003cp\u003eDiabetic neuropathy was defined as meeting two of the following three criteria: the presence of subjective symptoms believed to be due to diabetic polyneuropathy, decreased or absent Achilles tendon reflexes bilaterally, and reduced vibration sensation at both medial malleoli. Diabetic retinopathy (DR) was diagnosed by trained ophthalmologists and classified into four stages: no diabetic retinopathy (NDR), simple diabetic retinopathy (SDR), pre-proliferative diabetic retinopathy (PPDR), and proliferative diabetic retinopathy (PDR). Diabetic nephropathy was classified according to the level of urinary albumin excretion (UAE) into normal albuminuria (\u0026lt;\u0026thinsp;30 mg/gCr), microalbuminuria (\u0026ge;\u0026thinsp;30 to \u0026lt;\u0026thinsp;300 mg/gCr) and macroalbuminuria (\u0026ge;\u0026thinsp;300 mg/gCr).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical tests\u003c/h2\u003e \u003cp\u003eFasting blood samples were collected, and renal function tests, lipids, and HbA1c (National Glycohemoglobin Standardization Program) levels were determined using standard methods. UAE was measured using the latex agglutination method with spot urine samples. The estimated glomerular filtration rate (eGFR) was calculated using a previously defined formula [16].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGlucose metrics by CGM and calculation of GGap\u003c/h2\u003e \u003cp\u003eThe FreeStyle Libre Pro (FLP) (Abbott Japan, Tokyo, Japan) CGM device (FLP-CGM), was used at baseline to measure blood glucose levels every 15 min over a period of 14 days [15, 17]. Apart from wearing the FLP-CGM, no restrictions were imposed on the daily life activities of the participants. Previous studies showed that the accuracy of the FLP-CGM decreases during the first 24 h after attachment (Day 1 to Day 2) and during the last 4 days of the 14-day monitoring period [18]. Accordingly, data from the intermediate 8-day period were used for analysis. The downloaded dataset was analyzed, and the average blood glucose level was estimated using the FLP-CGM data. Glucose variability was assessed using standard deviation (SD), coefficient of variation (CV) [19], and mean amplitude of glycemic excursions (MAGE). The CV (%) was calculated by dividing the standard deviation (SD) by the corresponding mean blood glucose level. MAGE was calculated using the arithmetic mean of the differences between consecutive peaks and nadirs when the difference exceeded 1 SD of the mean blood glucose level [20]. Time in Range (TIR) was defined as the percentage of time spent with blood glucose level between 3.9 to 10.0 mmol/L, Time Above Range (TAR) above 10 mmol/L and TAR above 13.9 mmol/L were defined as the percentage of time spent above these corresponding glucose levels, and Time Below Range (TBR) below 3.9 mmol/L (TBR\u003csup\u003e\u0026lt;\u0026thinsp;3.9\u003c/sup\u003e) and TBR below 3.0 mmol/L (TBR\u003csup\u003e\u0026lt;\u0026thinsp;3.0\u003c/sup\u003e) were defined as the percentages of time below the corresponding glucose levels, as described previously [19]. The average glucose levels, CV, TIR, TAR, and TBR are major outcomes measurable by CGM [21]. The low blood glucose index (LBGI) and high blood glucose index (HBGI) were calculated by converting blood glucose levels into risk scores [22]. In addition, the mean of daily differences (MODD) [23] and interquartile ranges (IQRs) were calculated to assess day-to-day glucose variability. MODD was calculated as the mean of the absolute differences in blood glucose levels measured at the same time on consecutive days, and the IQR was calculated using the values at the same time points during the observation period.\u003c/p\u003e \u003cp\u003eThe eA1c has been developed to derive an estimated HbA1c level based on the average blood glucose levels [24]. The eA1c was calculated using the following formula: eA1c (%)\u0026thinsp;=\u0026thinsp;3.38\u0026thinsp;+\u0026thinsp;0.02345 \u0026times; (average glucose level [mg/dL]). In this study, GGap was defined as the difference between HbA1c and eA1c level (GGap\u0026thinsp;=\u0026thinsp;HbA1c \u0026ndash; eA1c).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD values or median (interquartile range), and categorical variables as numbers (percentages) of patients. Participants were divided into three groups based on the tertiles of the GGap values: low group (GGap\u0026thinsp;\u0026le;\u0026thinsp;0.16%, GGap\u003csup\u003e\u0026le;\u0026thinsp;0.16\u003c/sup\u003e), middle group (GGap of \u0026gt;\u0026thinsp;0.16 to \u0026le;\u0026thinsp;0.60%, GGap\u003csup\u003e0.16\u0026ndash;0.60\u003c/sup\u003e); and high group (GGap\u0026thinsp;\u0026gt;\u0026thinsp;0.60%, GGap\u003csup\u003e\u0026gt;\u0026thinsp;0.60\u003c/sup\u003e). Continuous data were compared using analysis of variance, Kruskal-Wallis test or Student's t-test as appropriate, and categorical data were compared using the chi-squared test. The relationship between various parameters measured by the FLP-CGM and GGap, as well as patient background factors, were compared among the three GGap groups. In addition, participants were classified according to the stage of diabetic nephropathy and DR, and also divided into groups that met or did not meet the following criteria: minimum blood glucose level\u0026thinsp;\u0026lt;\u0026thinsp;3.9 mmol/L, minimum blood glucose level\u0026thinsp;\u0026lt;\u0026thinsp;3.0 mmol/L, TBR\u0026thinsp;\u0026lt;\u0026thinsp;4%, and TBR\u0026thinsp;\u0026lt;\u0026thinsp;1%, and the GGap was compared among these groups. Finally, receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff value of the GGap for predicting the achievement of TBR of \u0026lt;\u0026thinsp;4% and TBR of \u0026lt;\u0026thinsp;1%. The cutoff value was determined based on the Youden index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1). All statistical tests were two-sided with a significance level of 5%. All analyses were performed using SAS software version 9.4 (SAS Institute, Cary, NC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the study investigators (Supplementary Table S1) and participants for their contribution to this study. The authors also acknowledge the assistance of D. Takayama and H. Yamada (Soiken Holdings, Inc., Tokyo, Japan) and N. Sakaguchi (University of Occupational and Environmental Health, Japan, Kitakyushu, Japan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the article’s validation, writing, review, and editing. They read and approved the final article. S.S., Y.O., K.T., T.M, K.T., S.W., N.K., H.Y., K.M., K.N., and N.I. collected the data. M.G. analyzed the data. H.W. received funding for the study. S.W. investigated the data. Y.O., T.M., and H.W. were responsible for project administration. Y.T., I.S., and H.W. supervised the article. S.S. wrote the original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on rea- sonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor disclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.W. has received research funds from Abbott Japan and is a member of the Advisory Board of Abbott Japan. N. K. received lecture fees from Abbott Japan Co., Ltd. The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Japan Agency for Medical Research and Development (AMED) under Grant Number JP20ek0210105 (to H.W.) and by the Manpei Suzuki Diabetes Foundation (to H.W.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStratton IM, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): Prospective observational study. \u003cem\u003eBr. Med. J.\u003c/em\u003e\u003cstrong\u003e321\u003c/strong\u003e, 405-412 (2000).\u003c/li\u003e\n\u003cli\u003eUK Prospective Diabetes Study (UKPDS) Group. 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Racial differences in the relationship of glucose concentrations and hemoglobin A1c levels. \u003cem\u003eAnn. Intern. Med.\u003c/em\u003e\u003cstrong\u003e167\u003c/strong\u003e, 95-102 (2017).\u003c/li\u003e\n\u003cli\u003eGrimsmann JM, et al. Glucose management indicator based on sensor data and laboratory HbA1c in people with type 1 diabetes from the DPV database: differences by sensor type. \u003cem\u003eDiabetes Care.\u003c/em\u003e\u003cstrong\u003e43\u003c/strong\u003e, e111-112 (2020).\u003c/li\u003e\n\u003cli\u003eGalindo RJ, et al. Comparison of the FreeStyle Libre Pro Flash continuous glucose monitoring (CGM) system and point-of-care capillary glucose testing in hospitalized patients with type 2 diabetes treated with basal-bolus insulin regimen. \u003cem\u003eDiabetes Care.\u003c/em\u003e\u003cstrong\u003e43\u003c/strong\u003e (2020)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"glycation gap, hypoglycemia, continuous glucose monitoring, glucose variability","lastPublishedDoi":"10.21203/rs.3.rs-5739052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5739052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe glycation gap (GGap), defined as the discrepancy between glycated hemoglobin (HbA1c) and the value estimated from actual blood glucose level, is associated with diabetic complications, but its association with hypoglycemia remains unclear. We evaluated the association between GGap and continuous glucose monitoring (CGM)-based hypoglycemic indices in patients with type 2 diabetes mellitus (T2DM). Baseline data from a multicenter cohort of 999 T2DM patients without cardiovascular disease were analyzed. The difference between HbA1c and estimated A1c (eA1c) was defined as the GGap, and various CGM indices were compared among low (≤0.16), medium (\u0026lt;0.16 to ≤0.60), and high (\u0026gt;0.60) GGap tertile groups. In the high GGap group, the average blood glucose was lower, while the Time Below Range \u0026lt;3.9 mmol/L (TBR\u003csup\u003e\u0026lt;3.9\u003c/sup\u003e) and \u0026lt;3.0 mmol/L (TBR\u003csup\u003e\u0026lt;3.0\u003c/sup\u003e), and low blood glucose index (LBGI) were higher than the low and middle GGap groups. Patients with minimum blood glucose levels of \u0026lt;3.9, \u0026lt;3.0 mmol/L, and TBR\u003csup\u003e\u0026lt;3.9\u003c/sup\u003e≥4%, and TBR\u003csup\u003e\u0026lt;3.0\u003c/sup\u003e≥1% had significantly higher GGap values. This is the first study to show the strong association of high GGap with CGM-based hypoglycemic indices with T2DM. To achieve diabetes treatment that effectively prevents the progression of diabetic complications, it is essential to assess the GGap of the individual patient before intensifying diabetes management.\u003c/p\u003e","manuscriptTitle":"Association of glycation gap with hypoglycemia CGM indices in Japanese patients with type 2 diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 16:32:29","doi":"10.21203/rs.3.rs-5739052/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5df234cd-6135-43f2-bc57-f742dfa57183","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42684742,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus"},{"id":42684743,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Diabetes complications"}],"tags":[],"updatedAt":"2025-09-05T05:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 16:32:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5739052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5739052","identity":"rs-5739052","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}