==== Front Diabetol Metab Syndr Diabetol Metab Syndr Diabetology & Metabolic Syndrome 1758-5996 BioMed Central London 37386515 1071 10.1186/s13098-023-01071-4 Research Differential correlation between time in range and eGFR or albuminuria in type 2 diabetes Jin Xuguang 1 Yang Xinyi 1 Xu Yixin 1 Liang Jingjing 1 Liu Chunyan 1 Guo Qingyu 1 Wang Wei 1 Feng Zhouqin 1 Yuan Yanyu 3 Zhou Hui 2 Zhang Zhen 2 Jiang Wenwen 3 Liang Yue 1 Lu Bin 1 Shao Jiaqing shaojiaq@hotmail.com 1 Zhong Yong zhongyongnj@163.com 1 Gu Ping guping@nju.edu.cn 1 1 grid.41156.37 0000 0001 2314 964X Medical School, Affiliated Jinling Hospital, Department of Endocrinology, Nanjing University, 305 East Zhongshan Road, Nanjing, 210002 Jiangsu China 2 grid.284723.8 0000 0000 8877 7471 Department of Endocrinology, Jinling Hospital, First School of Clinical Medicine, Southern Medical University, Nanjing, China 3 grid.89957.3a 0000 0000 9255 8984 Department of Endocrinology, the affiliated Jinling Hospital of Nanjing Medical University, Nanjing, Jiangsu China 5 5 2023 5 5 2023 2023 15 9211 4 2023 27 4 2023 © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Introduction As a CGM-derived indicator, ‘time in range’ (TIR) is emerging as a key indicator for accurate assessment of glycaemic control. However, there is few report focusing on the correlation of TIR with albumuria and renal fuction. The aim of this work was to investigate whether TIR, as well as nocturnal TIR and hypoglycaemic events is related to the presence and severity of albuminuria and decrease of eGFR in type 2 diabetes. Research design and methods A total of 823 patients were enrolled in this study. All patients received continuous glucose monitoring, TIR indicating the percentage of time that blood glucose was in the range of 3.9–10.0 mmol/L. The Spearman analysis was applied to analyze the relationship between TIR (or nocturnal TIR) and ACR. Logistic regression was used to explore whether TIR (or nocturnal TIR) is an independent risk factor for albuminuria. Results The prevalence of albuminuria decreased with increasing TIR quartiles. Binary logistic regression revealed that TIR as well as nocturnal TIR was obviously related to the presence of albuminuria. Multiple regression analysis found that only nocturnal TIR was obviously related to the severity of albuminuria. In our study, eGFR was significantly associated with the number of hypoglycemic events. Conclusions In T2DM patients, TIR and nocturnal TIR is associated with the presence of albuminuria independent of HbA1c and GV metrics. Nocturnal TIR shows better correlation than TIR. The role of TIR especially nocturnal TIR in the evaluation of diabetes kidney disease should be emphasized. Keywords Albuminuria Hypoglycemia Glomerular filtration rate Time in range Nocturnal time in range Continuous glucose monitoring Hemoglobin A1C Type 2 diabetes mellitus issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2023 ==== Body pmcIntroduction According to the Diabetes Control and Complications Trial (DCCT) and the UK Prospective Diabetes Study (UKPDS) [1, 2], hemoglobin A1C is the gold standard for assessing glycaemic control for decades and is strongly associated with the risk of long-term diabetic complications. However, as an indirect measure of average blood glucose levels over three months, HbA1c is considered to have some limitations [3]. HbA1c does not provide details about hypoglycaemia or hyperglycaemia and does not reflect glycemic variability [4]. HbA1c also has limitations in interpreting the risk of chronic complications of diabetes. For instance, HbA1c accounted for only 11% of the variation in risk of diabetic retinopathy observed in the Diabetes Control and Complications Trial (DCCT) [5]. Continuous glucose monitoring (CGM) provides an accurate reflection of an individual’s blood glucose status throughout the day. Compared to glycosylated haemoglobin, CGM technology can better reflect blood glucose variability. As a CGM-derived indicator, ‘time in range’ (TIR) is simple, intuitive and responsive to treatment and lifestyle changes, and has become a key indicator for assessing glycaemic control. It is negatively correlated with glycated haemoglobin and the American Diabetes Association 2021 guidelines [6] stated that it can be used to assess glycaemic control and might be an acceptable endpoint for future clinical trials. Several different studies [7] have reported that time range (TIR) is associated with the risk of microvascular complications and can predict the risk of future diabetic complications. Furthermore, according to the study [8], average blood glucose levels at night, but not daytime blood glucose values or glucose variability, were independently associated with the degree of vascular remodelling. This suggests that nighttime blood glucose may be a better indicator of the risk of diabetic complications than full-day blood glucose status. Circadian rhythms have received increasing attention in recent years with the award of the 2017 Nobel Prize to Young MW et al. [9] for their discoveries of molecular mechanisms controlling the circadian rhythm. Circadian rhythms also exist for various hypoglycemic hormones and baseline blood glucose levels, and studies have shown that baseline pre-meal glucose levels in animals and healthy humans show circadian rhythms under a regular light/dark cycle, with a trough during sleep and a peak during wakefulness [10–12]. Nevertheless, the clinical significance of circadian rhythms in blood glucose is not clear. Diabetic kidney disease (DKD) is a common chronic microvascular complication of diabetes that is now a major cause of CKD and end-stage renal disease, manifested mainly by a urinary albumin/creatinine ratio (UACR) ≥ 30 mg/g and/or an estimated glomerular filtration rate (eGFR) < 60 ml-min -¹-(1.73 m²)-¹ that persists for more than 3 months. In early screening, a random urine measurement of UACR is recommended to reflect urinary albumin excretion. TIR was found to be strongly associated with albuminuria in type 2 diabetes [13]. Furthermore, respective study found that each 10% treatment-induced increase in TIR was associated with 18% reduction in albuminuria in patients with T1D [14]. However, there is little report on the impact of nocturnal TIR and albuminuria in T2D. The aim of this work was to investigate whether TIR measured by CGM, especially nocturnal TIR and hypoglycaemic events is related to the presence and severity of albuminuria and decrease of eGFR in type 2 patients with diabetes. Research design and methods Participants A total of 823 patients (aged ≥ 18 years) with T2DM admitted to the Department of Endocrinology, Jinling Hospital, Nanjing University from April 2018 to July 2020 were recruited, all of whom met the 1999 WHO diagnostic criteria for type 2 diabetes mellitus. Exclusion criteria included (1) patients with acute complications of diabetes, acute stress such as trauma, surgery and severe infections, severe respiratory disease, malignant disease, pregnancy; (2) patients with definite hepatic disease; (3) patients with narcotic and psychotropic drugs, and a recent history of alcoholism; (4) using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula to calculate, patients with an estimated glomerular filtration rate (eGFR) of less than 30 ml/min/1.73m2. The study was supported by the local ethics committee. Clinical and biochemical measurements General clinical information and physical examination such as age, sex, duration of diabetes, hypertension, diabetic retinopathy and diabetic kidney disease were recorded. Height, weight, systolic blood pressure (SBP) were measured. Body mass index (BMI) was computed. Biochemical measurements such as blood and urine samples were tested after a 12-hour overnight fast. Hemoglobin A1C (HbA1c), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), blood urea nitrogen (BUN), and serum creatinine (SCr) were detected. Albumin creatinine ratio (ACR) was calculated from albumin and creatinine measured in urine samples, and based on ACR, patients was classified as normoalbuminuria (ACR < 30 mg/g), microalbuminuria (ACR 30–299 mg/g), and macroalbuminuria (ACR ≥ 300 mg/g). Estimate glomerular filtration rate (eGFR) was calculated by the CKD-EPI creatinine equation [15] using the clinical data of patients. CGM parameters Patients were asked to wear a continuous glucose monitoring system of Meiqi Company to monitor blood glucose levels every 5 min for 72 h continuously during the study. Capillary blood glucose at least four times was measured on each day of use of CGM to update the monitor. All 72 h of glucose data collected were calculated, TIR indicating the percentage of time that blood glucose was in the range of 3.9–10.0 mmol/L. Nocturnal TIR was defined as the percentage of time that blood glucose was in the range of 3.9–10.0 mmol/L between 00.00 am and 06:00 am (6 h). Patients were considered to be hypoglycaemic if they had at least one documented hypoglycaemic event during CGM monitoring. A blood glucose level < 3.9 mmol/L was recorded as one hypoglycaemic event. Nocturnal hypoglycaemic events are those that occur between 00.00 am and 06:00 am (6 h). Based on the original blood glucose data recorded by this system, a number of metrics concerning mean blood glucose (MBG) and glycemic variability (GV), including standard deviation (SD), average daily risk range (ADDR), mean amplitude of glucose excursions (MAGE), largest amplitude of plasma glucose excursions (LAGE), coefficient of variation (CV), and M-value were calculated using the EasyGV Version 9.0R2 provided by Oxford University. Statistical analysis The SPSS 22.0 software package was used for statistical analysis. Continuous data for normal distributions were expressed as mean ± SD, while data for abnormal distributions were expressed as median (upper and lower quartiles). Categorical data were expressed as numbers (percentages). Two normally distributed samples were compared using the Student’s t-test. One-way ANOVA was used for multi-sample comparisons and the KruskalWallis test was used for abnormal distributions. Categorical variables were tested using the χ2 test. The Spearman analysis was applied to analyze the relationship between TIR (or nocturnal TIR) and ACR. The binary logistic regression was applied to examine the independent connection between TIR (or nocturnal TIR) and ACR by adjusting age, sex, diabetes duration, BMI, lipid situation, SBP, SCr, HbA1c (%), and GV metrics. In addition, the multinomial logistic regression was applied to examine the independent connection between TIR (or nocturnal TIR) and different stages of ACR by adjusting age, P < 0.05 was considered statistically significant. Results Clinical characteristics among stages of ACR Groups The median (upper and lower quartiles) age of all 823 patients was 56.0[38.0,65.0] years, the median (upper and lower quartiles) duration of diabetes was 7[2, 12] years. There were 570 diabetic individuals without albuminuria (normoalbuminuria), 188 individuals with microalbuminuria and 65 diabetic individuals with macroalbuminuria. The prevalences of microalbuminuria and macroalbuminuria were 22.8% and 7.9%, respectively. With the aggravation of albuminuria, patients showed increased levels of SCr, SBP, MBG, ADDR, and M-value (P < 0.05), and lower levels of nocturnal TIR (P < 0.001). Median (upper and lower quartiles) nocturnal TIR was 97.50%(71.18%, 100.00%), 88.61%(56.88%, 100.00%), and 79.86%(47.85%, 99.23%) in normoalbuminuria (ACR < 30 mg/g), microalbuminuria (ACR 30–299 mg/g), and macroalbuminuria, respectively. TIR was significant different with the aggravation of albuminuria (P < 0.001). However, no significant difference between microalbuminuria and macroalbuminuria was evident(P = 0.98). In addition, there was no significant difference in HbA1c (%) among different groups. The comparison of clinical characteristics by quartiles (Q1-Q4) of TIR Further analysis after dividing patients into groups was done according to quartiles of TIR((Q1):≤ 42.92%; (Q2): 42.92–69.13%; (Q3): 69.13–85.45%; (Q4): >85.45%). The characteristics were shown in Table 1. Patients with the highest quartiles of TIR had lower TC, TG, HbA1c (%), ACR, SD, MAGE, MBG, ADDR, CV and M-value (P < 0.001). Notably, there was no significant difference in eGFR between the different TIR groups (P = 0.702). Table 1 The comparison of clinical characteristics by quartiles (Q1-Q4) of TIR. Quartiles (Q1-Q4) of TIR Q1 Q2 Q3 Q4 χ2/t/z P N 206 206 206 205 Age (y) 56.0(47.5,65.0) 57.0(48.0,66.0) 54.0(44.3,63.0) 56.0(48.0,64.0) 4.247 0.236 Diabetes duration (y) 8(3,15) 10(4,15) 6(2,12) 5(1,10) 31.323 < 0.001 Male (n, %) 66 63 70 72 3.834 0.28 SCr (µmol/L) 54.00(46.00,67.50) 56.00(46.00,67.00) 58.00(48.00,68.75) 59.00(48.00,70.00) 4.622 0.202 TC (mmol/L) 4.49(3.77,5.39) 4.46(3.72,5.35) 4.29(3.73,5.06) 4.27(3.53,4.88) 12.611 0.006 TG (mmol/L) 1.74(1.15,2.84) 1.59(1.59,2.56) 1.47(1.1,2.17) 1.37(1.02,2.12) 12.11 0.007 HDL (mmol/L) 0.97(0.86,1.19) 1.06(0.92,1.24) 1.06(0.9,1.24) 1.05(0.9,1.19) 8.794 0.032 LDL (mmol/L) 2.54(1.96,3.32) 2.66(2.07,3.37) 2.6(2.07,3.18) 2.51(1.89,3.12) 5.211 0.157 BMI 24.54(22.60,27.46) 25.34(22.0,27.24) 25.39(23.14,27.68) 25.39(22.60,27.78) 5.891 0.117 SBP (mmHg) 128(120,142) 128(120,140) 130(120,140) 129(120,138) 0.886 0.829 HbA1C (%) 9.9(8.9,11.7) 9(7.8,10.4) 7.9(7,9.2) 7(6.3,8.1) 256.012 < 0.001 eGFR, mL/min per 1.73 m2 107.74(98.50,121.56) 107.34(98.28,118.58) 111.59(100.4,123.42) 110.50(99.86,119.56) 1.417 0.702 ACR, mg/g 16.7(8.8,67.7) 13.7(7.5,59.0) 11.2(5.6,31.8) 10.4(5.7,31.3) 20.556 < 0.001 SD (mmol/L) 2.94(2.3,3.84) 2.79(2.29,3.38) 2.23(1.86,2.63) 1.49(1.27,1.8) 454.174 < 0.001 MAGE (mmol/L) 5.07(3.79,6.25) 5.12(4.06,6.21) 4.31(3.54,5.29) 3.38(2.89,4.04) 192.068 < 0.001 MBG (mmol/L) 11.96(11.25,13.04) 9.8(9.42,10.26) 8.46(8.17,8.86) 7.39(6.84,7.81) 793.17 < 0.001 CV 0.25(0.19,0.3) 0.28(0.24,0.35) 0.26(0.22,0.31) 0.2(0.17,0.25) 154.467 < 0.001 ADDR (mmol/L) 36.31(29.84,45.34) 36.31(29.84,45.34) 19.26(15.87,23.22) 11.71(8.75,14.46) 636.16 < 0.001 M-value (mmol/L) 24.72(16.64,34.93) 10.3(8.26,14.1) 4.71(3.77,5.95) 1.81(1.11,2.52) 784.969 < 0.001 TIR: time in range; Nocturnal TIR: nocturnal time in range; HbA1C: hemoglobin A1C; SD: standard deviation; MAGE: mean amplitude of glucose excursions; CV: coefficient of variation; ADDR: average daily risk range; eGFR: estimated glomerular filtration rate Prevalence of all stages of albuminuria in different quartiles (Q1-Q4) of TIR As shown in Fig. 1, individuals were classified into groups according to quartiles of the TIR, the proportion of “Normoalbuminuria” increased with the increase of TIR (P < 0.001). What else, the proportion of microalbuminuria and macroalbuminuria decreased with the increase of TIR (P < 0.05) (Fig. 2). Fig. 1 Prevalence of “Normalbuminuria” in different quartiles (Q1-Q4) of TIR. aTIR (Q1):≤ 42.92%; (Q2): 42.92–69.13%; (Q3): 69.13–85.45%; (Q4): >85.45%. bAs shown in this figure, patients were divided into groups according to quartiles of the time in range (TIR), the proportion of “Normoalbuminuria” increased with the increase of TIR (P < 0.001). P value for the significant Difference among the groups was determined by χ2-test Fig. 2 Prevalence of Albuminuria in different quartiles (Q1-Q4) of TIR. aTIR (Q1):≤ 42.92%; (Q2): 42.92–69.13%; (Q3): 69.13–85.45%; (Q4): >85.45%. bAs shown in this figure, patients were divided into groups according to quartiles of the time in range (TIR), the proportion of microalbuminuria and macroalbuminuria decreased with the increase of TIR (P < 0.05). P value for the significant difference among the groups was determined by χ2-test The correlation of TIR and nocturnal TIR with HbA1c, eGFR and ACR Spearman analysis was used to analyse the correlation between TIR and nocturnal TIR with HbA1c, eGFR and ACR and the results are shown in Table 2. Both TIR and nocturnal TIR were negatively correlated with HbA1c (P < 0.001, P < 0.001), ACR (P < 0.001, P < 0.001) and insignificantly correlated with eGFR (P = 0.367,P = 0.495). Notably, the correlation coefficient between nocturnal TIR and ACR was higher than that of TIR (R=-0.159 vs. R=-0.147). Table 2 The Correlation of TIR and nocturnal TIR with HbA1c, eGFR and ACR. HbA1c eGFR ACR TIR (3.9–10 mmol/L) R -0.544 0.029 -0.147 P < 0.001 0.367 < 0.001 Nocturnal TIR (3.9–10 mmol/L) R -0.425 0.022 -0.159 P < 0.001 0.495 < 0.001 TIR: time in range; Nocturnal TIR: nocturnal time in range; HbA1C: hemoglobin A1C; ACR: albumin/creatinine ratio; eGFR: estimated glomerular filtration rate Associations between TIR, nocturnal TIR and ACR Association of TIR and nocturnal TIR with the presence of albuminuria were investigated using binary logistic regression (Tables 3 and 4). After adjusting for age, diabetes duration, sex, BMI, lipid profile, blood pressure, and HbA1c (%) (model 1), the data revealed that TIR (odds ratio(OR): 0.992, 95% confidence interval (CI): 0.985–0.999, P = 0.026) as well as nocturnal TIR (OR: 0.990, 95% CI: 0.984–0.996, P = 0.002) was obviously related to the presence of albuminuria. After adjusting for SD, MAGE, CV and ADDR (model 2,3,4,5), the association persisted. However, after adjustment of M-value (model 6), the link between albuminuria and TIR was weakened (p = 0.197), the association between the presence of albuminuria and nocturnal TIR was still strong (p = 0.015). Table 3 Associations between between TIR and albuminuria Microalbuminuria Macroalbuminuria Any Albuminuria OR (95%CI) P OR (95%CI) P OR (95%CI) P Model 1 TIR 0.993(0.985-1.000) 0.062 0.988(0.976-1.000) 0.053 0.992(0.985–0.999) 0.026 Model 2 TIR 0.994(0.986–1.002) 0.126 0.99(0.977–1.002) 0.112 0.993(0.986–1.001) 0.082 SD 1.069(0.926–1.233) 0.363 1.113(0.941–1.316) 0.213 1.091(0.955–1.247) 0.2 Model 3 TIR 0.989(0.981–0.997) 0.008 0.987(0.974-1) 0.052 0.989(0.981–0.996) 0.004 MAGE 0.91(0.821–1.008) 0.072 0.948(0.816–1.101) 0.484 0.92(0.838–1.01) 0.079 Model 4 TIR 0.993(0.985-1) 0.065 0.987(0.975-1) 0.05 0.992(0.985–0.999) 0.026 CV 0.626(0.086–4.558) 0.644 13.67(1.45-128.892) 0.022 2.168(0.437–10.764) 0.344 Model 5 TIR 0.983(0.973–0.994) 0.003 0.988(0.973–1.003) 0.118 0.986(0.977–0.996) 0.006 ADDR 0.972(0.95–0.994) 0.014 1(0.975–1.025) 0.989 0.983(0.965–1.002) 0.078 Model 6 TIR 0.994(0.985–1.003) 0.216 0.992(0.979–1.005) 0.224 0.995(0.986–1.003) 0.197 M-value 1.005(0.992–1.017) 0.482 1.01(0.998–1.022) 0.105 1.007(0.997–1.018) 0.164 a TIR: time in range; HbA1C: hemoglobin A1C; SD: standard deviation; MAGE: mean amplitude of glucose excursions; CV: coefficient of variation; ADDR: average daily risk range; CI: confidence interval b Model 1 was adjusted for age, diabetes duration, sex, BMI, lipid profile, blood pressure, and HbA1c (%); model 2 was adjusted for variables as in model 1 and for SD; model 3 was adjusted for variables as in model 1 and for MAGE; model 4 was adjusted for variables as in model 1 and for CV; model 5 was adjusted for variables as in model 1 and for ADDR; model 6 was adjusted for variables as in model 1 and for M-value The multinomial Logistic regression found the weak relationship between TIR and severe stage of albuminuria (Microalbuminuria: P = 0.062. Macroalbuminuria: P = 0.053.) (Tables 4 and 6) (model 1). However, nocturnal TIR was obviously related to the severity of albuminuria (Microalbuminuria: OR: 0.992, 95% CI: 0.985–0.998, P = 0.015. Macroalbuminuria: OR: 0.984, 95% CI: 0.974–0.994, P = 0.002.) (model 1). Furthermore, the association persisted after adjusting for SD, MAGE, CV and ADDR (model 2,3,4,5), after adjustment of M-value (model 6), the link between the severe stage of albuminuria and nocturnal TIR was weakened (Microalbuminuria: p = 0.051; Macroalbuminuria: P = 0.012). Table 4 Associations between between nocturnal TIR and albuminuria Microalbuminuria Macroalbuminuria Any Albuminuria OR (95%CI) P OR (95%CI) P OR (95%CI) P Model 1 Nocturnal TIR 0.992(0.985–0.998) 0.015 0.984(0.974–0.994) 0.002 0.990(0.984–0.996) 0.002 Model 2 Nocturnal TIR 0.992(0.985–0.999) 0.027 0.992(0.985–0.999) 0.027 0.991(0.984–0.997) 0.002 SD 1.068(0.931–1.225) 0.348 1.068(0.931–1.225) 0.348 1.087(0.956–1.236) 0.002 Model 3 Nocturnal TIR 0.99(0.983–0.997) 0.005 0.983(0.972–0.993) 0.002 0.988(0.982–0.995) 0.001 MAGE 0.928(0.842–1.024) 0.137 0.954(0.827–1.101) 0.522 0.935(0.856–1.022) 0.139 Model 4 Nocturnal TIR 0.992(0.985–0.999) 0.018 0.984(0.973–0.994) 0.002 0.99(0.983–0.996) 0.001 CV 0.72(0.1-5.183) 0.744 13.837(1.455-131.599) 0.022 2.392(0.487–11.761) 0.283 Model 5 Nocturnal TIR 0.988(0.98–0.996) 0.003 0.984(0.972–0.996) 0.007 0.987(0.98–0.995) 0.001 ADDR 0.981(0.963–0.999) 0.04 0.998(0.975–1.021) 0.867 0.987(0.972–1.003) 0.109 Model 6 Nocturnal TIR 0.992(0.985-1) 0.051 0.986(0.975–0.997) 0.012 0.991(0.985–0.998) 0.015 M-value 1.003(0.991–1.014) 0.648 1.008(0.998–1.018) 0.128 1.005(0.997–1.014) 0.234 a TIR: time in range; Nocturnal TIR: nocturnal time in range; HbA1C: hemoglobin A1C; SD:standard deviation; MAGE: mean amplitude of glucose excursions; CV: coefficient of variation; ADDR: average daily risk range; CI: confidence interval; eGFR: estimated glomerular filtration rate b Model 1 was adjusted for age, diabetes duration, sex, BMI, lipid profile, blood pressure, and HbA1c (%); model 2 was adjusted for variables as in model 1 and for SD; model 3 was adjusted for variables as in model 1 and for MAGE; model 4 was adjusted for variables as in model 1 and for CV; model 5 was adjusted for variables as in model 1 and for ADDR; model 6 was adjusted for variables as in model 1 and for M-value Table 5 The Correlation of hypoglycaemic events and nocturnal hypoglycaemic events with HbA1c, eGFR and ACR. HbA1c eGFR ACR Hypoglycaemic events R -0.138 -0.131 -0.023 P < 0.001 < 0.001 0.476 Nocturnal hypoglycaemic events R -0.091 -0.096 -0.030 P 0.005 0.003 0.351 HbA1C: hemoglobin A1C; ACR: albumin/creatinine ratio; eGFR: estimated glomerular filtration rate Table 6 The correlation between eGFR and the number of hypoglycaemic events B 95%CI p number of hypoglycaemic events -0.090 (-2.769, -0.761) 0.001 age -0.497 (-1.103, -0.876) < 0.001 duration of diabetes -0.149 (-0.774, -0.347) < 0.001 blood pressure -0.073 (-0.188, -0.033) 0.005 The correlation of hypoglycaemic events and nocturnal hypoglycaemic events with HbA1c, eGFR and ACR The number of nocturnal hypoglycaemic events was used as a statistic for hypoglycaemia. Based on Spearman analysis, Table 5 presents the correlations between hypoglycaemicevents and nocturnal hypoglycaemic events with HbA1c, eGFR, and ACR. Hypoglycaemia and nocturnal hypoglycaemia were both negatively correlated with HbA1c (P < 0.001, P = 0.005), eGFR (P < 0.001, P = 0.003), and not significantly correlated with ACR (P = 0.476, P = 0.351). A multiple linear regression model was used to assess the correlation between eGFR and the number of hypoglycaemic events. The model showed that the number of hypoglycaemic, age, duration of diabetes and blood pressure were independent risk factors for eGFR. (B = -0.090, -0.497, -0.149, -0.073, all P < 0.05, Table 6). Discussion In our cohort of 823 T2D patients, the prevalence of albuminuria decreased with increasing TIR quartiles. Binary logistic regression revealed that TIR as well as nocturnal TIR was obviously related to the presence of albuminuria after adjusting for age, diabetes duration, sex, BMI, lipid profile, blood pressure, HbA1c (%), SD, MAGE, CV and ADDR. However, after adjustment of M-value, the link between albuminuria and TIR was weakened, the association between the presence of albuminuria nocturnal TIR was still significant. Multiple regression found the relationship between TIR and severity of albuminuria. However, nocturnal TIR was obviously related to the severe stage of albuminuria regardless of SD, MAGE, CV and ADDR. In our study, eGFR was not significantly associated with TIR, but was significantly associated with the number of hypoglycemic events and the number of nocturnal hypoglycemic events. As the gold standard for assessing glycaemic management, hemoglobin A1c (HbA1c) is considered to have a strong correlation with the microvascular complications of diabetes, including diabetic nephropathy [16, 17]. However, HbA1c does not reflect information on hyperglycaemia, hypoglycaemia and fluctuations in blood glucose, nor does it reflect the magnitude and frequency of intra- and inter-day glucose changes [18, 19]. In addition, the measurement of glycated haemoglobin can be affected by specific conditions including kidney insufficiency, anaemia, pregnancy, haemoglobinopathies and iron deficiency [20–23]. Due to the shortcomings of HbA1c, CGM-derived indicators, especially TIR has become an alternative marker of glycemic control over the last few years [24, 25]. There is insufficient evidence in previous studies to demonstrate the association of glycaemic variability with diabetic nephropathy in T2DM. The study by S.-M. Jin et al. did not find an independent association between glycaemic variability and the degree of proteinuria in patients with type 2 diabetes [26]. Subramanian S et al. suggest that glycaemic variability may be a factor in the development of DKD, but clear evidence is lacking [27]. Wakasugi S et al. demonstrated that FLP-CGMderived metrics related to intraday and interday glucose variability, including TIR, SD, MAGE and MODD, were significantly associated with albuminuria severity, and these associations remained significant after adjustment for HbA1c [28]. Our study also found that TIR was obviously related to the presence of albuminuria after adjusting for glycaemic variability Indicators including SD, MAGE, CV and ADDR. However, in contrast to their results, multiple regression analysis found a weaker relationship between TIR and the stage of albuminuria severity. A series of studies have demonstrated a correlation between CGM indicators, represented by TIR, and the development or progression of albuminuria in T2DM. Yoo JH et al. demonstrated that CGM-derived TIR significantly associated with the risk of albuminuria, even after adjusting for various confounding factors including CV. With a 10% increase in TIR, the risk of albuminuria was reduced by 6% [9]. However, after further adjustment for HbA1c, the study did not show a significant association between TIR and albuminuria. In contrast to their results, we found that TIR as well as nocturnal TIR was obviously related to the presence of albuminuria even after adjustment for HbA1c. In the study by Varghese JS, participants were divided into three groups based on glycemic profile (‘TIR profile’, ‘hyper’ and ‘hypo’). Compared with ‘TIR profile’, both ‘hyper’ and ‘hypo’ profiles had higher odds of macroalbuminuria and higher odds of diabetic kidney disease [29]. TIR was demonstrated to be associated with UACR, DPN and T2DM duration in the multicentre, prospective cohort study by Kuroda N et al. [30]. Consistent with those fndings, we found that the prevalence of albuminuria decreased as the TIR quartiles increased. The independently negative association between TIR and the presence of albuminuria exists even after adjusting for several risk factors including HbA1c. There is little previous research on the relationship between nocturnal blood glucose and diabetic complications. A study of T1DM showed that reduced nocturnal TIR was more closely associated with function of sudomotor nerves function of sudomotor nerves [31]. In our study, nocturnal TIR showed a better correlation with albuminuria compared to TIR. Binary logistic regression analysis showed that TIR as well as nocturnal TIR were significantly correlated with the presence of albuminuria, after adjustment for a range of indicators. However, after further correction for M-value, only nocturnal TIR remained significant. Furthermore, multiple regression analysis showed that TIR did not correlate significantly with the severity of albuminuria, while nocturnal TIR correlated significantly, even after adjustment for a number of indicators including HbA1c. One possible explanation is attributed to the effect of growth hormone secreted at night on the kidneys. Growth hormone (GH) is widely used to treat short stature in children, including children with chronic kidney disease (CKD). GH-excess can affect kidney health by causing glomerular hyperfiltration, hypertrophy, and glomerulosclerosis [32]. GH-excess is also an important promoter of diabetes nephropathy in T1DM patients. Studies in patients with T1DM showed that urinary GH and IGF-1 levels were related to microalbuminuria in patients [33]. In addition, GH also has a renal protective effect on humans. Studies have shown that GH therapy can protect cisplatin-induced nephropathy in rats [34]. To sum up, GH secreted at night has an impact on blood glucose and kidneys, and the nocturnal TIR can better include its impact on blood glucose, which may explain the better correlation between nocturnal TIR and albuminuria. In addition, cortisol as a circadian hormone is not easy to ignore. a study by Roy et al. [35] found a tendency for elevated cortisol secretion in patients with diabetic retinopathy or diabetic cardiovascular complications. Chiodini I et al. [36] showed that the degree of midnight cortisol secretion was directly related to the presence and number of complications of type 2 diabetes, including DKD. In contrast, other parameters of cortisol secretion were not significantly correlated with the presence or number of diabetic complications. We can therefore speculate that nocturnal TIR could incorporate the effect of nocturnal cortisol levels on blood glucose and therefore correlate better with diabetic nephropathy. In addition, diabetes significantly affects melatonin secretion levels at night. Hikichi et al. [37] compared melatonin secretion at night and during the day in non-diabetic and diabetic subjects and found that melatonin levels were lower at night in patients with diabetes, but not during the day as affected by diabetes. The study by Baris Afsar et al. [38] suggests that melatonin activates cardiovascular system and renal receptors to protect DN in preclinical models. Considering that elevated blood glucose at night may inhibit melatonin secretion, thus depriving the kidneys of the protective effect of melatonin, the association between nocturnal TIR and proteinuria is more significant compared to full-day TIR. Unlike albuminuria and TIR, our study found no significant correlation between eGFR and TIR. In recent years, the role of the renal tubules has received increasing attention in studies on the pathogenesis of DKD. As a traditional indicator of kidney disease, the increase of microalbuminuria or creatinine based glomerular filtration rate (eGFR) may occur long after the decline of renal function [39]. Some biomarkers of proximal renal tubular injury, such as kidney injury molecule 1 (KIM-1), N-acetyl-b-D-glucosaminidase (NAG), and liver fat acid binding protein (L-FABP), appear abnormal before proteinuria, and may become new biomarkers for DKD prediction [40]. At present, it is believed that the mechanism of albuminuria is the excessive inflammatory reaction of proximate tubular epithelial cells (PTECs) under the condition of diabetes [41, 42]. Considering the central role of renal tubules rather than glomeruli in the occurrence and development of early stage DKD, it can be explained why TIR, as a blood glucose control indicator, has a good correlation with albuminuria but not with eGFR. A notable result was that hypoglycaemic and nocturnal hypoglycaemic events were negatively correlated with eGFR. According to the study by Khanimov I [43], eGFR was strongly associated with an increased incidence of hypoglycaemia during hospitalization in non-critically ill patients. However, this study also showed that renal function was a strong predictor of hypoglycaemia regardless of the presence of diabetes, implying that hypoglycaemic events may not be specific for the prediction of diabetic nephropathy. Our study had several limitations that should be noted. Firstly, this is a retrospective, single-centre study and it can only describe the correlation, not the causal relationship. Secondly, all subjects received CGM for 72 h, which may not be representative of overall glucose status. Some glucose-raising hormones that may play a role in the development of diabetic complications such as cortisol, growth hormone and melatonin were not measured. In conclusion, our study revealed that TIR and nocturnal TIR is associated with the presence and severity of albuminuria independent of HbA1c and GV metrics. Nocturnal TIR shows better correlation than TIR. Hypoglycemia and nocturnal hypoglycemia events were negatively correlated with eGFR, while TIR was not correlated with eGFR. The role of TIR and nocturnal TIR in the evaluation of diabetes kidney disease should be emphasized. Authors’ contributions Ping Gu conceived and designed the research. Xuguang Jin, Xinyi Yang and Yixin Xu collected the data. Xuguang Jin analyzed and interpreted the data. Xuguang Jin performed the statistical analysis. Xuguang Jin wrote the manuscript. Ping Gu, Jiaqing Shao critically revised the manuscript and contributed to the discussion. Xuguang Jin is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate The study was approved by the ethics committee of Jinling Hospital affiliated with Nanjing University. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors’ information Not applicable. Abbreviations TIR time in range T2DM type 2 diabetes mellitus eGFR estimated glomerular filtration rate GV glycemic variability CV cardiovascular HbA1C hemoglobin A1C CGM continuous glucose monitoring SBP systolic blood pressure DBP diastolic blood pressure BMI body mass index TC total cholesterol TG triglyceride HDL high-density lipoprotein LDL low-density lipoprotein Scr serum creatinine ACR urinary albumin/creatinine ratio DKD diabetic kidney disease DR diabetic retinopathy MBG mean blood glucose. SD standard deviation. MAGE mean glucose fluctuation amplitude CV coefficient of variation MODD mean absolute difference of daytime glucose ADDR mean daily risk range TBR time below range TAR time above range OR odds ratio CI confidence interval T1DM type 1 diabetes mellitus Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Xuguang Jin, Xinyi Yang, Yixin Xu contributed equally to this work. ==== Refs References 1. UK Prospective Diabetes Study (UKPDS) Group Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) The Lancet 1998 352 9131 837 53 10.1016/S0140-6736(98)07019-6 2. The DCCTR Group “The Diabetes Control and Complications Trial (DCCT) Design and methodologic considerations for the feasibility phase. The DCCT Research Group Diabetes 1986 35 5 530 45 10.2337/diab.35.5.530 2869996 3. Beck RW Connor CG Mullen DM Wesley DM Bergenstal RM The fallacy of average: how using HbA1c alone to assess Glycemic Control can be misleading Diabetes Care Aug 2017 40 8 994 9 10.2337/dc17-0636 4. Qu Y Jacober SJ Zhang Q Wolka LL DeVries JH Rate of hypoglycemia in insulintreated patients with type 2 diabetes can be predicted from glycemic variability data Diabetes Technol Ther Nov 2012 14 11 1008 12 10.1089/dia.2012.0099 5. Lachin JM Genuth S Nathan DM Zinman B Rutledge BN DCCT/EDIC Research Group Effect of glycemic exposure on the risk of microvascular complications in the diabetes control and complications trial–revisited Diabetes 2008 57 4 995 1001 10.2337/db07-1618 18223010 6. American Diabetes Association 6. Glycemic targets: Standards of Medical Care in Diabetes-2021 Diabetes Care 2021 44 Suppl 1 73 S84 10.2337/dc21-S006 7. Bellido V Pinés-Corrales PJ Villar-Taibo R Ampudia-Blasco FJ Time-in-range for monitoring glucose control: is it time for a change? Diabetes Res Clin Pract 2021 177 108917 10.1016/j.diabres.2021.108917 34126129 8. Yano Y Hayakawa M Kuroki K Nighttime blood pressure, nighttime glucose values, and target-organ damages in treated type 2 diabetes patients Atherosclerosis 2013 227 1 135 9 10.1016/j.atherosclerosis.2012.12.006 23332181 9. Young MW Time Travels A 40-Year journey from Drosophila’s clock mutants to Human Circadian Disorders (Nobel lecture) Angew Chem Int Ed Engl 2018 57 36 11532 9 10.1002/anie.201803337 30003624 10. La Fleur SE Kalsbeek A Wortel J Buijs RM A suprachiasmatic nucleus generated rhythm in basal glucose concentrations J Neuroendocrinol 1999 11 8 643 52 10.1046/j.1365-2826.1999.00373.x 10447803 11. Challet E Malan A Turek FW Van Reeth O Daily variations of blood glucose, acid-base state and PCO2 in rats: effect of light exposure Neurosci Lett 2004 355 1–2 131 5 10.1016/j.neulet.2003.10.041 14729252 12. Cailotto C La Fleur SE Van Heijningen C The suprachiasmatic nucleus controls the daily variation of plasma glucose via the autonomic output to the liver: are the clock genes involved? Eur J Neurosci 2005 22 10 2531 40 10.1111/j.1460-9568.2005.04439.x 16307595 13. Yoo JH Choi MS Ahn J Association between continuous glucose monitoring-derived time in Range, other Core Metrics, and Albuminuria in Type 2 diabetes Diabetes Technol Ther 2020 22 10 768 76 10.1089/dia.2019.0499 32167394 14. Ranjan AG Rosenlund SV Hansen TW Rossing P Andersen S Nørgaard K Improved Time in Range over 1 year is Associated with reduced Albuminuria in individuals with sensor-augmented insulin pump-treated type 1 diabetes Diabetes Care 2020 43 11 2882 5 10.2337/dc20-0909 32887707 15. Levey AS Stevens LA Schmid CH A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011 Sep 20;155(6):408] Ann Intern Med 2009 150 9 604 12 10.7326/0003-4819-150-9-200905050-00006 19414839 16. Effect of intensive therapy on the development and progression of diabetic nephropathy in the Diabetes Control and Complications Trial The Diabetes Control and Complications (DCCT) Research Group Kidney Int 1995 47 6 1703 20 10.1038/ki.1995.236 7643540 17. Stratton IM Adler AI Neil HA Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study BMJ 2000 321 7258 405 12 10.1136/bmj.321.7258.405 10938048 18. Cox DJ Kovatchev BP Julian DM Frequency of severe hypoglycemia in insulinde-pendent diabetes mellitus can be predicted from self-monitoring blood glucose data J Clin Endocrinol Metab 1994 79 1659 62 7989471 19. Qu Y Jacober SJ Zhang Q Wolka LL DeVries JH Rate of hypoglycemia in insulin-treated patients with type 2 diabetes can be predicted fromglycemic variability data Diabetes Technol Ther 2012 14 1008 12 10.1089/dia.2012.0099 23101951 20. National Institute of Diabetes and Digestive and Kidney Diseases Health Information Center. Sickle cell trait & other hemoglobinopathies & diabetes (for providers) [Internet]. Available from https://www.niddk.nih.gov/health-information/diagnostic-tests/sickle-cell-trait-hemoglobinopathiesdiabetes. Accessed 12 January 2018. 21. Bry L Chen PC Sacks DB Effects of hemoglobin variants and chemically modified derivatives on assays for glycohemoglobin Clin Chem 2001 47 153 63 10.1093/clinchem/47.2.153 11159762 22. Ford ES Cowie CC Li C Handelsman Y Bloomgarden ZT Iron-deficiency anemia, noniron-deficiency anemia and HbA1c among adults in the US J Diabetes 2011 3 67 73 10.1111/j.1753-0407.2010.00100.x 20942846 23. Nielsen LR Ekbom P Damm P HbA1c levels are significantly lower in early and late pregnancy Diabetes Care 2004 27 1200 1 10.2337/diacare.27.5.1200 15111545 24. Battelino T Danne T Bergenstal RM Clinical targets for continuous glucose Monitoring Data Interpretation: recommendations from the International Consensus on Time in Range Diabetes Care 2019 42 8 1593 603 10.2337/dci19-0028 31177185 25. Bellido V, Aguilera E, Cardona-Hernandez R, et al. Expert Recommendations for using Time-in-range and other continuous glucose monitoring Metrics to achieve patient-centered Glycemic Control in people with diabetes [published online ahead of print, 2022 Apr 26]. J Diabetes Sci Technol. 2022;19322968221088601. 10.1177/19322968221088601. 26. Jin SM Kim TH Oh S Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring Diabet Med 2015 32 2 274 9 10.1111/dme.12607 25307609 27. Subramanian S Hirsch IB Diabetic kidney disease: is there a role for glycemic variability? Curr Diab Rep 2018 18 3 13 10.1007/s11892-018-0979-3 29450720 28. Wakasugi S Mita T Katakami N Associations between continuous glucose monitoring-derived metrics and diabetic retinopathy and albuminuria in patients with type 2 diabetes BMJ Open Diabetes Res Care 2021 9 1 e001923 10.1136/bmjdrc-2020-001923 33879513 29. Varghese JS Ho JC Anjana RM Profiles of intraday glucose in type 2 diabetes and their association with complications: an analysis of continuous glucose Monitoring Data Diabetes Technol Ther 2021 23 8 555 64 10.1089/dia.2020.0672 33720761 30. Kuroda N Kusunoki Y Osugi K Relationships between time in range, glycemic variability including hypoglycemia and types of diabetes therapy in japanese patients with type 2 diabetes mellitus: Hyogo Diabetes Hypoglycemia Cognition Complications study J Diabetes Investig 2021 12 2 244 53 10.1111/jdi.13336 32594655 31. Feng ZQ Guo QY Wang W Time in range, especially overnight time in range, is associated with sudomotor dysfunction in patients with type 1 diabetes DiabetolMetabSyndr 2021 13 1 119 10.1186/s13098-021-00739-z 32. Haffner D Grund A Leifheit-Nestler M Renal effects of growth hormone in health and in kidney disease Pediatr Nephrol 2021 36 8 2511 30 10.1007/s00467-021-05097-6 34143299 33. Verrotti A Cieri F Petitti MT Morgese G Chiarelli F Growth hormone and IGF-I in diabetic children with and without microalbuminuria Diabetes NutrMetab 1999 12 4 271 6 34. Mahran YF New insights into the protection of growth hormone in cisplatin-induced nephrotoxicity: the impact of IGF-1 on the Keap1-Nrf2/HO-1 signaling Life Sci 2020 253 117581 10.1016/j.lfs.2020.117581 32209424 35. Roy MS Roy A Brown S Increased urinary-free cortisol outputs in patients with diabetes J Diabetes Complications 1998 12 1 24 7 10.1016/s1056-8727(97)00006-8 9442811 36. Chiodini I Adda G Scillitani A Cortisol secretion in patients with type 2 diabetes: relationship with chronic complications Diabetes Care 2007 30 1 83 8 10.2337/dc06-1267 17192338 37. Hikichi T Tateda N Miura T Alteration of melatonin secretion in patients with type 2 diabetes and proliferative diabetic retinopathy Clin Ophthalmol 2011 5 655 60 10.2147/OPTH.S19559 21629571 38. Afsar B ElsurerAfsar R Sag AA Sweet dreams: therapeutic insights, targeting imaging and physiologic evidence linking sleep, melatonin and diabetic nephropathy Clin Kidney J 2020 13 4 522 30 10.1093/ckj/sfz198 32905249 39. Krolewski AS Progressive renal decline: the new paradigm of diabetic nephropathy in type 1 diabetes Diabetes Care 2015 38 6 954 62 10.2337/dc15-0184 25998286 40. Gilbert RE Proximal Tubulopathy: Prime Mover and Key Therapeutic Target in Diabetic kidney disease Diabetes 2017 66 4 791 800 10.2337/db16-0796 28325740 41. Hasegawa K Wakino S Simic P Renal tubular Sirt1 attenuates diabetic albuminuria by epigenetically suppressing Claudin-1 overexpression in podocytes Nat Med 2013 19 11 1496 504 10.1038/nm.3363 24141423 42. Li S Jia Y Xue M Inhibiting Rab27a in renal tubular epithelial cells attenuates the inflammation of diabetic kidney disease through the miR-26a-5p/CHAC1/NF-kB pathway Life Sci 2020 261 118347 10.1016/j.lfs.2020.118347 32853650 43. Khanimov I Zingerman B Korzetz A Association between estimated GFR and incident hypoglycaemia during hospitalization Nephrol (Carlton) 2022 27 2 162 70 10.1111/nep.13984