Improved Time in Range Over 1 Year Is Associated With Reduced Albuminuria in Individuals With Sensor-Augmented Insulin Pump–Treated Type 1 Diabetes

Glycated hemoglobin A_1c (HbA_1c) is the gold standard for assessment of glucose control in people with type 1 diabetes (T1D) (1). HbA_1c has in several studies been shown to be a strong predictor for chronic diabetes complications but has limitations in predicting hypoglycemia risk and describing the day-to-day glucose excursions (2). Furthermore, the relationship between HbA_1c and mean glucose concentration varies among individuals (3).

Recently, the use of continuous glucose monitoring (CGM) has provided new measures for assessing glucose control beyond HbA_1c and even in real time (4). Consensus guidelines recommend keeping CGM values >70% of time in range (TIR) of 3.9–10 mmol/L because they correspond to an HbA_1c level <53 mmol/mol (7%) (5). However, the evidence for the relationship between CGM-derived TIR and chronic diabetes complications is limited.

The aim of this study was to compare the changes in albuminuria over a year with the changes in TIR in people with T1D.

Study design, participants, and procedures are described in detail elsewhere (6). In brief, a randomized, controlled, open-labeled parallel trial was conducted on 60 participants with T1D, with a history of albuminuria, and on stable renin-angiotensin-aldosterone system (RAAS) inhibition. They were randomly assigned to sensor-augmented insulin pump (SAP) or multiple daily injection (MDI) and followed for 1 year with in-clinic visits after 0, 3, 6, 9, and 12 months. The study was conducted between February 2012 and December 2014, approved by the regional ethics committee (H-3-2011-122), and registered at ClinicalTrials.gov (NCT01454700).

Adults with T1D, aged 18–75 years, HbA_1c ≥58 mmol/mol (≥7.5%), estimated glomerular filtration rate ≥45 mL/min/1.73 m², treated with RAAS inhibition, and a urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g at screening were included. A total of 273 individuals were invited to participate, 84 were screened, 63 were enrolled, and 55 completed the study, with 26 assigned to SAP and 29 to MDI.

All participants were monitored with a blinded CGM (iPro2; Medtronic) for 6 days at baseline and for the MDI group also at the study end. All were encouraged to measure four blood glucoses daily. All were trained in carbohydrate counting and diabetes self-care.

Participants assigned to SAP wore an insulin pump (MiniMed Paradigm; Medtronic) and a real-time CGM (Enlite; Medtronic). The insulin pump was set to suspend insulin at a sensor glucose level of 3.0 mmol/L.

At every visit, BMI and mean arterial pressure (MAP) were calculated, and blood and urine samples were collected. For the SAP group, the CGM and insulin pump data were also uploaded (CareLink Pro; Medtronic). No adjustments for CGM malfunctions, errors, or data loss were performed.

In this subanalysis, only the CGM data for the SAP group were included for analysis. Consensus guidelines for reporting CGM data were followed (5,7). Percentage of TIR (%TIR 3.9–10.0 mmol/L), time above range level 1 (%TAR₁ >10.0 mmol/L), time above range level 2 (%TAR₂ >13.9 mmol/L), time below range level 1 (%TBR₁ <3.9 mmol/L), time below range level 2 (%TBR₂ <3.0 mmol/L), and the glucose management indicator (GMI) (8) were calculated. Glycemic variability was expressed as coefficient of variation (%CV).

A linear mixed model with a participant-specific random intercept was used to analyze the effect of changes in %TIR, %CV, and GMI on the changes in UACR, adjusting for changes in MAP, HbA_1c, and BMI. UACR was skewedly distributed and logarithmically transformed before analysis. Normally distributed data were presented as mean (95% CI). Nonnormally distributed data were presented as median and interquartile range. All statistical analyses were performed with SAS 9.4 M5 (SAS Institute Inc.).

Twenty-six participants were assigned to the SAP group. They included 25% women and had a median (interquartile range) age of 51 (46–63) years, diabetes duration of 32 (23–43) years, BMI of 27.5 (23.5–31.1) kg/m², HbA_1c of 75 (68–88) mmol/mol (9.0% [8.4–10.4%]), and UACR of 89 (37–250) mg/g. All participants were on RAAS inhibition treatment at baseline.

HbA_1c and UACR improved significantly from baseline to study end. There were no changes in MAP, BMI, or in the treatment with RAAS or any other antihypertensive drugs (6).

Participants used the CGM, on average, for 68% (61–78%) of the total follow-up time (Table 1). There was a significant improvement in CGM-derived TIR, average glucose level, glycemic variability, and GMI from baseline to study end (all P < 0.01) (Table 1).

The changes in UACR from baseline to study end were inversely correlated with the changes in %TIR (R = −0.03; P = 0.04) (Supplementary Fig. 1). However, no significant difference was observed in UACR changes when stratifying the %TIR changes into quartiles (P = 0.5) (Supplementary Table 1).

The UACR decreased, with 19% (10–28%) per 10% increase in %TIR (P = 0.038) and with 22% (11–33%) per 10-mmHg decrease in MAP (P < 0.001) (Supple‐mentary Fig. 2). In the adjusted model only, changes in UACR were also significantly related to the changes in average glucose level and GMI (both P = 0.03). No relationships were seen in the adjusted model between the changes in UACR and HbA_1c (P = 0.071), %CV (P = 0.59), and BMI (P = 0.83) (Supplementary Table 2).

In this 1-year longitudinal study, treatment-induced increase in CGM-derived TIR was significantly associated with reduced albuminuria in individuals with SAP-treated T1D and albuminuria. This was independent of changes in blood pressure, HbA_1c, and BMI. Furthermore, the glycemic variability did not have any significant impact on albumin excretion. This is the first study to demonstrate a prospective association of changes in CGM-derived %TIR and GMI with improvements in albuminuria.

Former studies have confirmed the relationship between HbA_1c and %TIR, but less evidence exists on the link between %TIR and chronic diabetes complications. The available studies are limited to individuals with type 2 diabetes and retinopathy (9), observation of seven-point finger prick measurements (10), and/or retrospective data with 3 days’ consecutive CGM data (11), in contrast to the recommendation that CGM data from at least 10–14 days are needed (5). The strength of this study is that CGM data were collected for 1 year and were analyzed every 3 months in relation to corresponding levels of HbA_1c, UACR, and MAP.

In our original published study (6), changes in albuminuria were only significantly lower for the SAP group compared with the MDI group when accounting for changes in HbA_1c. This extended analysis shows that the reduction in albuminuria is better explained by the changes in %TIR and GMI. In contrast, the improvements in glycemic variability for the SAP group could not explain the changes in albuminuria. Thus, we can speculate that albuminuria may be determined by the overall glucose load rather than by the glucose fluctuations. However, more studies are needed to confirm these findings.

The study has limitations. First, the MDI group was not included in the main analysis because they only wore blinded CGMs for 6 days at baseline and at study end. According to consensus guidelines for CGM use recommending >70% of possible CGM readings over a 2-week period, the MDI group had insufficient CGM data to assess the glucose control (7,12). However, when including the MDI group in the analysis, the relationships between %TIR and UACR almost reached statistical significance (P = 0.052) (Supple‐mentary Table 3). Lastly, the study was neither designed nor powered to demonstrate whether %TIR or any other CGM-derived metrics were better correlated to changes in albuminuria than HbA_1c alone.

Nonetheless, we conclude that increased %TIR is significantly associated with improved excretion of albumin in the urine in individuals with T1D and that %TIR complements HbA_1c with real-time evaluations of the glucose control in relation to effect on albuminuria.

Acknowledgments. The authors thank the laboratory at Steno Diabetes Center Copenhagen (Gentofte, Denmark) for excellent technical assistance and study nurse Anette Hougaard (Steno Diabetes Center Copenhagen) for the invaluable help.

Funding. A.G.R. received a postdoctoral fellowship from the Danish Diabetes Academy (PD002-19) supported by the Novo Nordisk Foundation and received a research grant from the Danish Diabetes Association.

Duality of Interest. This work was supported by an unrestricted grant from Medtronic A/S, Denmark. S.V.R. has been employed at Novo Nordisk after study termination. P.R. has received honoraria for consultancy to Steno Diabetes Center Copenhagen from Novo Nordisk, Bayer, Boehringer Ingelheim, Gilead Sciences, Inc., Vifor Pharma, Astellas Pharma, AstraZeneca, and Sanofi and has received research grants from Novo Nordisk and AstraZeneca. S.A. has received an unrestricted research grant from Medtronic A/S, Denmark. K.N. has served as an adviser for Medtronic, Abbott Laboratories, and Novo Nordisk; owns stocks in Novo Nordisk; has received research grants from Novo Nordisk, Zealand Pharma, and Roche; and has received fees for speaking from Medtronic, Roche, Rubin Medical, Sanofi, Zealand Pharma, and Novo Nordisk.

Medtronic A/S had no role in the study design, data collection, data analysis, interpretation, or writing of the manuscript.

Author Contributions. A.G.R. wrote the manuscript. S.V.R., T.W.H., P.R., S.A., and K.N. reviewed and edited the manuscript. A.G.R. and S.V.R. are the guarantors of this work and, as such, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented at the 80th Scientific Sessions of the American Diabetes Association, 12–16 June 2020.