Smart insulin dosing software based on trend arrows is an effective means of controlling hyperglycemia in non-endocrinological inpatients with diabetes: a randomized controlled trial

Smart insulin dosing software based on trend arrows is an effective means of controlling hyperglycemia in non-endocrinological inpatients with diabetes: a randomized controlled trial | 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 Research Article Smart insulin dosing software based on trend arrows is an effective means of controlling hyperglycemia in non-endocrinological inpatients with diabetes: a randomized controlled trial Lihua Zhou, Chenwei Wu, Chunhong Wang, Xia Yu, Duoduo Qu, Yaling Yang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6878924/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2026 Read the published version in Endocrine → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Artificial intelligence and real-time continuous glucose monitoring (rtCGM) are important inventions in the history of diabetes. The aim of this study was to compare the effectiveness of two methods of glycemic control (smart insulin dosing software and traditional physician experience). Methods Subjects were randomized 1:1 into a smart insulin group (App group) and a traditional physician group (Control group). The former calculates the insulin dose based on the application (with trend arrows that respond to the magnitude and direction of glucose changes), while the latter calculates the insulin dose based on the physician's experience. All subjects underwent rtCGM and capillary blood glucose (CBG) testing during the study. The duration of the study was 5 to 7 days. We compared the mean blood glucose during the intervention. Results Baseline information was matched between the two groups. Patients in the App group had lower mean CBG (mmol/L) before dinner ( P = 0.006) and at bedtime ( P = 0.039) compared to the control group. The mean results of rtCGM during the study showed that patients in the App group had lower mean glucose ( P = 0.003), glucose management index ( P = 0.003), time in range (64.0% (51.0%, 73.0%) vs 47.0% (29.5%, 67.0%), P = 0.001) and time above of range (35.0% (23.0%, 47.0%) vs 52.0% (29.5.0%, 70.0%), P = 0.003). There was no difference in the incidence of hypoglycemia between the two groups of patients ( P = 0.394). Conclusion Smart insulin dosing software with trend arrows has a better effect on controlling hyperglycemia than traditional physician experience, and does not increase the risk of hypoglycemia. insulin dose adjustment trend arrows continuous glucose monitoring artificial intelligence time in range Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The latest data show that the number of diabetes patients worldwide has exceeded 500 million 1 , with a prevalence of more than 10% in the Chinese population 2 . Insulin is an important tool for controlling hyperglycemia, and the method of adjusting insulin dosage is indeed purely empirical, which is particularly difficult in non-endocrinology departments. Studies have shown that non-endocrinologists generally lack knowledge of insulin type, dosage, and glycemic management, and there are significant barriers to in-hospital glycemic management 3 . Disease stress, therapeutic agents, and disease severity are important causes of hyperglycemia during hospitalization 4 . Hyperglycemia is an important factor affecting the length of hospital stay and total cost of hospitalization in diabetic patients. Artificial Intelligence (AI) is a great boon for diabetic patients and plays an important role in insulin dosage adjustment. The Insulin Dosing System is a software package that incorporates patient-specific dose-response data into a data model and then calculates the dose of new medication needed to reach the next desired goal. In 2003, Gross first suggested that smart insulin dose calculators could play an important role in controlling postprandial blood glucose in patients with type 1 diabetes mellitus(T1DM) 5 . Since then, smart dosing systems have begun to spread among diabetic patients and have shown some advantages over clinicians adjusting glucose empirically. Results of a randomized controlled trial suggest that smart software to calculate insulin doses improves the quality of glycemic control and does not increase hypoglycemia compared to nurse-adjusted insulin doses 6 . Meanwhile, researchers in Germany have found that hemoglobin A1c (HbA1c) can be better controlled through the use of a smartphone glucose management app 7 . Thus, we proposed to explore the glucose lowering effect of smart insulin dosing system in non-endocrinology hospitalized diabetic patients. Therefore, smart insulin dosing systems offer many benefits to diabetic patients. We propose to investigate the glucose-lowering effect of smart insulin dosing system on non-endocrinology hospitalized diabetic patients. Continuous glucose monitoring (CGM) technology is an important and effective tool for blood glucose management. Factors such as accuracy and convenience have led to the widespread use of CGM for diabetic patients and its great clinical benefits 8,9 . Given the unique benefits of AI and CGM, several researchers have combined the two for glycemic management 10 , Breton et al. found that CGM combined with an intelligent insulin dose titration system reduced glycemic fluctuations 11 , both of which were conducted in patients with T1DM. CGM data is a continuous nontemporal series that can provide rich information about glucose, which promotes the use of data mining to further characterize glucose dynamics. Time in range (TIR) is a new metric for assessing glycemic management. Several observational studies have shown that TIR is strongly associated with pathophysiologic characteristics of diabetes, indicators of glycemic variability, and risk of complications and mortality 12–14 . The Glycemic management index (GMI) is a new measure of short-term glycemic control proposed in 2018 15 , which is also recommended by current guidelines 16 . Similarly, Glycemic Risk Index (GRI) is an important parameter of CGM and plays an important role in the management of microvascular complications in diabetes 17 . The trend arrow is an important feature of the CGM that shows the magnitude and direction of blood glucose changes. Users can utilize the trend arrows to predict future blood glucose levels so that they can take the next step in treatment, which can help alleviate impending hypoglycemia or hyperglycemia. Most studies on smart insulin dosing software have not included trend arrow information from CGM. Therefore, this study proposes to make full use of trend arrow information to combine smart insulin dosing software and CGM as a method of glycemic control. The glycemic control effect of this method was compared with the traditional physician's empirical adjustment of blood glucose in non-endocrinology hospitalized diabetic patients. Materials and methods Patients A total of 92 diabetic patients were included in this study, who were hospitalized (non-endocrine department) at Shanghai Public Health Clinical Center between October 2023 and September 2024, and all of them and used multiple daily insulin injections to control hyperglycemia. The study was approved by the Ethical Research Committee of the Shanghai Public Health Clinical Center and the clinical trial registration number was NCT05389839. All subjects signed a written informed consent. Inclusion criteria ①18–75 years old; ②insulin for glycemic control, and no oral hypoglycemic drugs; ③type 1 or type 2 diabetes mellitus; ④non-endocrine inpatient; ⑤three meals per day. Exclusion criteria ①those who had a myocardial infarction or cardiovascular event in the past three months; ②patients with renal insufficiency (estimated glomerular filtration rate < 45 ml/min/1.73m 2 ); ③patients with hepatic insufficiency (alanine aminotransferase or aspartate aminotransferase more than twice the upper limit of the normal range);④patients with other serious comorbidities; ⑤patients with asymptomatic hypoglycemia; ⑥those who were unwilling to participate or unable to cooperate; Withdrawal criteria ①inability to complete capillary glucose testing during the trial; ②inability of the patient to eat regularly. Methods Subjects who fulfilled the conditions of the enrollment criteria were divided into two groups by random number method, i.e., smart insulin dosage software-adjusted blood glucose group (App group) and traditional physician experience-adjusted blood glucose group (Control group). All patients completed capillary glucose testing before three meals and at bedtime and wore a real-time CGM, which was a Silicon Kinetics Continuous Glucose Monitoring System (SiJoy GS1 CGM, Shenzhen Silicon Bionics Technology Co., Ltd.) for all subjects. The duration of the study was 5–7 days. At the end of the study, capillary glucose and CGM data were compared between the two groups. Principles of blood glucose adjustment Basal insulin was calculated in both groups as 0.25 u per kg of body weight in both groups. Mealtime insulin was calculated differently, with the App group calculating the insulin dose based on the Smart App and the Control group adjusting the insulin dose based on the physician's experience. The smart dose system is a suite of software that incorporates patient-specific dose-response data into a data model and then calculates the insulin dose, which is the dose needed to reach the next desire 18 . The calculation method for the smart insulin dosing system used in this study is described in the Supplementary. Hypoglycemia defined as capillary blood glucose less than 3.9 mmol/L. The glucose range for TIR was defined as 3.9 to 10 mmol/L, time above range (TAR) is greater than 10 mmol/L, time blow range (TBR) is less than 3.9 mmol/L. The formula for GMI (%) is 3.31 + 0.02392 x (mean glucose(mg/dl)form CGM). The GRI is calculated as described in previous literature 19 . Statistical analysis SPSS 26.0 software was used for statistical analyses. Normally distributed data were presented as means + standard deviation and non-normally distributed data were expressed as median and interquartile intervals (IQ 25–75%). Categorical variables were presented as frequencies or percentages. The t-test and chi-square test were used for comparisons between two groups for continuous and categorical variables, respectively. Line, scatter, and bar graphs were plotted using GraphPad (8.0). Gplus system V3.58.0(Shanghai iMedpower Tech. LTD, China)was applied to process CGM data. Two-sided tests p < 0.05 were considered significant. Results Study population A total of 92 diabetic patients receiving insulin therapy participated in this study (Fig. 2 ). After randomization, there were 47 subjects in the App group and 45 subjects in the control group. Four patients in the App group withdrew from the study (two patients were too sick to eat and two patients refused capillary glucose monitoring) and two patients in the control group refused capillary glucose measurements and withdrew from the study. Ultimately, 43 patients in each of the App and control groups completed the study. The total study population was 65.1% male, with a median age of 58 years and a median number of days of intervention of 7 days. Comparison of baseline data between the two groups of patients The baseline data of the two groups are shown in Table 1 . Subjects in both groups were matched for gender, age, body mass index (BMI), systolic blood pressure (SBP) and diastolic blood pressure (DBP). Fasting glucose, fasting C-peptide, fasting insulin and duration of diabetes did not differ between the two groups. 36% (31/86) of the subjects had cirrhosis, and the percentage of patients with cirrhosis did not differ between the two groups (32.6% vs 39.5%, P = 0.500). A total of 25.6% (22/86) of subjects underwent surgery during the study period, and the percentage of operated patients did not differ between the two groups (20.9% vs. 30.2%, P = 0.323). Table 1 Baseline information for both groups of subjects. Characteristics App group (n = 43) Control group (n = 43) P Sex (Male) 28 (65.1%) 28 (65.1%) 0.665 Age (year) 58.0 (43.0, 62.0) 58.0 (50.0, 65.0) 0.282 BMI (kg/m 2 ) 24.3 (21.5, 26.4) 24.16 (21.15, 25.71) 0.675 SBP (mmHg) 125.0 (117.0, 136.0) 125.0 (117.0, 136.0) 0.248 DBP (mmHg) 79.0 (70.0, 89.0) 79.0 (70.0, 89.0) 0.247 FPG (mmol/L) 8.49 (7.07, 10.74) 8.99 (7.9, 13.16) 0.090 FCP (pg/dL) 1.20 0.60, 2.56) 1.76 1.08, 2.72) 0.172 FIN (ug/dL) 8.90 (6.00, 14.14) 13.08 (5.91, 25.04) 0.132 HbA1c (%) 9.80 (8.00, 11.1) 9.40 (8.25, 10.5) 0.675 Creatinine (mg/dL) 61.9 (50.6, 81.2) 69.9 (56.0, 82.6) 0.195 eGFR (ml/min1.73m2) 108.4 (88.4, 145.0) 107.1 (79.7, 127.9) 0.210 Duration of DM (year) 5.0 (0.1, 12.0) 7.0 (0.5, 15.0) 0.384 Cirrhosis (N, %) 14 (32.6%) 17 (39.5%) 0.500 Surgeries (N, %) 9 (20.9%) 13 (30.2%) 0.323 BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting blood glucose; FCP, Fasting C-peptide; FIN, fasting insulin; HbA1c, hemoglobin A1c; eGFR, estimated glomerular filtration rate; DM, diabetes mellitus. Comparison of capillary blood glucose and dose of insulin used in two groups of patients All subjects received daily capillary blood glucose testing before three meals and at bedtime, and we compared the mean values of capillary blood glucose over the study period, the results of which are shown in Table 2 and Fig. 3 . There was no difference in capillary blood glucose before breakfast (App group vs Control group, 7.56 (6.94, 9.53) vs 8.26 (7.28, 9.65)mmol/L, P = 0.148) and capillary blood glucose before lunch (10.21 (8.58, 12.16) vs 11.17 (9.89, 14.86)mmol/L, P = 0.096) between the two groups. Patients in the APP group had lower capillary blood glucose before dinner (10.07 (8.70, 11.60) vs 11.42 (9.94, 14.46)mmol/L, P = 0.006) and lower capillary blood glucose at bedtime (9.93 (8.83, 11.33) vs 10.71 (9.62, 12.72)mmol/L, P = 0.039). There were no significant differences between the two groups in terms of total insulin dose (230.0 (200.0, 293.0) vs 244.0 (172.0, 297.0)U, P = 0.972), mean daily insulin dose (37.0 (28.7, 46.4) vs 35.7 (31.9, 45.0)U, P = 0.675), and insulin dose per kilogram of body weight (0.59 (0.47, 0.73) vs 0.57 (0.47, 0.74)U/kg, P = 0.789). The overall incidence of hypoglycemia during the study period was 17.4% (15/86). There was no difference in the incidence of hypoglycemia between the two groups (14.0% vs 20.9%, P = 0.394) (Table 2 ). Table 2 Comparison of mean capillary blood glucose and dose of insulin used in the two groups at the end of the study. Parameters App group (n = 43) Control group (n = 43) P Days 7.0 (6.0, 7.0) 7.0 (6.0, 7.0) 0.271 CBG before breakfast(mmol/L) (mmol/L) (mmol/L) 7.56 (6.94, 9.53) 8.26 (7.28, 9.65) 0.148 CBG before lunch (mmol/L) 10.21 (8.58, 12.16) 11.17 (9.89, 14.86) 0.096 CBG before dinner (mmol/L) 10.07 (8.70, 11.60) 11.42 (9.94, 14.46) 0.006 CBG before bedtime (mmol/L) 9.93 (8.83, 11.33) 10.71 (9.62, 12.72) 0.039 Total insulin dose (U) (U) 230.0 (200.0, 293.0) 244.0 (172.0, 297.0) 0.972 Total insulin dose /weight (U/kg) 0.59 (0.47, 0.73) 0.57 (0.47, 0.74) 0.789 Average daily insulin dose (U/day) 37.0 (28.7, 46.4) 35.7 (31.9, 45.0) 0.675 Hypoglycemia (N, %) 6 (14.0%) 9 (20.9%) 0.394 CBG, capillary blood glucose. Comparison of CGM parameters between the two groups of patients As show in Table 3 , the mean glucose (MG) ( 9.01 (8.04, 9.70) vs 10.45 (8.78, 12.27)mmol/L, P = 0.003)and standard deviation (SD) of blood glucose(3.00 (2.29, 3.46) vs 3.48 (2.84, 4.04)mmol/L, P = 0.011༉in App group were lower than those in the control group. The lowest glucose recorded by CGM did not differ between the two groups, and the highest blood glucose value was higher in the control group (20.60 (17.05, 24.25) vs 18.20 (15.50, 20.90)mmol/L, P = 0.010). GMI was lower in the App group (7.81 (7.09, 8.59) vs 7.19 (6.77, 7.49)%, P = 0.003) (Fig. 4 A). Patients in the App group had higher TIR (64 (51, 73) vs 47 (29.5, 67.0)%, P = 0.001), lower TAR (35 (23, 47) vs 52 (29.5, 70.0)%, P = 0.003), and TBR did not differ between the two groups (P = 0.469) (Fig. 4 B). Indicators reflecting glycemic fluctuations also differed between the two groups. Patients in the App group had lower mean amplitude of glucose excursions (MAGE)༈7.28 (5.80, 8.39) vs 7.98 (6.82, 9.44)mmol/L, P = 0.018༉(Fig. 4 C), mean of daily difference (MODD)༈2.42 (1.91, 2.76) vs 2.85 (2.13, 3.47) mmol/L, P = 0.018༉(Fig. 4 D)and largest amplitude of glycemic excursion (LAGE)༈14.20 (12.10, 17.80) vs 16.10 (13.15, 18.85)mmol/L, P = 0.029༉(Fig. 4 E), and coefficient of variation (CV) was not different between the two groups ( P = 0.757). Patients in the App group had a lower GRI (35.84 (28.44, 52.60) vs62.98 (39.50, 83.07), P = 0.000) (Fig. 4 F). Figure 5 shows the ambulatory glucose profiles (AGP), the overall glucose of patients in the App group was lower than that in the control group. Table 3 Comparison of CGM data between the two groups of patients after the intervention. Parameters App group (n = 43) Control group (n = 43) P MG (mmol/L) 9.01 (8.04, 9.70) 10.45 (8.78, 12.27) 0.003 SD (mmol/L) 3.00 (2.29, 3.46) 3.48 (2.84, 4.04) 0.011 CV (%) 0.34 (0.28, 0.37) 0.33 (0.27, 0.38) 0.757 MAGE (mmol/L) 7.28 (5.80, 8.39) 7.98 (6.82, 9.44) 0.018 MODD (mmol/L) 2.42 (1.91, 2.76) 2.85 (2.13, 3.47) 0.018 LAGE (mmol/L) 14.20 (12.10, 17.80) 16.10 (13.15, 18.85) 0.029 Min-G (mmol/L) 3.30 (2.90, 4.10) 3.60 (2.85, 6.45) 0.205 Max-G(mmol/L) 18.20 (15.50, 20.90) 20.60 (17.05, 24.25) 0.010 TIR (%) 64.0 (51.0, 73.0) 47.0 (29.5, 67.0) 0.001 TAR (%) 35.0 (23.0, 47.0) 52.0 (29.5, 70.0) 0.003 TBR (%) 1.0 (0, 2.0) 0 (0, 2.5) 0.469 GMI (%) 7.81 (7.09, 8.59) 7.19 (6.77, 7.49) 0.003 GRI 35.84 (28.44, 52.60) 62.98 (39.50, 83.07) 0.000 MG, Mean glucose; CV, coefficient of variation; SD, standard deviation; MAGE, mean amplitude of glucose excursions;MODD, mean of daily difference༛LAGE, largest amplitude of glycemic excursion, Min-GM, minimum glucose; Max-G, maximum glucose; TIR, time in range; TAR, time above range; TBR, time below range; GMI, glucose management index; GRI, glycemic risk index. Comparison of patients with TIR greater than 70% and patients with TIR less than 70% We used 70% as the cut-off point for TIR and categorized the patients into a TIR greater than 70% group and a TIR less than 70% group. Comparison of the two groups is shown in Table S1 . There were no differences in gender, age, BMI, HbA1c, C-peptide, incidence of hypoglycemia and percentage of cirrhosis between the two groups. At the end of the study, 29.1% (25/86) of patients had a TIR of more than 70%, including 68% in the App group. Patients in the TIR greater than 70% group had lower baseline fasting glucose (8.09 (7.00, 8.97) vs 9.78 (7.85, 13.13)mmol/L, P = 0.006). Glycemic control was better in the group with a TIR greater than 70%, and total insulin dose (222.0 (160.0, 271.5) vs 247 (203.5, 312.5)U, P = 0.016), average daily insulin dose (32.14 (25.14, 40.93) vs 39.14 (32.23, 48.22)U, P = 0.002), and insulin dose per kilogram of body weight (0.51 (0.38, 0.61) vs 0.63 (0.50, 0.76)U, P = 0.007) were lower in this group. Discussion Hyperglycemia is common in hospitalized patients and is a detrimental factor in the severity of illness and cost of hospitalization. Insulin is an important tool for glycemic control, and insulin dose adjustments are often based on physician experience. Smart insulin dosing system helps doctors and patients get more accurate insulin doses. In this study, we combined the mature AI technology with the glycemic profile characteristics of diabetic patients, based on the individualized characteristics of patients, fully utilized the advantages of CGM and referred to its trend arrow information to provide timely and effective pre-meal insulin dosage adjustment for diabetic patients. We conducted this randomized controlled trial to compare the effects of smart insulin dosing software with the empirical adjustment of glucose by traditional physicians. In this study, patients whose blood glucose was adjusted according to App had lower capillary blood glucose levels, especially before dinner and at bedtime, compared with those whose blood glucose was adjusted empirically by traditional physicians. According to the CGM data, the average blood glucose of the App group was lower, which also indicated the consistency between CGM data and capillary blood glucose. Previous studies have shown that insulin dose calculators are better for postprandial blood glucose control in insulin treated patients, and this study only measured capillary blood glucose 5 . Similar studies have also shown the importance of smart insulin dosing software for postprandial glucose 20 . These studies are consistent with the results of this study, showing that smart insulin dosing software has better hypoglycemic effects in diabetic patients. In addition, there was no difference in total insulin use between the two groups, and patients in the traditional physician group had a higher incidence of hypoglycemia. Research from Sara et al. 21 shows that smart insulin dosing software can reduce insulin doses, which is where the results differ from our study. CGM recording of dynamic glucose data and trend arrows is another feature of this study. CGM is one of the great advances in diabetes technology, which not only records glucose data, but also provides more meaningful indicators of diabetes management than HbA1c, such as TIR, CV, GMI, MAGE 22,23 . Studies have shown that TIR is directly associated with the risk of diabetes complications and death 24,25 . In hospitalized patients, hyperglycemia, hypoglycemia, and glucose variability were all associated with adverse outcomes. In our study, patients in the App group had higher TIR and lower MAGE, suggesting that patients in the App group had less glucose fluctuation 26 . This is slightly different from the results of Secher's study, which found that smart insulin dosing software lowered CV but did not significantly change TIR compared to usual care 27 . Unlike previous studies, this study took into account the information from the trend arrows and fully considered the magnitude and direction of the patients' blood glucose changes to avoid too high or too low blood glucose. The trend arrows might be one of the reasons why the incidence of hypoglycemia in the App group was smaller than that in the traditional physician group. In this study, 29% of the patients had a TIR of more than 70%, which may be related to the subjects having very high blood glucose and undergoing surgery during the study period. In addition, 36% of diabetic patients had cirrhosis, which has a higher risk phase for hyperglycemia 28 . Moreover, studies have shown that diabetic patients with cirrhosis have worse glycemic control compared to diabetic patients without cirrhosis 29 . This study has some shortcomings. First, the length of the study was only maintained for one week due to the fact that the subjects were non-endocrinology inpatients. Second, the HbA1C was not assessed at the end of the study due to the length of the study, which is an important indicator of diabetes management. In summary, among non-endocrinology hospitalized diabetic patients, smart insulin dosing software with trending arrows was shown to be more effective in controlling hyperglycemia without increasing the risk of hypoglycemia compared with traditional physician experience. Additional clinical studies are needed to further support our findings. Declarations The authors declare no competing interests. Funding: This work was funded by the Shanghai Municipality Health Commission (202140085), and the Shanghai Public Health Clinical Center (KY-GW-2023-13). Author Contribution Author contributions contributed to the study conception and design. Material preparation, data collection, and analysis were performed by L.H.Z, C.W.W, C. H. W, and D.D.Q. The first draft of the manuscript was written by L.H.Z. and X.L.Z. All authors commented on previous versions of the manuscript. Xia Yu provided technical support. All authors read and approved the final manuscript. 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Flash glucose monitoring and automated bolus calculation in type 1 diabetes treated with multiple daily insulin injections: a 26 week randomised, controlled, multicentre trial. Diabetologia. 2021;64(12):2713–2724. Lee WG, Wells CI, McCall JL, Murphy R, Plank LD. Prevalence of diabetes in liver cirrhosis: A systematic review and meta-analysis. Diabetes Metab Res Rev. 2019;35(6):e3157. Castera L, Cusi K. Diabetes and cirrhosis: Current concepts on diagnosis and management. Hepatology. 2023;77(6):2128–2146. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Smart insulin dosage app design program. Table S1 Comparison of patients with TIR greater than 70% and those with TIR less than 70%. Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2026 Read the published version in Endocrine → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 14 Jun, 2025 Submission checks completed at journal 14 Jun, 2025 First submitted to journal 12 Jun, 2025 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. 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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-6878924","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485764454,"identity":"f2d19348-c441-4d4a-80ea-bca0bc815f72","order_by":0,"name":"Lihua Zhou","email":"","orcid":"","institution":"Shanghai Public Health Clinical Center","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Zhou","suffix":""},{"id":485764455,"identity":"24515032-be76-47fb-b0d9-37e1dda5cafe","order_by":1,"name":"Chenwei Wu","email":"","orcid":"","institution":"Shanghai Public Health Clinical 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Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZiB+AGJIAPEHAxs74rQkQLUwzihISybOJpgWZp4PhxgbCKk2OM788EFCxR27+bObHz62MTjAzMB++OgGfFokm9mMDRLOPEtunHPM2DjH4A4fA09a2g18WviZGcwkEtsOJzNLJJhJ5xg8Y2aQ4DHDq4WNmf0bWAubRPo3aQuDw4wNhLTwM/OAbbHjkcgxk2YgRotkM08x0C+HEyQkcooNewzSktkI+cXg/PGNDz5UHLaXn5G+8cGPPzZ2/OyHj+HVAgOJDXDfEaMcBOyJVTgKRsEoGAUjEAAAuyRGLCxH+p0AAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Public Health Clinical Center","correspondingAuthor":true,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-06-12 09:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6878924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6878924/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12020-025-04509-z","type":"published","date":"2026-02-16T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87573550,"identity":"eaf0b5a7-8898-4121-b2bc-82c672fd45a7","added_by":"auto","created_at":"2025-07-25 11:19:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57668,"visible":true,"origin":"","legend":"\u003cp\u003eResearch route.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/05094295c685d7a2bc4250bb.png"},{"id":87574413,"identity":"fb604879-a326-4019-b66e-ba8f37892707","added_by":"auto","created_at":"2025-07-25 11:27:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48708,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of this study.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/2637a7eff0c25303fd3165de.png"},{"id":87573553,"identity":"b6dc93b3-1215-4317-a922-2a3366baa9ee","added_by":"auto","created_at":"2025-07-25 11:19:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13199,"visible":true,"origin":"","legend":"\u003cp\u003eCapillary blood glucose in both groups after intervention. * represents \u003cem\u003eP\u003c/em\u003e＜0.05, ** represents \u003cem\u003eP\u003c/em\u003e＜0.01.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/ce318e9ed8cb66caf12bf264.png"},{"id":87574414,"identity":"2ef66b3f-125a-4ed7-a576-4a913f9c83ec","added_by":"auto","created_at":"2025-07-25 11:27:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60289,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of CGM parameters, GMI (A), TIA/TAR/TBR (B), MAGE (C), MODD (D), LAGE (E) and GRI (F) in two groups of patients. * represents \u003cem\u003eP\u003c/em\u003e＜0.05, ** represents \u003cem\u003eP\u003c/em\u003e＜0.01.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/085d3a1f95ce4e639baa1eaa.png"},{"id":87573555,"identity":"f6628f27-4f97-4e54-9b4e-ffbb15b7c07f","added_by":"auto","created_at":"2025-07-25 11:19:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":812179,"visible":true,"origin":"","legend":"\u003cp\u003eAmbulatory glucose profiles of the two groups of patients. Data were expressed as median and interquartile intervals (IQ 25-75%).\u003c/p\u003e","description":"","filename":"Figure15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/11123d13aa23fab24ed328ea.jpg"},{"id":103251902,"identity":"5fcf4081-4a8b-4e26-87ef-9e687f917b7b","added_by":"auto","created_at":"2026-02-23 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1799420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/fcd86115-6bb8-47e4-a7db-52ed8e1fffa9.pdf"},{"id":87573552,"identity":"be068bb2-b61b-4bd8-b358-48fcb7a7150e","added_by":"auto","created_at":"2025-07-25 11:19:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16706,"visible":true,"origin":"","legend":"\u003cp\u003eSmart insulin dosage app design program.\u003c/p\u003e\n\u003cp\u003eTable S1 Comparison of patients with TIR greater than 70% and those with TIR less than 70%.\u003c/p\u003e","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6878924/v1/eb9f81c310ac7946564e625f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Smart insulin dosing software based on trend arrows is an effective means of controlling hyperglycemia in non-endocrinological inpatients with diabetes: a randomized controlled trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe latest data show that the number of diabetes patients worldwide has exceeded 500 million\u003csup\u003e1\u003c/sup\u003e, with a prevalence of more than 10% in the Chinese population \u003csup\u003e2\u003c/sup\u003e. Insulin is an important tool for controlling hyperglycemia, and the method of adjusting insulin dosage is indeed purely empirical, which is particularly difficult in non-endocrinology departments. Studies have shown that non-endocrinologists generally lack knowledge of insulin type, dosage, and glycemic management, and there are significant barriers to in-hospital glycemic management\u003csup\u003e3\u003c/sup\u003e. Disease stress, therapeutic agents, and disease severity are important causes of hyperglycemia during hospitalization\u003csup\u003e4\u003c/sup\u003e. Hyperglycemia is an important factor affecting the length of hospital stay and total cost of hospitalization in diabetic patients. Artificial Intelligence (AI) is a great boon for diabetic patients and plays an important role in insulin dosage adjustment. The Insulin Dosing System is a software package that incorporates patient-specific dose-response data into a data model and then calculates the dose of new medication needed to reach the next desired goal. In 2003, Gross first suggested that smart insulin dose calculators could play an important role in controlling postprandial blood glucose in patients with type 1 diabetes mellitus(T1DM)\u003csup\u003e5\u003c/sup\u003e. Since then, smart dosing systems have begun to spread among diabetic patients and have shown some advantages over clinicians adjusting glucose empirically. Results of a randomized controlled trial suggest that smart software to calculate insulin doses improves the quality of glycemic control and does not increase hypoglycemia compared to nurse-adjusted insulin doses\u003csup\u003e6\u003c/sup\u003e. Meanwhile, researchers in Germany have found that hemoglobin A1c (HbA1c) can be better controlled through the use of a smartphone glucose management app\u003csup\u003e7\u003c/sup\u003e. Thus, we proposed to explore the glucose lowering effect of smart insulin dosing system in non-endocrinology hospitalized diabetic patients. Therefore, smart insulin dosing systems offer many benefits to diabetic patients. We propose to investigate the glucose-lowering effect of smart insulin dosing system on non-endocrinology hospitalized diabetic patients.\u003c/p\u003e\u003cp\u003eContinuous glucose monitoring (CGM) technology is an important and effective tool for blood glucose management. Factors such as accuracy and convenience have led to the widespread use of CGM for diabetic patients and its great clinical benefits\u003csup\u003e8,9\u003c/sup\u003e. Given the unique benefits of AI and CGM, several researchers have combined the two for glycemic management\u003csup\u003e10\u003c/sup\u003e, Breton et al. found that CGM combined with an intelligent insulin dose titration system reduced glycemic fluctuations\u003csup\u003e11\u003c/sup\u003e, both of which were conducted in patients with T1DM. CGM data is a continuous nontemporal series that can provide rich information about glucose, which promotes the use of data mining to further characterize glucose dynamics. Time in range (TIR) is a new metric for assessing glycemic management. Several observational studies have shown that TIR is strongly associated with pathophysiologic characteristics of diabetes, indicators of glycemic variability, and risk of complications and mortality\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. The Glycemic management index (GMI) is a new measure of short-term glycemic control proposed in 2018\u003csup\u003e15\u003c/sup\u003e, which is also recommended by current guidelines\u003csup\u003e16\u003c/sup\u003e. Similarly, Glycemic Risk Index (GRI) is an important parameter of CGM and plays an important role in the management of microvascular complications in diabetes\u003csup\u003e17\u003c/sup\u003e. The trend arrow is an important feature of the CGM that shows the magnitude and direction of blood glucose changes. Users can utilize the trend arrows to predict future blood glucose levels so that they can take the next step in treatment, which can help alleviate impending hypoglycemia or hyperglycemia. Most studies on smart insulin dosing software have not included trend arrow information from CGM. Therefore, this study proposes to make full use of trend arrow information to combine smart insulin dosing software and CGM as a method of glycemic control. The glycemic control effect of this method was compared with the traditional physician's empirical adjustment of blood glucose in non-endocrinology hospitalized diabetic patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003eA total of 92 diabetic patients were included in this study, who were hospitalized (non-endocrine department) at Shanghai Public Health Clinical Center between October 2023 and September 2024, and all of them and used multiple daily insulin injections to control hyperglycemia. The study was approved by the Ethical Research Committee of the Shanghai Public Health Clinical Center and the clinical trial registration number was NCT05389839. All subjects signed a written informed consent.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003e①18\u0026ndash;75 years old; ②insulin for glycemic control, and no oral hypoglycemic drugs; ③type 1 or type 2 diabetes mellitus; ④non-endocrine inpatient; ⑤three meals per day.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003cp\u003e①those who had a myocardial infarction or cardiovascular event in the past three months; ②patients with renal insufficiency (estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e); ③patients with hepatic insufficiency (alanine aminotransferase or aspartate aminotransferase more than twice the upper limit of the normal range);④patients with other serious comorbidities; ⑤patients with asymptomatic hypoglycemia; ⑥those who were unwilling to participate or unable to cooperate;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWithdrawal criteria\u003c/strong\u003e\u003cp\u003e①inability to complete capillary glucose testing during the trial; ②inability of the patient to eat regularly.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cp\u003eSubjects who fulfilled the conditions of the enrollment criteria were divided into two groups by random number method, i.e., smart insulin dosage software-adjusted blood glucose group (App group) and traditional physician experience-adjusted blood glucose group (Control group). All patients completed capillary glucose testing before three meals and at bedtime and wore a real-time CGM, which was a Silicon Kinetics Continuous Glucose Monitoring System (SiJoy GS1 CGM, Shenzhen Silicon Bionics Technology Co., Ltd.) for all subjects. The duration of the study was 5\u0026ndash;7 days. At the end of the study, capillary glucose and CGM data were compared between the two groups.\u003c/p\u003e\n\u003ch3\u003ePrinciples of blood glucose adjustment\u003c/h3\u003e\n\u003cp\u003eBasal insulin was calculated in both groups as 0.25 u per kg of body weight in both groups. Mealtime insulin was calculated differently, with the App group calculating the insulin dose based on the Smart App and the Control group adjusting the insulin dose based on the physician's experience.\u003c/p\u003e\u003cp\u003eThe smart dose system is a suite of software that incorporates patient-specific dose-response data into a data model and then calculates the insulin dose, which is the dose needed to reach the next desire\u003csup\u003e18\u003c/sup\u003e. The calculation method for the smart insulin dosing system used in this study is described in the Supplementary.\u003c/p\u003e\u003cp\u003eHypoglycemia defined as capillary blood glucose less than 3.9 mmol/L. The glucose range for TIR was defined as 3.9 to 10 mmol/L, time above range (TAR) is greater than 10 mmol/L, time blow range (TBR) is less than 3.9 mmol/L. The formula for GMI (%) is 3.31\u0026thinsp;+\u0026thinsp;0.02392 x (mean glucose(mg/dl)form CGM). The GRI is calculated as described in previous literature\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eSPSS 26.0 software was used for statistical analyses. Normally distributed data were presented as means\u0026thinsp;+\u0026thinsp;standard deviation and non-normally distributed data were expressed as median and interquartile intervals (IQ 25\u0026ndash;75%). Categorical variables were presented as frequencies or percentages. The t-test and chi-square test were used for comparisons between two groups for continuous and categorical variables, respectively. Line, scatter, and bar graphs were plotted using GraphPad (8.0). Gplus system V3.58.0(Shanghai iMedpower Tech. LTD, China)was applied to process CGM data. Two-sided tests \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eA total of 92 diabetic patients receiving insulin therapy participated in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After randomization, there were 47 subjects in the App group and 45 subjects in the control group. Four patients in the App group withdrew from the study (two patients were too sick to eat and two patients refused capillary glucose monitoring) and two patients in the control group refused capillary glucose measurements and withdrew from the study. Ultimately, 43 patients in each of the App and control groups completed the study. The total study population was 65.1% male, with a median age of 58 years and a median number of days of intervention of 7 days.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComparison of baseline data between the two groups of patients\u003c/h3\u003e\n\u003cp\u003eThe baseline data of the two groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Subjects in both groups were matched for gender, age, body mass index (BMI), systolic blood pressure (SBP) and diastolic blood pressure (DBP). Fasting glucose, fasting C-peptide, fasting insulin and duration of diabetes did not differ between the two groups. 36% (31/86) of the subjects had cirrhosis, and the percentage of patients with cirrhosis did not differ between the two groups (32.6% vs 39.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.500). A total of 25.6% (22/86) of subjects underwent surgery during the study period, and the percentage of operated patients did not differ between the two groups (20.9% vs. 30.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.323).\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\u003eBaseline information for both groups of subjects.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApp group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (65.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (65.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.0 (43.0, 62.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.0 (50.0, 65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.3 (21.5, 26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.16 (21.15, 25.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125.0 (117.0, 136.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125.0 (117.0, 136.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.0 (70.0, 89.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.0 (70.0, 89.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.49 (7.07, 10.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.99 (7.9, 13.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFCP (pg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20 0.60, 2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.76 1.08, 2.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIN (ug/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.90 (6.00, 14.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.08 (5.91, 25.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.132\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.80 (8.00, 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.40 (8.25, 10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.9 (50.6, 81.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.9 (56.0, 82.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (ml/min1.73m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108.4 (88.4, 145.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.1 (79.7, 127.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of DM (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.0 (0.1, 12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0 (0.5, 15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirrhosis (N, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgeries (N, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (30.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting blood glucose; FCP, Fasting C-peptide; FIN, fasting insulin; HbA1c, hemoglobin A1c; eGFR, estimated glomerular filtration rate; DM, diabetes mellitus.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eComparison of capillary blood glucose and dose of insulin used in two groups of patients\u003c/h3\u003e\n\u003cp\u003eAll subjects received daily capillary blood glucose testing before three meals and at bedtime, and we compared the mean values of capillary blood glucose over the study period, the results of which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was no difference in capillary blood glucose before breakfast (App group vs Control group, 7.56 (6.94, 9.53) vs 8.26 (7.28, 9.65)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148) and capillary blood glucose before lunch (10.21 (8.58, 12.16) vs 11.17 (9.89, 14.86)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096) between the two groups. Patients in the APP group had lower capillary blood glucose before dinner (10.07 (8.70, 11.60) vs 11.42 (9.94, 14.46)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and lower capillary blood glucose at bedtime (9.93 (8.83, 11.33) vs 10.71 (9.62, 12.72)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039). There were no significant differences between the two groups in terms of total insulin dose (230.0 (200.0, 293.0) vs 244.0 (172.0, 297.0)U, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.972), mean daily insulin dose (37.0 (28.7, 46.4) vs 35.7 (31.9, 45.0)U, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.675), and insulin dose per kilogram of body weight (0.59 (0.47, 0.73) vs 0.57 (0.47, 0.74)U/kg, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.789). The overall incidence of hypoglycemia during the study period was 17.4% (15/86). There was no difference in the incidence of hypoglycemia between the two groups (14.0% vs 20.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.394) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eComparison of mean capillary blood glucose and dose of insulin used in the two groups at the end of the study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApp group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.0 (6.0, 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0 (6.0, 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCBG before breakfast(mmol/L) (mmol/L) (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.56 (6.94, 9.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.26 (7.28, 9.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCBG before lunch (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.21 (8.58, 12.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.17 (9.89, 14.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCBG before dinner (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.07 (8.70, 11.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.42 (9.94, 14.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCBG before bedtime (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.93 (8.83, 11.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.71 (9.62, 12.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal insulin dose (U)\u003c/p\u003e\u003cp\u003e(U)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e230.0 (200.0, 293.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e244.0 (172.0, 297.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal insulin dose /weight (U/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59 (0.47, 0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57 (0.47, 0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage daily insulin dose (U/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.0 (28.7, 46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.7 (31.9, 45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoglycemia (N, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eCBG, capillary blood glucose.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eComparison of CGM parameters between the two groups of patients\u003c/h2\u003e\u003cp\u003eAs show in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the mean glucose (MG) ( 9.01 (8.04, 9.70) vs 10.45 (8.78, 12.27)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003)and standard deviation (SD) of blood glucose(3.00 (2.29, 3.46) vs 3.48 (2.84, 4.04)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011༉in App group were lower than those in the control group. The lowest glucose recorded by CGM did not differ between the two groups, and the highest blood glucose value was higher in the control group (20.60 (17.05, 24.25) vs 18.20 (15.50, 20.90)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). GMI was lower in the App group (7.81 (7.09, 8.59) vs 7.19 (6.77, 7.49)%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Patients in the App group had higher TIR (64 (51, 73) vs 47 (29.5, 67.0)%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), lower TAR (35 (23, 47) vs 52 (29.5, 70.0)%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and TBR did not differ between the two groups (P\u0026thinsp;=\u0026thinsp;0.469) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Indicators reflecting glycemic fluctuations also differed between the two groups. Patients in the App group had lower mean amplitude of glucose excursions (MAGE)༈7.28 (5.80, 8.39) vs 7.98 (6.82, 9.44)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018༉(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), mean of daily difference (MODD)༈2.42 (1.91, 2.76) vs 2.85 (2.13, 3.47) mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018༉(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD)and largest amplitude of glycemic excursion (LAGE)༈14.20 (12.10, 17.80) vs 16.10 (13.15, 18.85)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029༉(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), and coefficient of variation (CV) was not different between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.757). Patients in the App group had a lower GRI (35.84 (28.44, 52.60) vs62.98 (39.50, 83.07), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the ambulatory glucose profiles (AGP), the overall glucose of patients in the App group was lower than that in the control group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of CGM data between the two groups of patients after the intervention.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApp group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.01 (8.04, 9.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.45 (8.78, 12.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\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=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.29, 3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.48 (2.84, 4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\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=\".\" colname=\"c2\"\u003e\u003cp\u003e0.34 (0.28, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.33 (0.27, 0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.757\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.28 (5.80, 8.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.98 (6.82, 9.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\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=\".\" colname=\"c2\"\u003e\u003cp\u003e2.42 (1.91, 2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.85 (2.13, 3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAGE (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.20 (12.10, 17.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.10 (13.15, 18.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin-G (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.30 (2.90, 4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.60 (2.85, 6.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax-G(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.20 (15.50, 20.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.60 (17.05, 24.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010\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=\".\" colname=\"c2\"\u003e\u003cp\u003e64.0 (51.0, 73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.0 (29.5, 67.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.0 (23.0, 47.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.0 (29.5, 70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0, 2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0, 2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.469\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.81 (7.09, 8.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.19 (6.77, 7.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.84 (28.44, 52.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.98 (39.50, 83.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eMG, Mean glucose; CV, coefficient of variation; SD, standard deviation; MAGE, mean amplitude of glucose excursions;MODD, mean of daily difference༛LAGE, largest amplitude of glycemic excursion, Min-GM, minimum glucose; Max-G, maximum glucose; TIR, time in range; TAR, time above range; TBR, time below range; GMI, glucose management index; GRI, glycemic risk index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eComparison of patients with TIR greater than 70% and patients with TIR less than 70%\u003c/h2\u003e\u003cp\u003eWe used 70% as the cut-off point for TIR and categorized the patients into a TIR greater than 70% group and a TIR less than 70% group. Comparison of the two groups is shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. There were no differences in gender, age, BMI, HbA1c, C-peptide, incidence of hypoglycemia and percentage of cirrhosis between the two groups. At the end of the study, 29.1% (25/86) of patients had a TIR of more than 70%, including 68% in the App group. Patients in the TIR greater than 70% group had lower baseline fasting glucose (8.09 (7.00, 8.97) vs 9.78 (7.85, 13.13)mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Glycemic control was better in the group with a TIR greater than 70%, and total insulin dose (222.0 (160.0, 271.5) vs 247 (203.5, 312.5)U, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), average daily insulin dose (32.14 (25.14, 40.93) vs 39.14 (32.23, 48.22)U, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and insulin dose per kilogram of body weight (0.51 (0.38, 0.61) vs 0.63 (0.50, 0.76)U, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) were lower in this group.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHyperglycemia is common in hospitalized patients and is a detrimental factor in the severity of illness and cost of hospitalization. Insulin is an important tool for glycemic control, and insulin dose adjustments are often based on physician experience. Smart insulin dosing system helps doctors and patients get more accurate insulin doses. In this study, we combined the mature AI technology with the glycemic profile characteristics of diabetic patients, based on the individualized characteristics of patients, fully utilized the advantages of CGM and referred to its trend arrow information to provide timely and effective pre-meal insulin dosage adjustment for diabetic patients. We conducted this randomized controlled trial to compare the effects of smart insulin dosing software with the empirical adjustment of glucose by traditional physicians. In this study, patients whose blood glucose was adjusted according to App had lower capillary blood glucose levels, especially before dinner and at bedtime, compared with those whose blood glucose was adjusted empirically by traditional physicians. According to the CGM data, the average blood glucose of the App group was lower, which also indicated the consistency between CGM data and capillary blood glucose. Previous studies have shown that insulin dose calculators are better for postprandial blood glucose control in insulin treated patients, and this study only measured capillary blood glucose\u003csup\u003e5\u003c/sup\u003e. Similar studies have also shown the importance of smart insulin dosing software for postprandial glucose\u003csup\u003e20\u003c/sup\u003e. These studies are consistent with the results of this study, showing that smart insulin dosing software has better hypoglycemic effects in diabetic patients. In addition, there was no difference in total insulin use between the two groups, and patients in the traditional physician group had a higher incidence of hypoglycemia. Research from Sara et al.\u003csup\u003e21\u003c/sup\u003e shows that smart insulin dosing software can reduce insulin doses, which is where the results differ from our study.\u003c/p\u003e\u003cp\u003eCGM recording of dynamic glucose data and trend arrows is another feature of this study. CGM is one of the great advances in diabetes technology, which not only records glucose data, but also provides more meaningful indicators of diabetes management than HbA1c, such as TIR, CV, GMI, MAGE\u003csup\u003e22,23\u003c/sup\u003e. Studies have shown that TIR is directly associated with the risk of diabetes complications and death\u003csup\u003e24,25\u003c/sup\u003e. In hospitalized patients, hyperglycemia, hypoglycemia, and glucose variability were all associated with adverse outcomes. In our study, patients in the App group had higher TIR and lower MAGE, suggesting that patients in the App group had less glucose fluctuation\u003csup\u003e26\u003c/sup\u003e. This is slightly different from the results of Secher's study, which found that smart insulin dosing software lowered CV but did not significantly change TIR compared to usual care\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnlike previous studies, this study took into account the information from the trend arrows and fully considered the magnitude and direction of the patients' blood glucose changes to avoid too high or too low blood glucose. The trend arrows might be one of the reasons why the incidence of hypoglycemia in the App group was smaller than that in the traditional physician group.\u003c/p\u003e\u003cp\u003eIn this study, 29% of the patients had a TIR of more than 70%, which may be related to the subjects having very high blood glucose and undergoing surgery during the study period. In addition, 36% of diabetic patients had cirrhosis, which has a higher risk phase for hyperglycemia\u003csup\u003e28\u003c/sup\u003e. Moreover, studies have shown that diabetic patients with cirrhosis have worse glycemic control compared to diabetic patients without cirrhosis\u003csup\u003e29\u003c/sup\u003e. This study has some shortcomings. First, the length of the study was only maintained for one week due to the fact that the subjects were non-endocrinology inpatients. Second, the HbA1C was not assessed at the end of the study due to the length of the study, which is an important indicator of diabetes management. In summary, among non-endocrinology hospitalized diabetic patients, smart insulin dosing software with trending arrows was shown to be more effective in controlling hyperglycemia without increasing the risk of hypoglycemia compared with traditional physician experience. Additional clinical studies are needed to further support our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis work was funded by the Shanghai Municipality Health Commission (202140085), and the Shanghai Public Health Clinical Center (KY-GW-2023-13).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAuthor contributions contributed to the study conception and design. Material preparation, data collection, and analysis were performed by L.H.Z, C.W.W, C. H. W, and D.D.Q. The first draft of the manuscript was written by L.H.Z. and X.L.Z. All authors commented on previous versions of the manuscript. Xia Yu provided technical support. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe are grateful to Mr. Yunyang Sun and Shanghai Imedpower Tech Ltd. Provided the CGMs data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOng KL, Stafford LK, McLaughlin SA, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eThe Lancet.\u003c/em\u003e 2023;402(10397):203\u0026ndash;234.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia W, Weng J, Zhu D, et al. Standards of medical care for type 2 diabetes in China 2019. \u003cem\u003eDiabetes Metab Res Rev.\u003c/em\u003e 2019;35(6):e3158.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLatta S, Alhosaini MN, Al-Solaiman Y, et al. 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Association Between Continuous Glucose Monitoring-Derived Glycemia Risk Index and Albuminuria in Type 2 Diabetes. \u003cem\u003eDiabetes Technology \u0026amp; Therapeutics.\u003c/em\u003e 2023;25(10):726\u0026ndash;735.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCook CB, Mann LJ, King EC, et al. Management of insulin therapy in urban diabetes patients is facilitated by use of an intelligent dosing system. \u003cem\u003eDiabetes Technol Ther.\u003c/em\u003e 2004;6(3):326\u0026ndash;335.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlonoff DC, Wang J, Rodbard D, et al. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. \u003cem\u003eJournal of Diabetes Science and Technology.\u003c/em\u003e 2022;17(5):1226\u0026ndash;1242.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossetti P, Ampudia-Blasco FJ, Laguna A, et al. 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A Systematic Review Supporting the Endocrine Society Clinical Practice Guideline for the Management of Hyperglycemia in Adults Hospitalized for Noncritical Illness or Undergoing Elective Surgical Procedures. \u003cem\u003eThe Journal of Clinical Endocrinology \u0026amp; Metabolism.\u003c/em\u003e 2022;107(8):2139\u0026ndash;2147.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSecher AL, Pedersen-Bjergaard U, Svendsen OL, et al. Flash glucose monitoring and automated bolus calculation in type 1 diabetes treated with multiple daily insulin injections: a 26 week randomised, controlled, multicentre trial. \u003cem\u003eDiabetologia.\u003c/em\u003e 2021;64(12):2713\u0026ndash;2724.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee WG, Wells CI, McCall JL, Murphy R, Plank LD. Prevalence of diabetes in liver cirrhosis: A systematic review and meta-analysis. \u003cem\u003eDiabetes Metab Res Rev.\u003c/em\u003e 2019;35(6):e3157.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastera L, Cusi K. Diabetes and cirrhosis: Current concepts on diagnosis and management. \u003cem\u003eHepatology.\u003c/em\u003e 2023;77(6):2128\u0026ndash;2146.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"insulin dose adjustment, trend arrows, continuous glucose monitoring, artificial intelligence, time in range","lastPublishedDoi":"10.21203/rs.3.rs-6878924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6878924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eArtificial intelligence and real-time continuous glucose monitoring (rtCGM) are important inventions in the history of diabetes. The aim of this study was to compare the effectiveness of two methods of glycemic control (smart insulin dosing software and traditional physician experience).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eSubjects were randomized 1:1 into a smart insulin group (App group) and a traditional physician group (Control group). The former calculates the insulin dose based on the application (with trend arrows that respond to the magnitude and direction of glucose changes), while the latter calculates the insulin dose based on the physician's experience. All subjects underwent rtCGM and capillary blood glucose (CBG) testing during the study. The duration of the study was 5 to 7 days. We compared the mean blood glucose during the intervention.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBaseline information was matched between the two groups. Patients in the App group had lower mean CBG (mmol/L) before dinner (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and at bedtime (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) compared to the control group. The mean results of rtCGM during the study showed that patients in the App group had lower mean glucose (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), glucose management index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), time in range (64.0% (51.0%, 73.0%) vs 47.0% (29.5%, 67.0%), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and time above of range (35.0% (23.0%, 47.0%) vs 52.0% (29.5.0%, 70.0%), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). There was no difference in the incidence of hypoglycemia between the two groups of patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.394).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSmart insulin dosing software with trend arrows has a better effect on controlling hyperglycemia than traditional physician experience, and does not increase the risk of hypoglycemia.\u003c/p\u003e","manuscriptTitle":"Smart insulin dosing software based on trend arrows is an effective means of controlling hyperglycemia in non-endocrinological inpatients with diabetes: a randomized controlled trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 11:19:43","doi":"10.21203/rs.3.rs-6878924/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-26T11:57:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T15:22:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36590260251986218841680683583000140161","date":"2025-08-06T11:33:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298890855918347885620445250518436164884","date":"2025-08-05T15:28:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T21:02:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178230107058506422382169096850752962059","date":"2025-07-22T06:21:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T12:27:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-14T09:17:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-14T09:15:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2025-06-12T09:32:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4ee90cfe-2926-4518-9761-346395b97bdc","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:08:59+00:00","versionOfRecord":{"articleIdentity":"rs-6878924","link":"https://doi.org/10.1007/s12020-025-04509-z","journal":{"identity":"endocrine","isVorOnly":false,"title":"Endocrine"},"publishedOn":"2026-02-16 15:57:42","publishedOnDateReadable":"February 16th, 2026"},"versionCreatedAt":"2025-07-25 11:19:43","video":"","vorDoi":"10.1007/s12020-025-04509-z","vorDoiUrl":"https://doi.org/10.1007/s12020-025-04509-z","workflowStages":[]},"version":"v1","identity":"rs-6878924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6878924","identity":"rs-6878924","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}