European Journal of Cardiovascular Medicine

Hypoglycaemia remains one of the most significant and persistent challenges in the management of diabetes mellitus, particularly among individuals with type 1 diabetes and those with insulin-treated type 2 diabetes [1]. Episodes of hypoglycaemia, especially when recurrent or severe, are associated with substantial clinical and psychosocial consequences, including impaired quality of life, fear of insulin intensification, reduced adherence to treatment, increased risk of cardiovascular events, cognitive dysfunction, and higher rates of emergency healthcare utilization [2]. Despite advances in glucose-lowering therapies and monitoring technologies, hypoglycaemia continues to represent a major barrier to achieving optimal glycaemic control [3].

The introduction of continuous glucose monitoring (CGM) systems marked a major shift in diabetes care by enabling real-time tracking of interstitial glucose levels and providing trend information beyond what was possible with self-monitoring of blood glucose [4]. Multiple randomized trials and meta-analyses have demonstrated that CGM use improves glycaemic control and reduces hypoglycaemia compared with conventional finger-stick monitoring [5]. However, most conventional CGM systems remain inherently reactive. Alerts are typically triggered only when glucose levels have already crossed predefined hypoglycaemic thresholds, limiting the opportunity for timely preventive action [6]. As a result, hypoglycaemic events may still occur, particularly during sleep, physical activity, or periods of rapid glucose decline.

Recent technological advancements have led to the development of AI-enabled and predictive CGM systems that aim to move beyond reactive monitoring toward anticipatory hypoglycaemia prevention [7]. These systems incorporate predictive algorithms that analyze real-time glucose trends, rates of change, and historical patterns to forecast impending hypoglycaemia minutes before glucose levels fall below critical thresholds [8]. By generating predictive low-glucose alerts or algorithm-driven recommendations, these technologies allow users to intervene earlier through carbohydrate intake, insulin dose adjustment, or behavioral modifications. In some platforms, predictive CGM features are further integrated with automated insulin delivery or hybrid closed-loop systems, enabling partial or fully automated responses to predicted hypoglycaemic risk [9,10].

The clinical promise of AI-predictive CGM lies in its potential to reduce hypoglycaemia burden without compromising overall glycaemic control [11]. Early randomized controlled trials have suggested that predictive alerts and algorithm-based systems may reduce time spent in hypoglycaemia, decrease the frequency of severe hypoglycaemic events, and improve patient confidence in insulin management. However, findings across trials have been inconsistent [12]. Variability exists in study populations, including differences in diabetes type, age groups, baseline hypoglycaemia risk, and insulin delivery methods. Additionally, AI-predictive CGM systems differ substantially in algorithm design, prediction horizons, alert thresholds, and degree of automation, which may influence observed outcomes [13].

Furthermore, the increasing integration of AI-based technologies into routine diabetes care has raised important questions for clinicians, patients, and health systems regarding clinical effectiveness, cost-effectiveness, and appropriate patient selection. While individual trials provide valuable insights, they are often underpowered to detect differences in infrequent but clinically important outcomes such as severe hypoglycaemia. A systematic synthesis of randomized evidence is therefore essential to quantify the overall effect of AI-predictive CGM on hypoglycaemia burden and to determine whether these technologies offer meaningful advantages over standard CGM or usual care.

To date, systematic reviews of CGM have largely focused on traditional real-time or intermittently scanned systems, without specifically isolating the impact of predictive or AI-enabled features [14]. As predictive algorithms represent a distinct technological advance with different mechanisms of action, their effects cannot be assumed to mirror those of conventional CGM [15]. A focused systematic review and meta-analysis of randomized trials evaluating AI-predictive CGM is needed to address this evidence gap.

Accordingly, the present study aims to systematically review and meta-analyze randomized controlled trials assessing the effect of AI-predictive continuous glucose monitoring systems on hypoglycaemia burden in individuals with diabetes. By pooling high-quality randomized evidence, this review seeks to clarify the magnitude and consistency of hypoglycaemia reduction, explore sources of heterogeneity across populations and device types, and evaluate secondary glycaemic and safety outcomes. The findings are intended to inform clinical decision-making, guide guideline development, and support evidence-based adoption of AI-enabled predictive CGM technologies in contemporary diabetes management.

Literature Search A comprehensive literature search was conducted to identify randomized controlled trials evaluating AI-predictive continuous glucose monitoring and hypoglycaemia outcomes. Electronic databases including PubMed/MEDLINE, Embase, Cochrane CENTRAL, and Web of Science were searched from inception to the date of final search. Additional records were identified through ClinicalTrials.gov and manual screening of reference lists of relevant studies. Search terms combined concepts related to continuous glucose monitoring, artificial intelligence or predictive algorithms, hypoglycaemia, and randomized trials. No language or publication status restrictions were applied. The search strategy was developed a priori and aligned with PRISMA guidelines [16]. This systematic review and meta-analysis were prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420251270444). Screening and Selection of Studies Titles and abstracts retrieved from the literature search were independently screened by two reviewers to identify potentially eligible studies. Full texts of relevant articles were then assessed for inclusion based on predefined eligibility criteria. Discrepancies at any stage were resolved through discussion and consensus, with involvement of a third reviewer when necessary. Reasons for exclusion at the full-text stage were documented. The study selection process was conducted in accordance with PRISMA guidelines and is presented using a PRISMA flow diagram. Data Extraction Data were independently extracted by two reviewers using a standardized, pre-piloted data extraction form. Extracted information included study characteristics (author, year, country, design, sample size, and follow-up duration), participant demographics and clinical characteristics, details of the AI-predictive continuous glucose monitoring intervention and comparator, and outcome data related to hypoglycaemia burden and glycaemic control. Primary outcome data included time spent in hypoglycaemia (<70 mg/dL and <54 mg/dL), while secondary outcomes included hypoglycaemic event frequency, severe hypoglycaemia, HbA1c, mean glucose, and adverse events. Any discrepancies in data extraction were resolved by consensus or consultation with a third reviewer. Statistical Analysis Statistical analyses will be performed using Stata version 18.0 (StataCorp, College Station, TX, USA). For continuous outcomes, pooled effect estimates will be calculated as mean differences (MDs) or standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs). For dichotomous outcomes, risk ratios (RRs) with 95% CIs will be used. A random-effects model using the restricted maximum likelihood (REML) method will be applied to account for between-study heterogeneity. Statistical heterogeneity will be assessed using Cochran’s Q test and quantified with the I² statistic. Prespecified subgroup and sensitivity analyses will be conducted to explore sources of heterogeneity, and publication bias will be evaluated using funnel plots and Egger’s test where appropriate. Risk of Bias Assessment The risk of bias of included randomized controlled trials will be independently assessed by two reviewers using the Cochrane Risk of Bias tool for randomized trials (RoB 2.0) [17]. Bias will be evaluated across five domains: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions, (3) bias due to missing outcome data, (4) bias in measurement of the outcome, and (5) bias in selection of the reported result. Each domain will be judged as low risk, some concerns, or high risk of bias, leading to an overall risk-of-bias judgment for each study. Discrepancies will be resolved by consensus or by consultation with a third reviewer.

Demographic and Baseline Characteristics

A total of 11 [18-28] randomized controlled studies were included in this systematic review and meta-analysis, comprising an overall population of 1,164 participants. Of these, 568 were male and 596 were female, reflecting a balanced sex distribution across the included trials. The pooled mean follow-up duration was 12.49 months, allowing assessment of both short- and intermediate-term outcomes. The mean age of participants was 37 years, with most studies enrolling predominantly adults, although some included younger populations. The majority of participants had type 1 diabetes mellitus, with one study including individuals with insulin-treated type 2 diabetes. Baseline glycaemic control was moderate, with a mean HbA1c of 6.71%, and the mean duration of diabetes was 13.52 years, indicating a largely experienced insulin-treated population at established risk for hypoglycaemia.

Effect of AI-Predictive Continuous Glucose Monitoring on Hypoglycaemia Burden

Figure 2 illustrates the pooled effect of AI-predictive continuous glucose monitoring (CGM) on hypoglycaemia burden across 11 randomized controlled trials, analyzed using a random-effects REML model. Individual study estimates are represented by squares proportional to their statistical weight, with horizontal lines indicating 95% confidence intervals (CIs). Considerable variability is observed across studies, reflecting differences in study populations, CGM devices, and follow-up durations. Overall, Figure 2 demonstrates a statistically significant reduction in hypoglycaemia burden with AI-predictive CGM, with a pooled effect size of 34.63 (95% CI: 20.84–48.41) favoring the intervention. Substantial between-study heterogeneity was present (τ² = 542.52; I² = 99.91%), confirmed by a significant Cochran Q test (Q(10) = 8254.80, p < 0.001). Despite this heterogeneity, the overall effect remained significant (z = 4.92, p < 0.001), supporting the clinical benefit of AI-predictive CGM in reducing hypoglycaemia burden.

Figure 3. Effect of AI-Predictive Continuous Glucose Monitoring on Hypoglycaemia Burden

Figure 3 depicts the pooled effect of AI-predictive continuous glucose monitoring (CGM) on hypoglycaemia burden across 11 randomized controlled trials, analyzed using a random-effects REML model. Each study is represented by a square proportional to its statistical weight, with horizontal lines indicating the corresponding 95% confidence intervals (CIs). The individual effect estimates demonstrate variability in magnitude across studies, reflecting differences in study populations, intervention characteristics, and follow-up durations. The overall pooled estimate shows a statistically significant reduction in hypoglycaemia burden with AI-predictive CGM, with a summary effect size of 21.90 (95% CI: 9.54–34.26) favoring the intervention. Substantial heterogeneity was observed among the included trials (τ² = 435.43; I² = 99.89%), and the Cochran Q test confirmed significant between-study variability (Q(10) = 1864.31, p < 0.001). Despite this high heterogeneity, the overall effect remained statistically significant (z = 3.47, p < 0.001), indicating a consistent direction of benefit across studies.

Effect of AI-Predictive Continuous Glucose Monitoring on Hypoglycaemia Burden

Figure 4 shows the pooled effect of AI-predictive continuous glucose monitoring (CGM) on hypoglycaemia burden across 11 randomized controlled trials, analyzed using a random-effects REML model. Individual study estimates are represented by blue squares, with square size proportional to study weight and horizontal lines indicating 95% confidence intervals (CIs). The magnitude of effect varies across studies, reflecting heterogeneity in study populations, intervention characteristics, and follow-up duration.The overall pooled analysis demonstrates a statistically significant reduction in hypoglycaemia burden with AI-predictive CGM, with a summary effect size of 29.13 (95% CI: 17.22–41.03) favoring the intervention. Substantial between-study heterogeneity was observed (τ² = 404.13; I² = 99.89%), confirmed by a significant Cochran Q test (Q(10) = 1593.85, p < 0.001). Despite this heterogeneity, the overall effect remained statistically significant (z = 4.79, p < 0.001), indicating a consistent direction of benefit across included trials.

Effect of AI-Predictive Continuous Glucose Monitoring on Severe Hypoglycaemia Events

Figure 5 summarizes the pooled effect of AI-predictive continuous glucose monitoring (CGM) on severe hypoglycaemia events across 11 randomized controlled trials, analyzed using a random-effects REML model. Individual studies are represented by blue squares proportional to their statistical weight, with horizontal lines indicating 95% confidence intervals (CIs). The pooled estimate demonstrates a statistically significant reduction in severe hypoglycaemia, with an overall effect size of 0.70 (95% CI: 0.17–1.23) favoring AI-predictive CGM. However, there was extreme between-study heterogeneity (τ² = 0.80; I² = 99.99%), confirmed by a significant Cochran Q test (Q(10) = 585.23, p < 0.001), reflecting substantial variability in event definitions, baseline risk, and intervention characteristics across trials. Despite this heterogeneity, the overall effect remained statistically significant (z = 2.57, p = 0.01), indicating a consistent direction of benefit with AI-predictive CGM in reducing severe hypoglycaemia events.

Effect of AI-Predictive Continuous Glucose Monitoring on Time in Range

Figure 6 illustrates the pooled effect of AI-predictive continuous glucose monitoring (CGM) on time in range (70–180 mg/dL) across 10 randomized controlled trials, analyzed using a random-effects REML model. Individual study estimates are represented by blue squares, with square size proportional to their statistical weight and horizontal lines indicating 95% confidence intervals (CIs). The pooled analysis demonstrates a significant improvement in time in range with AI-predictive CGM, with an overall effect size of 1.35 (95% CI: 1.22–1.48) favoring the intervention. Moderate between-study heterogeneity was observed (τ² = 0.03; I² = 67.16%), with a significant Cochran Q test (Q(9) = 27.71, p < 0.001). Despite this heterogeneity, the direction and magnitude of effect were consistent across studies, and the overall effect was highly statistically significant (z = 20.21, p < 0.001), supporting the benefit of AI-predictive CGM in improving glycaemic time in range.

Effect of AI-Predictive Continuous Glucose Monitoring on HbA1c

Figure 7 presents the pooled effect of AI-predictive continuous glucose monitoring (CGM) on glycaemic control as measured by HbA1c across 10 randomized controlled trials, using a random-effects REML model. Individual studies are depicted by blue squares proportional to their statistical weight, with horizontal lines indicating 95% confidence intervals (CIs).

The pooled analysis demonstrates a significant improvement in HbA1c favoring AI-predictive CGM, with an overall effect size of 1.30 (95% CI: 1.15–1.46). Moderate to substantial heterogeneity was observed across studies (τ² = 0.05; I² = 74.70%), confirmed by a significant Cochran Q test (Q(9) = 36.87, p < 0.001). Despite this heterogeneity, the direction of effect was consistently favorable, and the overall effect was highly statistically significant (z = 16.15, p < 0.001), indicating that AI-predictive CGM is associated with meaningful improvements in long-term glycaemic control.

Effect of AI-Predictive Continuous Glucose Monitoring on Mean Glucose Levels

Figure 8 illustrates the pooled effect of AI-predictive continuous glucose monitoring (CGM) on mean glucose levels across 11 randomized controlled trials, analyzed using a random-effects REML model. Individual study estimates are shown as blue squares proportional to their statistical weight, with horizontal lines representing 95% confidence intervals (CIs).

The pooled analysis demonstrates a significant reduction in mean glucose levels associated with AI-predictive CGM, with an overall effect size of −1.05 (95% CI: −1.38 to −0.71), favoring the intervention. Moderate to substantial heterogeneity was observed among studies (τ² = 0.19; I² = 76.41%), confirmed by a significant Cochran Q test (Q(10) = 30.56, p < 0.001). Despite this heterogeneity, the direction of effect was consistently toward lower mean glucose levels, and the overall effect was highly statistically significant (z = −6.09, p < 0.001), supporting the role of AI-predictive CGM in improving overall glycaemic exposure.

Effect of AI-Predictive Continuous Glucose Monitoring on CGM Wear Duration

Figure 9 presents the pooled effect of AI-predictive continuous glucose monitoring (CGM) on CGM wear duration across 10 randomized controlled trials, analyzed using a random-effects REML model. Individual study estimates are shown as blue squares proportional to their statistical weight, with horizontal lines representing 95% confidence intervals (CIs).

The pooled analysis did not demonstrate a statistically significant difference in CGM wear duration between AI-predictive CGM and comparator groups, with an overall effect estimate of 174.36 (95% CI: −27.45 to 376.16). Substantial heterogeneity was observed across studies (τ² = 104,687.76; I² = 100%), indicating marked variability in reported wear-time metrics and study designs. The Cochran Q test confirmed significant heterogeneity (Q(9) = 643.57, p < 0.001). The overall effect did not reach statistical significance (z = 1.69, p = 0.09), suggesting that AI-predictive features may not consistently influence CGM adherence or wear duration across diverse clinical settings.

In this systematic review and meta-analysis of randomized controlled trials, we evaluated the impact of AI-predictive continuous glucose monitoring (CGM) on hypoglycaemia burden and broader glycaemic outcomes. Across 11 trials including 1,164 participants, AI-predictive CGM was associated with a significant reduction in hypoglycaemia burden, accompanied by improvements in time in range, HbA1c, and mean glucose levels, without a consistent effect on CGM wear duration [29]. Collectively, these findings suggest that predictive and algorithm-driven CGM technologies offer clinically meaningful advantages beyond conventional, reactive CGM systems [30].

The primary finding of reduced hypoglycaemia burden is particularly important, as hypoglycaemia remains a major barrier to optimal insulin titration and long-term glycaemic control [31]. Predictive alerts and AI-enabled algorithms allow users to intervene before glucose levels cross hypoglycaemic thresholds, thereby addressing a key limitation of standard CGM [32]. Although substantial heterogeneity was observed across studies, the direction of effect consistently favored AI-predictive CGM, supporting the robustness of this benefit across diverse populations and device platforms [33].

Improvements in secondary glycaemic outcomes further reinforce the clinical value of AI-predictive CGM [34]. The observed increase in time in range and reduction in mean glucose suggest that hypoglycaemia reduction was not achieved at the expense of overall glycaemic control [35]. Instead, these technologies appear to facilitate safer glucose optimization, likely by reducing glycaemic variability and fear-driven insulin underdosing [36]. The modest but significant improvement in HbA1c aligns with this interpretation and supports the role of predictive CGM in long-term diabetes management.

When compared with prior meta-analyses of CGM, our findings extend and refine existing evidence. Earlier meta-analyses evaluating traditional real-time or intermittently scanned CGM demonstrated reductions in hypoglycaemia and modest HbA1c improvements compared with self-monitoring of blood glucose, but they did not distinguish between reactive and predictive technologies [36, 37]. More recent reviews of hybrid closed-loop or automated insulin delivery systems reported substantial reductions in hypoglycaemia and improved time in range; however, these analyses often conflated insulin automation effects with CGM-specific predictive features [38]. By focusing specifically on AI-predictive CGM, including systems with predictive alerts independent of full closed-loop insulin delivery, our meta-analysis isolates the contribution of predictive intelligence itself. This distinction is clinically relevant, as many patients use predictive CGM without fully automated insulin delivery.

The high degree of heterogeneity observed across outcomes reflects differences in study design, diabetes type, baseline hypoglycaemia risk, follow-up duration, and algorithm sophistication. In particular, trials enrolling high-risk populations or using more advanced predictive or machine-learning–based algorithms tended to report larger effects [39]. These findings highlight the importance of patient selection and device characteristics in maximizing benefit and may explain variability across individual trials and prior meta-analyses.

Several limitations should be acknowledged. The number of included trials remains modest, and reporting of hypoglycaemia metrics was not uniform. Additionally, most studies enrolled individuals with type 1 diabetes, limiting generalizability to insulin-treated type 2 diabetes. Finally, long-term clinical outcomes such as cardiovascular events and quality of life were inconsistently reported.

this meta-analysis demonstrates that AI-predictive CGM significantly reduces hypoglycaemia burden while improving overall glycaemic control. Compared with prior CGM meta-analyses, these findings underscore the added value of predictive intelligence beyond standard glucose monitoring. Future trials should focus on long-term outcomes, real-world effectiveness, and identification of patient subgroups most likely to benefit from AI-driven predictive technologies. Conflict of Interest The authors certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript. Funding The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work. Authors’ contributions. All authors contributed equally to the manuscript and read and approved the final version of the manuscript. Acknowledgement This paper is the collaborative work of all authors under the mentorship for the research work from BIR (Biomedical and International Research). We all authors acknowledge this mentorship for this meta-analysis.

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27. Zaharieva DP, Turksoy K, McGaugh SM, Pooni R, Vienneau T, Ly T, Riddell MC. Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes. Diabetes Technol Ther. 2019 Jun;21(6):313-321. doi: 10.1089/dia.2018.0364. Epub 2019 May 6. PMID: 31059282; PMCID: PMC6551983.

28. Habteab A, Castañeda J, de Valk H, Choudhary P, Bosi E, Lablanche S, de Portu S, Da Silva J, Vorrink-de Groot L, Shin J, Cohen O. Predicting Factors Associated with Hypoglycemia Reduction with Automated Predictive Insulin Suspension in Patients at High Risk of Severe Hypoglycemia: An Analysis from the SMILE Randomized Trial. Diabetes Technol Ther. 2020 Sep;22(9):681-685. doi: 10.1089/dia.2019.0495. PMID: 32412858; PMCID: PMC7478192.

29. Perlman JE, Gooley TA, McNulty B, Meyers J, Hirsch IB. HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis. Diabetes Technol Ther. 2021 Apr;23(4):253-258. doi: 10.1089/dia.2020.0501. Epub 2020 Dec 1. PMID: 33253015; PMCID: PMC8255314.

30. Rodbard D. Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes. Diabetes Technol Ther. 2017 Jun;19(S3):S25-S37. doi: 10.1089/dia.2017.0035. PMID: 28585879; PMCID: PMC5467105.

31. Berry SA, Goodman I, Heller S, Iqbal A. The impact of technology on impaired awareness of hypoglycaemia in type 1 diabetes. Ther Adv Endocrinol Metab. 2025 Jun 12;16:20420188251346260. doi: 10.1177/20420188251346260. PMID: 40520684; PMCID: PMC12165273.

32. Medanki S, Dommati N, Bodapati HH, Katru VNSK, Moses G, Komaraju A, Donepudi NS, Yalamanchili D, Sateesh J, Turimerla P. Artificial intelligence powered glucose monitoring and controlling system: Pumping module. World J Exp Med. 2024 Mar 20;14(1):87916. doi: 10.5493/wjem.v14.i1.87916. PMID: 38590308; PMCID: PMC10999070.

33. Deng M, Yang R, Zheng X, Deng Y, Jiang J. Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges. Front Endocrinol (Lausanne). 2025 Nov 19;16:1620132. doi: 10.3389/fendo.2025.1620132. PMID: 41347125; PMCID: PMC12672236.

34. Yuan L, Wang Y, Xing M, Liu T, Xiang D. Global research trends in AI-assisted blood glucose management: a bibliometric study. Front Endocrinol (Lausanne). 2025 May 28;16:1579640. doi: 10.3389/fendo.2025.1579640. PMID: 40502401; PMCID: PMC12151842.

35. Ólafsdóttir AF, Sveen KA, Wijkman M, Hallström S, Nilsson PH, Sterner Isaksson S, Holmer H, Ekström M, Imberg H, Lind M. Systematic intensive therapy in addition to continuous glucose monitoring in adults with type 1 diabetes: a multicentre, open-label, randomised controlled trial. Lancet Reg Health Eur. 2025 Oct 16;59:101485. doi: 10.1016/j.lanepe.2025.101485. PMID: 41142656; PMCID: PMC12553072.

36. Nathanson D, Eeg-Olofsson K, Spelman T, Bülow E, Kyhlstedt M, Levrat-Guillen F, Bolinder J. Intermittently scanned continuous glucose monitoring compared with blood glucose monitoring is associated with lower HbA1c and a reduced risk of hospitalisation for diabetes-related complications in adults with type 2 diabetes on insulin therapies. Diabetologia. 2025 Jan;68(1):41-51. doi: 10.1007/s00125-024-06289-z. Epub 2024 Oct 26. PMID: 39460755; PMCID: PMC11663194.

37. Ali Badawi SA, Alhajri AHM, Osman Altayb NM, Mohmmed E, Elamin Hassabelrasoul RK, Sidahmed Mohammed SA, Altahir Mohammed WH, El Ghali JA. Efficacy of Real-Time Continuous Glucose Monitoring in Improving Glycemic Outcomes Among Adults With Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials. Cureus. 2025 Aug 7;17(8):e89534. doi: 10.7759/cureus.89534. PMID: 40918786; PMCID: PMC12413689.

38. Ware J, Hovorka R. Recent advances in closed-loop insulin delivery. Metabolism. 2022 Feb;127:154953. doi: 10.1016/j.metabol.2021.154953. Epub 2021 Dec 7. PMID: 34890648; PMCID: PMC8792215.

39. Heller S, Lingvay I, Marso SP, Philis-Tsimikas A, Pieber TR, Poulter NR, Pratley RE, Hachmann-Nielsen E, Kvist K, Lange M, Moses AC, Trock Andresen M, Buse JB; DEVOTE Study Group. Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE. Diabetes Obes Metab. 2020 Dec;22(12):2248-2256. doi: 10.1111/dom.14208. PMID: 32996693; PMCID: PMC7756403.

1. Nakhleh A, Shehadeh N. Hypoglycemia in diabetes: An update on pathophysiology, treatment, and prevention. World J Diabetes. 2021 Dec 15;12(12):2036-2049. doi: 10.4239/wjd.v12.i12.2036. PMID: 35047118; PMCID: PMC8696639.

2. Holt RIG, DeVries JH, Hess-Fischl A, Hirsch IB, Kirkman MS, Klupa T, Ludwig B, Nørgaard K, Pettus J, Renard E, Skyler JS, Snoek FJ, Weinstock RS, Peters AL. The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2021 Dec;64(12):2609-2652. doi: 10.1007/s00125-021-05568-3. Erratum in: Diabetologia. 2022 Jan;65(1):255. doi: 10.1007/s00125-021-05600-6. PMID: 34590174; PMCID: PMC8481000.

3. Adolfsson P, Rentoul D, Klinkenbijl B, Parkin CG. Hypoglycaemia Remains the Key Obstacle to Optimal Glycaemic Control - Continuous Glucose Monitoring is the Solution. Eur Endocrinol. 2018 Sep;14(2):50-56. doi: 10.17925/EE.2018.14.2.50. Epub 2018 Sep 10. PMID: 30349594; PMCID: PMC6182923.

4. Cappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications. Diabetes Metab J. 2019 Aug;43(4):383-397. doi: 10.4093/dmj.2019.0121. PMID: 31441246; PMCID: PMC6712232.

5. Martens T, Beck RW, Bailey R, Ruedy KJ, Calhoun P, Peters AL, Pop-Busui R, Philis-Tsimikas A, Bao S, Umpierrez G, Davis G, Kruger D, Bhargava A, Young L, McGill JB, Aleppo G, Nguyen QT, Orozco I, Biggs W, Lucas KJ, Polonsky WH, Buse JB, Price D, Bergenstal RM; MOBILE Study Group. Effect of Continuous Glucose Monitoring on Glycemic Control in Patients With Type 2 Diabetes Treated With Basal Insulin: A Randomized Clinical Trial. JAMA. 2021 Jun 8;325(22):2262-2272. doi: 10.1001/jama.2021.7444. PMID: 34077499; PMCID: PMC8173473.

6. Di Molfetta S, Rossi A, Boscari F, Irace C, Laviola L, Bruttomesso D. Criteria for Personalised Choice of a Continuous Glucose Monitoring System: An Expert Opinion. Diabetes Ther. 2024 Nov;15(11):2263-2278. doi: 10.1007/s13300-024-01654-y. Epub 2024 Sep 30. Erratum in: Diabetes Ther. 2025 Jan;16(1):135. doi: 10.1007/s13300-024-01666-8. PMID: 39347900; PMCID: PMC11467157.

7. Ji C, Jiang T, Liu L, Zhang J, You L. Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management. Front Endocrinol (Lausanne). 2025 May 26;16:1571362. doi: 10.3389/fendo.2025.1571362. PMID: 40491592; PMCID: PMC12146165.

8. Dassau E, Cameron F, Lee H, Bequette BW, Zisser H, Jovanovic L, Chase HP, Wilson DM, Buckingham BA, Doyle FJ 3rd. Real-Time hypoglycemia prediction suite using continuous glucose monitoring: a safety net for the artificial pancreas. Diabetes Care. 2010 Jun;33(6):1249-54. doi: 10.2337/dc09-1487. PMID: 20508231; PMCID: PMC2875433.

9. Ware J, Hovorka R. Closed-loop insulin delivery: update on the state of the field and emerging technologies. Expert Rev Med Devices. 2022 Nov;19(11):859-875. doi: 10.1080/17434440.2022.2142556. Epub 2022 Nov 4. PMID: 36331211; PMCID: PMC9780196.

10. Fuchs J, Hovorka R. Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy. Expert Rev Med Devices. 2020 Jul;17(7):707-720. doi: 10.1080/17434440.2020.1784724. Epub 2020 Jul 3. PMID: 32569476; PMCID: PMC7441745.

11. Ying Z, Li X, Chen Y. Artificial intelligence in glycemic management for diabetes: Applications, opportunities and challenges. J Transl Int Med. 2025 Aug 12;13(4):314-317. doi: 10.1515/jtim-2025-0039. PMID: 40861074; PMCID: PMC12371392.

12. Dave D, Erraguntla M, Lawley M, DeSalvo D, Haridas B, McKay S, Koh C. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes. 2021 Apr 29;6(2):e26909. doi: 10.2196/26909. PMID: 33913816; PMCID: PMC8120423.

13. Fraser RA, Walker RJ, Campbell JA, Ekwunife O, Egede LE. Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes. NPJ Digit Med. 2025 Nov 18;8(1):687. doi: 10.1038/s41746-025-02036-9. PMID: 41254217; PMCID: PMC12627454.

14. Alfadli SF, Alotaibi YS, Aqdi MJ, Almozan LA, Alzubaidi ZB, Altemani HA, Almutairi SD, Alabdullah HA, Almehmadi AA, Alanzi AL, Azzam AY. Effectiveness of continuous glucose monitoring systems on glycemic control in adults with type 1 diabetes: A systematic review and meta-analysis. Metabol Open. 2025 Jul 29;27:100382. doi: 10.1016/j.metop.2025.100382. Erratum in: Metabol Open. 2025 Aug 05;27:100383. doi: 10.1016/j.metop.2025.100383. PMID: 40791933; PMCID: PMC12337206.

15. Vaddiraju S, Burgess DJ, Tomazos I, Jain FC, Papadimitrakopoulos F. Technologies for continuous glucose monitoring: current problems and future promises. J Diabetes Sci Technol. 2010 Nov 1;4(6):1540-62. doi: 10.1177/193229681000400632. PMID: 21129353; PMCID: PMC3005068.

16. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 Mar 29;372:n71. doi: 10.1136/bmj.n71. PMID: 33782057; PMCID: PMC8005924.

17. Nejadghaderi SA, Balibegloo M, Rezaei N. The Cochrane risk of bias assessment tool 2 (RoB 2) versus the original RoB: A perspective on the pros and cons. Health Sci Rep. 2024 Jun 3;7(6):e2165. doi: 10.1002/hsr2.2165. PMID: 38835932; PMCID: PMC11147813.

18. Walker TC, Yucha CB. Continuous glucose monitors: use of waveform versus glycemic values in the improvements of glucose control, quality of life, and fear of hypoglycemia. J Diabetes Sci Technol. 2014 May;8(3):488-93. doi: 10.1177/1932296814528434. Epub 2014 Mar 22. PMID: 24876611; PMCID: PMC4455439.

19. Calhoun PM, Buckingham BA, Maahs DM, Hramiak I, Wilson DM, Aye T, Clinton P, Chase P, Messer L, Kollman C, Beck RW, Lum J; In Home Closed Loop Study Group. Efficacy of an Overnight Predictive Low-Glucose Suspend System in Relation to Hypoglycemia Risk Factors in Youth and Adults With Type 1 Diabetes. J Diabetes Sci Technol. 2016 Nov 1;10(6):1216-1221. doi: 10.1177/1932296816645119. Erratum in: J Diabetes Sci Technol. 2017 Mar;11(2):NP1. doi: 10.1177/1932296817692062. PMID: 27207890; PMCID: PMC5094319.

20. Avari P, Moscardo V, Jugnee N, Oliver N, Reddy M. Glycemic Variability and Hypoglycemic Excursions With Continuous Glucose Monitoring Compared to Intermittently Scanned Continuous Glucose Monitoring in Adults With Highest Risk Type 1 Diabetes. J Diabetes Sci Technol. 2020 May;14(3):567-574. doi: 10.1177/1932296819867688. Epub 2019 Aug 2. PMID: 31375042; PMCID: PMC7576953.

21. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; Fiallo-Scharer R, Cheng J, Beck RW, Buckingham BA, Chase HP, Kollman C, Laffel L, Lawrence JM, Mauras N, Tamborlane WV, Wilson DM, Wolpert H. Factors predictive of severe hypoglycemia in type 1 diabetes: analysis from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized control trial dataset. Diabetes Care. 2011 Mar;34(3):586-90. doi: 10.2337/dc10-1111. Epub 2011 Jan 25. PMID: 21266651; PMCID: PMC3041185.

22. Brown SA, Beck RW, Raghinaru D, Buckingham BA, Laffel LM, Wadwa RP, Kudva YC, Levy CJ, Pinsker JE, Dassau E, Doyle FJ 3rd, Ambler-Osborn L, Anderson SM, Church MM, Ekhlaspour L, Forlenza GP, Levister C, Simha V, Breton MD, Kollman C, Lum JW, Kovatchev BP; iDCL Trial Research Group. Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial. Diabetes Care. 2020 Aug;43(8):1822-1828. doi: 10.2337/dc20-0124. Epub 2020 May 29. PMID: 32471910; PMCID: PMC7372060.

23. Lingvay I, Buse JB, Franek E, Hansen MV, Koefoed MM, Mathieu C, Pettus J, Stachlewska K, Rosenstock J. A Randomized, Open-Label Comparison of Once-Weekly Insulin Icodec Titration Strategies Versus Once-Daily Insulin Glargine U100. Diabetes Care. 2021 Jul;44(7):1595-1603. doi: 10.2337/dc20-2878. Epub 2021 Apr 19. PMID: 33875484; PMCID: PMC8323172.

24. Mosquera-Lopez C, Roquemen-Echeverri V, Jacobs PG, Ramsey K, Pinsonault J, Eom J, Ling H, Chen D, Branigan D, Castle JR, Hood KK, Gillingham MB, Aby-Daniel D, Wilson LM. Evaluation of a Prediction-Based Bedtime Intervention in Reducing Nocturnal Low Glucose in Adults With Type 1 Diabetes: The DailyDose Bedtime Smart Snack Crossover Study. Diabetes Care. 2025 Oct 1;48(10):1766-1773. doi: 10.2337/dc25-0407. PMID: 40705054; PMCID: PMC12451843.

25. Buckingham BA, Cameron F, Calhoun P, Maahs DM, Wilson DM, Chase HP, Bequette BW, Lum J, Sibayan J, Beck RW, Kollman C. Outpatient safety assessment of an in-home predictive low-glucose suspend system with type 1 diabetes subjects at elevated risk of nocturnal hypoglycemia. Diabetes Technol Ther. 2013 Aug;15(8):622-7. doi: 10.1089/dia.2013.0040. Epub 2013 Jul 24. PMID: 23883408; PMCID: PMC3746249.

26. Zaharieva D, Yavelberg L, Jamnik V, Cinar A, Turksoy K, Riddell MC. The Effects of Basal Insulin Suspension at the Start of Exercise on Blood Glucose Levels During Continuous Versus Circuit-Based Exercise in Individuals with Type 1 Diabetes on Continuous Subcutaneous Insulin Infusion. Diabetes Technol Ther. 2017 Jun;19(6):370-378. doi: 10.1089/dia.2017.0010. PMID: 28613947; PMCID: PMC5510047.

27. Zaharieva DP, Turksoy K, McGaugh SM, Pooni R, Vienneau T, Ly T, Riddell MC. Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes. Diabetes Technol Ther. 2019 Jun;21(6):313-321. doi: 10.1089/dia.2018.0364. Epub 2019 May 6. PMID: 31059282; PMCID: PMC6551983.

28. Habteab A, Castañeda J, de Valk H, Choudhary P, Bosi E, Lablanche S, de Portu S, Da Silva J, Vorrink-de Groot L, Shin J, Cohen O. Predicting Factors Associated with Hypoglycemia Reduction with Automated Predictive Insulin Suspension in Patients at High Risk of Severe Hypoglycemia: An Analysis from the SMILE Randomized Trial. Diabetes Technol Ther. 2020 Sep;22(9):681-685. doi: 10.1089/dia.2019.0495. PMID: 32412858; PMCID: PMC7478192.

29. Perlman JE, Gooley TA, McNulty B, Meyers J, Hirsch IB. HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis. Diabetes Technol Ther. 2021 Apr;23(4):253-258. doi: 10.1089/dia.2020.0501. Epub 2020 Dec 1. PMID: 33253015; PMCID: PMC8255314.

30. Rodbard D. Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes. Diabetes Technol Ther. 2017 Jun;19(S3):S25-S37. doi: 10.1089/dia.2017.0035. PMID: 28585879; PMCID: PMC5467105.

31. Berry SA, Goodman I, Heller S, Iqbal A. The impact of technology on impaired awareness of hypoglycaemia in type 1 diabetes. Ther Adv Endocrinol Metab. 2025 Jun 12;16:20420188251346260. doi: 10.1177/20420188251346260. PMID: 40520684; PMCID: PMC12165273.

32. Medanki S, Dommati N, Bodapati HH, Katru VNSK, Moses G, Komaraju A, Donepudi NS, Yalamanchili D, Sateesh J, Turimerla P. Artificial intelligence powered glucose monitoring and controlling system: Pumping module. World J Exp Med. 2024 Mar 20;14(1):87916. doi: 10.5493/wjem.v14.i1.87916. PMID: 38590308; PMCID: PMC10999070.

33. Deng M, Yang R, Zheng X, Deng Y, Jiang J. Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges. Front Endocrinol (Lausanne). 2025 Nov 19;16:1620132. doi: 10.3389/fendo.2025.1620132. PMID: 41347125; PMCID: PMC12672236.

34. Yuan L, Wang Y, Xing M, Liu T, Xiang D. Global research trends in AI-assisted blood glucose management: a bibliometric study. Front Endocrinol (Lausanne). 2025 May 28;16:1579640. doi: 10.3389/fendo.2025.1579640. PMID: 40502401; PMCID: PMC12151842.

35. Ólafsdóttir AF, Sveen KA, Wijkman M, Hallström S, Nilsson PH, Sterner Isaksson S, Holmer H, Ekström M, Imberg H, Lind M. Systematic intensive therapy in addition to continuous glucose monitoring in adults with type 1 diabetes: a multicentre, open-label, randomised controlled trial. Lancet Reg Health Eur. 2025 Oct 16;59:101485. doi: 10.1016/j.lanepe.2025.101485. PMID: 41142656; PMCID: PMC12553072.

36. Nathanson D, Eeg-Olofsson K, Spelman T, Bülow E, Kyhlstedt M, Levrat-Guillen F, Bolinder J. Intermittently scanned continuous glucose monitoring compared with blood glucose monitoring is associated with lower HbA1c and a reduced risk of hospitalisation for diabetes-related complications in adults with type 2 diabetes on insulin therapies. Diabetologia. 2025 Jan;68(1):41-51. doi: 10.1007/s00125-024-06289-z. Epub 2024 Oct 26. PMID: 39460755; PMCID: PMC11663194.

37. Ali Badawi SA, Alhajri AHM, Osman Altayb NM, Mohmmed E, Elamin Hassabelrasoul RK, Sidahmed Mohammed SA, Altahir Mohammed WH, El Ghali JA. Efficacy of Real-Time Continuous Glucose Monitoring in Improving Glycemic Outcomes Among Adults With Type 2 Diabetes: A Systematic Review of Randomized Controlled Trials. Cureus. 2025 Aug 7;17(8):e89534. doi: 10.7759/cureus.89534. PMID: 40918786; PMCID: PMC12413689.

38. Ware J, Hovorka R. Recent advances in closed-loop insulin delivery. Metabolism. 2022 Feb;127:154953. doi: 10.1016/j.metabol.2021.154953. Epub 2021 Dec 7. PMID: 34890648; PMCID: PMC8792215.

39. Heller S, Lingvay I, Marso SP, Philis-Tsimikas A, Pieber TR, Poulter NR, Pratley RE, Hachmann-Nielsen E, Kvist K, Lange M, Moses AC, Trock Andresen M, Buse JB; DEVOTE Study Group. Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE. Diabetes Obes Metab. 2020 Dec;22(12):2248-2256. doi: 10.1111/dom.14208. PMID: 32996693; PMCID: PMC7756403.

1. Nakhleh A, Shehadeh N. Hypoglycemia in diabetes: An update on pathophysiology, treatment, and prevention. World J Diabetes. 2021 Dec 15;12(12):2036-2049. doi: 10.4239/wjd.v12.i12.2036. PMID: 35047118; PMCID: PMC8696639.

2. Holt RIG, DeVries JH, Hess-Fischl A, Hirsch IB, Kirkman MS, Klupa T, Ludwig B, Nørgaard K, Pettus J, Renard E, Skyler JS, Snoek FJ, Weinstock RS, Peters AL. The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2021 Dec;64(12):2609-2652. doi: 10.1007/s00125-021-05568-3. Erratum in: Diabetologia. 2022 Jan;65(1):255. doi: 10.1007/s00125-021-05600-6. PMID: 34590174; PMCID: PMC8481000.

3. Adolfsson P, Rentoul D, Klinkenbijl B, Parkin CG. Hypoglycaemia Remains the Key Obstacle to Optimal Glycaemic Control - Continuous Glucose Monitoring is the Solution. Eur Endocrinol. 2018 Sep;14(2):50-56. doi: 10.17925/EE.2018.14.2.50. Epub 2018 Sep 10. PMID: 30349594; PMCID: PMC6182923.

4. Cappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications. Diabetes Metab J. 2019 Aug;43(4):383-397. doi: 10.4093/dmj.2019.0121. PMID: 31441246; PMCID: PMC6712232.

5. Martens T, Beck RW, Bailey R, Ruedy KJ, Calhoun P, Peters AL, Pop-Busui R, Philis-Tsimikas A, Bao S, Umpierrez G, Davis G, Kruger D, Bhargava A, Young L, McGill JB, Aleppo G, Nguyen QT, Orozco I, Biggs W, Lucas KJ, Polonsky WH, Buse JB, Price D, Bergenstal RM; MOBILE Study Group. Effect of Continuous Glucose Monitoring on Glycemic Control in Patients With Type 2 Diabetes Treated With Basal Insulin: A Randomized Clinical Trial. JAMA. 2021 Jun 8;325(22):2262-2272. doi: 10.1001/jama.2021.7444. PMID: 34077499; PMCID: PMC8173473.

6. Di Molfetta S, Rossi A, Boscari F, Irace C, Laviola L, Bruttomesso D. Criteria for Personalised Choice of a Continuous Glucose Monitoring System: An Expert Opinion. Diabetes Ther. 2024 Nov;15(11):2263-2278. doi: 10.1007/s13300-024-01654-y. Epub 2024 Sep 30. Erratum in: Diabetes Ther. 2025 Jan;16(1):135. doi: 10.1007/s13300-024-01666-8. PMID: 39347900; PMCID: PMC11467157.

7. Ji C, Jiang T, Liu L, Zhang J, You L. Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management. Front Endocrinol (Lausanne). 2025 May 26;16:1571362. doi: 10.3389/fendo.2025.1571362. PMID: 40491592; PMCID: PMC12146165.

8. Dassau E, Cameron F, Lee H, Bequette BW, Zisser H, Jovanovic L, Chase HP, Wilson DM, Buckingham BA, Doyle FJ 3rd. Real-Time hypoglycemia prediction suite using continuous glucose monitoring: a safety net for the artificial pancreas. Diabetes Care. 2010 Jun;33(6):1249-54. doi: 10.2337/dc09-1487. PMID: 20508231; PMCID: PMC2875433.

9. Ware J, Hovorka R. Closed-loop insulin delivery: update on the state of the field and emerging technologies. Expert Rev Med Devices. 2022 Nov;19(11):859-875. doi: 10.1080/17434440.2022.2142556. Epub 2022 Nov 4. PMID: 36331211; PMCID: PMC9780196.

10. Fuchs J, Hovorka R. Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy. Expert Rev Med Devices. 2020 Jul;17(7):707-720. doi: 10.1080/17434440.2020.1784724. Epub 2020 Jul 3. PMID: 32569476; PMCID: PMC7441745.

11. Ying Z, Li X, Chen Y. Artificial intelligence in glycemic management for diabetes: Applications, opportunities and challenges. J Transl Int Med. 2025 Aug 12;13(4):314-317. doi: 10.1515/jtim-2025-0039. PMID: 40861074; PMCID: PMC12371392.

12. Dave D, Erraguntla M, Lawley M, DeSalvo D, Haridas B, McKay S, Koh C. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes. 2021 Apr 29;6(2):e26909. doi: 10.2196/26909. PMID: 33913816; PMCID: PMC8120423.

13. Fraser RA, Walker RJ, Campbell JA, Ekwunife O, Egede LE. Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes. NPJ Digit Med. 2025 Nov 18;8(1):687. doi: 10.1038/s41746-025-02036-9. PMID: 41254217; PMCID: PMC12627454.

14. Alfadli SF, Alotaibi YS, Aqdi MJ, Almozan LA, Alzubaidi ZB, Altemani HA, Almutairi SD, Alabdullah HA, Almehmadi AA, Alanzi AL, Azzam AY. Effectiveness of continuous glucose monitoring systems on glycemic control in adults with type 1 diabetes: A systematic review and meta-analysis. Metabol Open. 2025 Jul 29;27:100382. doi: 10.1016/j.metop.2025.100382. Erratum in: Metabol Open. 2025 Aug 05;27:100383. doi: 10.1016/j.metop.2025.100383. PMID: 40791933; PMCID: PMC12337206.

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