Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Jun 25;43(8):e70404. doi: 10.1111/dme.70404

Beyond HbA1c: A CGM‐centred three‐pillar framework for glycaemic variability in pre‐diabetes and type 2 diabetes

Taoming Qian 1, Donghao Guo 2, Meijun Zhang 3, Yuhan Liu 1, Juan Jin 2,✉
PMCID: PMC13380337  PMID: 42357829

Abstract

Background

Traditional management of type 2 diabetes mellitus (T2DM) and pre‐diabetes centres on HbA1c reduction, yet this single metric fails to capture glycaemic variability (GV)—an independent driver of β‐cell dysfunction, endothelial damage, oxidative stress, inflammation and cardiovascular risk, even in the pre‐diabetes stage. Unlike existing reviews that focus predominantly on T2DM or HbA1c‐centric approaches, this perspective articulates a unified three‐pillar closed‐loop framework that integrates continuous glucose monitoring (CGM) across the entire pre‐diabetes‐to‐remission continuum.

Perspective

We synthesize evidence from prospective cohorts, mechanistic studies, randomized controlled trials and real‐world data to demonstrate that GV is a modifiable therapeutic target. CGM uniquely reveals dynamic glucose patterns invisible to HbA1c, enabling precise, real‐time interventions that improve time in range (TIR), reduce GV and hypoglycaemia and support the achievement of clinical remission (HbA1c <48 mmol/mol (<6.5%) without glucose‐lowering medication for ≥3 months) in subsets of patients.

Conclusions

CGM‐centred management of glycaemic stability offers a clinically actionable paradigm shift from static, average‐glucose control to dynamic, precision‐guided care. The proposed three‐pillar framework—tiered personalized targets, data‐driven intelligent decision support and empowered patient–clinician collaboration—provides a structured roadmap grounded in current evidence. Long‐term outcomes in pre‐diabetes, cost‐effectiveness and global accessibility remain important areas for future investigation.

Keywords: continuous glucose monitoring, glycaemic variability, HbA1c, pre‐diabetes, type 2 diabetes

What's new?

  • Glycaemic variability (GV)—an independent driver of β‑cell dysfunction, oxidative stress, inflammation and cardiovascular risk—is detectable and modifiable at the prediabetes stage, yet remains invisible to conventional HbA1c monitoring.

  • Continuous glucose monitoring (CGM) uniquely uncovers real‑time glycaemic patterns (postprandial spikes, dawn phenomenon, nocturnal dips) and provides validated metrics—time in range (TIR), time below range (TBR) and coefficient of variation (CV)—that redefine glycaemic control as multidimensional stability rather than a single retrospective average, enabling earlier detection of dysglycaemia before overt hyperglycaemia emerges.

  • A CGM‑centred closed‑loop framework comprising three interdependent pillars—tiered personalized dynamic targets, data‑driven intelligent decision support and empowered patient‑clinician collaboration—offers a structured, evidence‑based roadmap to translate dynamic glucose data into actionable clinical decisions across the entire prediabetes‑to‑remission continuum.

1. INTRODUCTION

The natural history of type 2 diabetes mellitus (T2DM) begins with pre‐diabetes, characterized by insulin resistance and progressive β‐cell dysfunction, frequently exacerbated by obesity and a sedentary lifestyle. 1 Traditional management has focused on reducing HbA1c to prevent progression to overt T2DM and microvascular complications. However, emerging evidence indicates that HbA1c alone does not fully capture the pathogenic contributions of glycaemic variability (GV) and postprandial hyperglycaemia to accelerated β‐cell decline, endothelial dysfunction and cardiovascular risk—even at the pre‐diabetes stage. 2

Prospective cohort studies 3 , 4 and mechanistic investigations demonstrate that GV is an independent predictor of progression from pre‐diabetes to T2DM and of macrovascular events. Postprandial glucose excursions trigger oxidative stress and inflammation via increased reactive oxygen species production and the NF‐κB pathway activation. 5 , 6 In contrast, early interventions that achieve glycaemic stability can halt or reverse this trajectory: lifestyle‐induced weight loss in individuals with pre‐diabetes restores normoglycaemia in 40%–50% of cases, 7 whereas intensive lifestyle or weight‐management interventions in early T2DM achieve remission rates of 30%–60% at 1 year. However, it is important to acknowledge that remission or prevention is achieved in only approximately 40%–50% of individuals; diabetes progression involves complex multifactorial mechanisms (including genetic, inflammatory and environmental factors) beyond lifestyle and glycaemic control alone, and a meaningful subset of patients will not achieve sustained remission despite optimal management. 8 , 9

Remission—defined as HbA1c <48 mmol/mol (<6.5%) without glucose‐lowering medication for at least 3 months—depends not only on lowering mean glucose but also on sustained reduction in GV to alleviate glucotoxicity and enable β‐cell recovery. 10 Continuous glucose monitoring (CGM) uniquely elucidates these dynamics by uncovering real‐time glycaemic patterns that are obscured by HbA1c or intermittent self‐monitoring of blood glucose. Randomized controlled trials in both pre‐diabetes and T2DM have shown that CGM‐guided management improves time in range (TIR), reduces GV and HbA1c and limits hypoglycaemia, thereby fostering conditions favourable for remission. 11 , 12 A critical gap remains the global accessibility of CGM. High device and sensor costs, lack of reimbursement infrastructure and limited technological literacy preclude widespread adoption. This may widen health disparities. 13

The treatment paradigm must therefore shift from HbA1c‐centred average glucose control to CGM‐centred management of glycaemic stability and variability throughout the pre‐diabetes‐to‐remission continuum. To illustrate the proposed transformation in diabetes care, Figure 1 presents a conceptual framework that contrasts the limitations of traditional HbA1c‐centred management with a CGM‐guided precision strategy focused on glycaemic stability and variability. This closed‐loop model, built on three interdependent pillars, provides the foundation for the precision management approach outlined in this perspective.

FIGURE 1.

FIGURE 1

Open in a new tab

Paradigm shift from HbA1c‐centred to continuous glucose monitoring (CGM)‐centred precision management of glycaemic variability in type 2 diabetes and pre‐diabetes. The schematic illustrates the transition from a static, retrospective HbA1c‐based approach to a dynamic, proactive strategy anchored by continuous glucose monitoring (CGM). Key CGM‐derived metrics, including time in range (TIR; 3.9–10.0 mmol/L) and time below range (TBR), are highlighted alongside the three foundational pillars of the proposed closed‐loop framework: (1) tiered personalized dynamic targets, (2) data‐intelligent interventions and (3) empowered clinician–patient partnership enabled by remote data sharing.

2. NEW FOUNDATION: CGM‐DEFINED GLYCAEMIC CONTROL QUALITY IN TYPE 2 DIABETES AND PRE‐DIABETES

CGM fundamentally transforms the assessment of glycaemia by converting episodic measurements into comprehensive, real‐time profiles. This technology generates clinically validated metrics that reveal dynamic patterns of glucose dysregulation, offering unique insights into disease progression risk and potential for remission in pre‐diabetes and type 2 diabetes mellitus (T2DM). 14 , 15 , 16 Unlike HbA1c, which provides a retrospective average, CGM captures intraday and interday fluctuations, enabling earlier detection of subtle abnormalities that precede overt hyperglycaemia. 17

The primary efficacy end point is TIR; 3.9–10.0 mmol/L. Higher TIR is associated with preserved β‐cell function, reduced microvascular complications, and lower oxidative stress, while improvements in TIR predict greater durability of remission in early T2DM. 18 , 19 Randomized trials demonstrate that CGM‐guided personalized nutrition or lifestyle interventions significantly increase TIR, even in pre‐diabetes, where baseline TIR is often lower than anticipated despite normal HbA1c. 20 , 21 In populations with intermediate hyperglycaemia, TIR improvements, often achieved through weight loss or targeted dietary adjustments, are associated with reduced postprandial excursions and enhanced metabolic flexibility, supporting its role as a sensitive marker of early metabolic restoration. 22 , 23

Safety and stability are captured by time below range (TBR <3.9 mmol/L) and coefficient of variation (CV). Low TBR (<4%) minimizes iatrogenic hypoglycaemia, which can undermine adherence to lifestyle interventions essential for remission. 24 The CV <36% threshold comes from international consensus recommendations validated mainly in T2DM. Emerging data suggest it also applies to pre‐diabetes. 25 , 26 Elevated CV is detectable early in pre‐diabetes, often preceding overt hyperglycaemia, and reflects underlying insulin resistance or β‐cell dysfunction.

CGM also identifies actionable patterns—postprandial spikes, dawn phenomenon and nocturnal dips—that drive progression in pre‐diabetes and relapse after remission. 27 , 28 These patterns are frequently missed by HbA1c or fasting glucose alone but are readily visualized with CGM, enabling targeted interventions such as meal timing adjustments, pre‐meal physical activity or macronutrient redistribution. 15 , 20 In pre‐diabetes, CGM reveals substantial interindividual variability in postprandial responses to identical meals, underscoring the value of pattern recognition for personalized prevention strategies and highlighting the limitations of one‐size‐fits‐all dietary recommendations. 21 , 29

Collectively, these metrics redefine glycaemic quality as multidimensional stability rather than a single retrospective average. TIR prioritizes effective control. TBR and CV emphasize safety and consistency. Pattern analysis enables precision interventions. 15 , 24 In pre‐diabetes and early T2DM, where remission is achievable through lifestyle modification, CGM‐derived metrics provide earlier, more granular signals of dysglycaemia than traditional markers, supporting proactive management to halt progression, relieve glucotoxicity and promote metabolic recovery. 18 , 20 As CGM adoption expands in populations without diabetes, these metrics are increasingly positioned to guide risk stratification and preventive therapies, potentially reshaping clinical guidelines for intermediate hyperglycaemia. 30 , 31

3. EVIDENCE FOUNDATION

Robust randomized controlled trials (RCTs) and real‐world studies provide compelling evidence that CGM improves glycaemic control in T2DM while simultaneously reducing hypoglycaemia risk and GV—outcomes critical for cardiovascular protection and sustained remission potential. 30 , 32 , 33 Unlike traditional self‐monitoring of blood glucose, CGM offers detailed, objective insights into daily glucose dynamics, enabling timely therapeutic adjustments. 17 Table 1 summarizes pivotal trials across insulin‐treated, non‐insulin‐treated and insulin‐naïve T2DM populations, highlighting consistent benefits in HbA1c reduction, TIR, TBR and GV stabilization. Pre‐diabetes‐specific supportive data from pilot interventional studies are summarized in the lower rows of Table 1.

TABLE 1.

Selected clinical trials demonstrating CGM benefits in type 2 diabetes and pre‐diabetes.

Study (year) Design/Population Key findings Core CGM metrics PMID
Steno2tech (Lind, 2024) 34 RCT; 76 insulin‐treated T2DM, HbA1c ≥7.5% CGM superior to BGM for HbA1c, hypoglycaemia, and GV TIR ↑15.2% (95% CI 4.6–25.9); HbA1c ↓0.9%; TBR ↓ 38489032
Reed (2024) 35 Crossover RCT; 47 high‐CVD‐risk non‐insulin T2DM CGM improved HbA1c, GV, and cardiometabolic risk; minimal hypoglycaemia TIR ↑25% (57.8 → 82.8%); CV ↓ (26.2 → 23.8%) 38680050
ONWARDS 1 substudy (Bergenstal, 2024) 36 RCT; 984 insulin‐naïve T2DM CGM‐guided basal insulin: comparable HbA1c, low TBR, stable GV TIR >70% achieved; TBR <4%/<1% 39374601
Lau (2024) 37 RCT; 105 non‐insulin T2DM, HbA1c >7% CGM + telemonitoring superior and safe HbA1c ↓0.65% greater; RR 1.92 for ≥0.5% reduction 39433218
SECURE‐T2DM (Pasquel, 2025) 38 Single‐arm; 305 insulin‐treated T2DM CGM‐integrated AID markedly improved control TIR ↑20% (45 → 66%); HbA1c ↓0.8% 39951268
ONWARDS 9 (Bergenstal, 2025) 39 Single‐arm RCT; 51 insulin‐naïve T2DM CGM‐titrated weekly insulin: large gains, no severe hypoglycaemia TIR ↑22% (54.4 → 76.4%); HbA1c ↓1.17% 40040516
Martens (2025) 40 RCT; 72 non‐insulin T2DM, HbA1c 7.5%–12% CGM‐guided lifestyle without early medication adjustment TIR ↑25% (46 → 71%); HbA1c ↓1.3% 39757879
Yost (2020) 41 Mixed‐methods pilot; 15 adults with pre‐diabetes (HbA1c 5.7%–6.4%) CGM + low‐carbohydrate diet coaching: 93% satisfaction, weight ↓1.4 lb. (p = 0.02), HbA1c ↓0.71% (p < 0.001) Tendency for ↓ time >7.8 mmol/L and mean glucose 33325831
Ma (2025) 42 Pilot RCT; 41 adults with pre‐diabetes (RT‐CGM n = 20 vs. control n = 21) RT‐CGM‐guided individualized education superior to control for HbA1c reduction at 1‐year (p = 0.007) and 2‐year (p = 0.033) follow‐up TIR/TAR/TBR used for real‐time meal adjustments 40538565
Open in a new tab

Note: Table primarily summarizes key T2DM RCTs. Pre‐diabetes‐specific evidence is derived from smaller pilot and interventional studies (Yost 2020 and Ma 2025 shown above). Long‐term hard clinical outcome RCTs and cost‐effectiveness data in pre‐diabetes remain limited.

These trials collectively demonstrate CGM's multifaceted advantages. In insulin‐treated T2DM, CGM consistently achieves clinically meaningful HbA1c reductions (0.8%–0.9%) alongside substantial TIR gains (15%–20%) and TBR minimization, without increasing severe hypoglycaemia. 34 , 38 Among non‐insulin users, including high‐cardiovascular‐risk cohorts, CGM yields even larger TIR improvements (up to 25%) and GV reductions, with additional cardiometabolic benefits such as lowered triglycerides and projected atherosclerotic risk. 35 , 40 Telemonitoring‐enhanced CGM further amplifies HbA1c lowering in real‐world settings. 37

Notably, CGM‐guided basal insulin titration in insulin‐naïve patients achieves TIR targets (>70%) comparable to daily analogues while maintaining exceptionally low TBR (<4%) and brief hypoglycaemic episodes. 36 , 39 Across regimens, GV stabilization—reflected in reduced CV and standard deviation (SD)—is a recurring theme, independent of baseline control. 33 These findings align with broader consensus that CGM metrics outperform HbA1c alone in predicting complications and guiding therapy. 43

The evidence underscores CGM's transformative role: it not only enhances traditional end points but also reveals actionable patterns that mitigate GV‐driven oxidative stress and endothelial damage. 44 In diverse T2DM subpopulations, CGM facilitates safer intensification, supports lifestyle optimization and promotes stability—foundational for preventing progression and pursuing remission. 30

4. CORE STRATEGY: BUILDING A CGM‐CENTRED CLOSED‐LOOP SYSTEM

The transition to CGM‐centred management requires a structured, closed‐loop framework that translates dynamic glucose data into actionable clinical decisions. This strategy rests on three interdependent pillars: tiered personalized targets, data‐driven intelligent decision support, and empowered patient–clinician collaboration. Together, these elements shift care from reactive HbA1c‐driven adjustments to proactive stability optimization across the pre‐diabetes‐to‐T2DM continuum. 30 , 31

4.1. Pillar 1: Tiered, personalized dynamic targets

Uniform HbA1c thresholds fail to account for individual variability in glucose patterns, remission potential and complication risk. CGM enables stratified, stage‐specific goals co‐developed with patients and reviewed quarterly. 45 For pre‐diabetes—where early intervention can prevent progression—targets emphasize stringent stability: TIR >80%–85%, CV <36% and TBR <1% to minimize glucotoxicity and preserve β‐cell function. 21 , 41 In early or established T2DM, where remission remains achievable through lifestyle‐induced weight loss, ambitious yet feasible goals apply: TIR >70%–80%, TBR <4% and CV <36%. 45 , 46 For high‐risk groups (elderly patients or those with cardiovascular disease), safety predominates: TIR >50%–60% with stringent TBR <1% to minimize hypoglycaemia while preserving quality of life. 45 Dynamic adjustment based on longitudinal CGM trends—rather than isolated HbA1c values—supports sustained remission and prevents relapse, with evidence showing that personalized TIR goals enhance adherence and metabolic outcomes. 47

4.2. Pillar 2: Data‐driven intelligent decision support

Effective translation of CGM data demands advanced tools that automate insight generation and guide precise interventions. 48 Automated clinical decision support systems (CDSS) analyse patterns—such as recurrent postprandial spikes or dawn phenomenon—and provide evidence‐based recommendations, including dose titration or behavioural prompts. 32 Integration with automated insulin delivery (AID) systems enables real‐time optimization; nonrandomized and randomized trials in T2DM demonstrate TIR increases of 20% with concomitant GV reduction and low hypoglycaemia risk. 38 Predictive analytics, leveraging machine learning on longitudinal CGM profiles, can identify individual risks and patterns with high accuracy, facilitating preemptive adjustments in diet, activity, or pharmacotherapy. 14 In non‐insulin‐treated T2DM, CGM‐guided decision support has yielded HbA1c reductions of 0.5%–1.3% without increased hypoglycaemia, underscoring its utility for early intensification. 37

4.3. Pillar 3: Empowered patient–clinician collaboration

CGM redefines the therapeutic alliance by enabling continuous, remote data sharing that extends beyond episodic visits. Intuitive dashboards overlay glucose traces with logged meals, activity and stressors, transforming patients into active partners who recognize personal triggers and self‐adjust behaviours. 20 This visualization fosters sustained lifestyle changes critical for remission, with trials showing improved glycaemic control and potential for enhanced self‐management in pre‐diabetes and T2DM cohorts using shared CGM platforms. 35 , 47 Clinicians evolve into coaches, interpreting trends collaboratively to refine strategies and reinforce motivation. Remote monitoring further improves glycaemic control in diverse populations, with telehealth‐integrated CGM demonstrating superior TIR versus standard care. Qualitative evidence highlights strengthened therapeutic relationships and reduced diabetes distress through objective, shared decision making. 49

This closed‐loop framework—personalized targets, intelligent analytics, and collaborative empowerment—operationalizes CGM's potential to deliver safer, more stable, and individualized care. By prioritizing multidimensional stability over singular averages, it addresses core drivers of progression and positions CGM as indispensable for achieving and sustaining remission in pre‐diabetes and T2DM. 50 Figure 2 illustrates the stage‐specific application of the three‐pillar framework across the pre‐diabetes‐to‐remission continuum. The transition to CGM‐centred management requires a structured, closed‐loop framework.

FIGURE 2.

FIGURE 2

Open in a new tab

Stage‐adapted application of the three‐pillar CGM‐centred framework across the pre‐diabetes‐to‐remission continuum. The schematic shows how the three pillars are dynamically tailored to different disease stages. Pillar 1 presents stage‐specific glycaemic targets for time in range (TIR), time below range (TBR) and coefficient of variation (CV). Pillar 2 highlights the most relevant decision support tools, and Pillar 3 indicates the recommended frequency of patient–clinician collaboration. All elements are interconnected through a closed‐loop feedback system to enable precision management and support progression toward remission where possible.

5. LIMITATIONS

This perspective is a narrative synthesis and therefore subject to selection bias. CGM evidence in pre‐diabetes is derived primarily from small pilots and observational studies, with no large‐scale, long‐term RCTs demonstrating definitive reductions in diabetes incidence or hard clinical outcomes. Mechanistic links to β‐cell preservation and remission, while biologically plausible, exceed direct causal evidence from current RCTs. Specifically, the assumption that GV reduction directly preserves β‐cell function remains largely inferential, supported by preclinical models but not yet confirmed in prospective human trials. Cost‐effectiveness is well supported in insulin‐treated T2DM but limited in pre‐diabetes and resource‐constrained settings. Formal Grading of Recommendations Assessment, Development and Evaluation (GRADE) grading of evidence is not applied systematically, as this is a perspective rather than a systematic review; however, where cited, evidence quality is noted descriptively. Broader implementation requires addressing accessibility, equity and value beyond optimized standard care. Access to CGM remains highly inequitable globally, with limited reimbursement in low‐ and middle‐income countries, representing a major translational barrier. 16 , 17

6. CONCLUSION

Evidence positions the management of glycaemic GV as an important component—though not the sole determinant—of achieving durable remission where possible and preventing complications in type 2 diabetes. The traditional HbA1c‐centric paradigm is inherently limited for capturing dynamic glucose patterns. The maturation of CGM and the clinical validation of its dynamic metrics—TIR, TBR and CV—provide granular insight to assess and intervene on GV. However, the technology alone is not sufficient; individual responses vary, and not all patients achieve remission. This article has outlined a systematic framework translating CGM data into action. The proposed CGM‐centred closed‐loop management framework—integrating staged dynamic targets, data‐driven intelligent decision support, and an empowered collaborative care model—offers a structured roadmap grounded in available RCTs and real‐world data.

In pre‐diabetes, this approach may support early intervention to halt progression in subsets of patients. In established type 2 diabetes, it facilitates safer treatment intensification and lifestyle optimization where remission is attainable. Future research should prioritize adequately powered RCTs evaluating long‐term clinical outcomes, cost‐effectiveness across diverse populations (including resource‐limited settings), and implementation strategies to realize the full translational potential of CGM‐centred precision management. In summary, moving beyond HbA1c to incorporate CGM‐centred management of glycaemic stability represents a data‐driven evolution in diabetes care philosophy—from chasing a single averaged number to pursuing high‐quality, individualized glycaemic stability.

AUTHOR CONTRIBUTIONS

Taoming Qian: Conceptualization, literature search, data curation, writing – original draft, writing – review & editing, visualization, final approval. Donghao Guo: Conceptualization, literature search, data curation, writing – original draft, writing – review & editing, final approval. Meijun Zhang: Literature search, data validation, writing – review & editing, final approval. Yuhan Liu: Data curation, writing – review & editing, visualization, final approval. Juan Jin (corresponding author): Conceptualization, methodology, supervision, writing – review & editing, data interpretation, project administration, final approval. All authors contributed substantially to the work, approved the final version to be published, agree to be accountable for all aspects of the work, and have agreed on the journal to which the article has been submitted.

FUNDING INFORMATION

This research received no external funding.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article, as no data were created or analysed in this study.

REFERENCES

  • 1. Unnikrishnan R, Shaw JE, Chan JCN, et al. Prediabetes. Nat Rev Dis Primers. 2025;11(1):49. doi: 10.1038/s41572-025-00635-0 [DOI] [PubMed] [Google Scholar]
  • 2. Chen J, Yi Q, Wang Y, et al. Long‐term glycemic variability and risk of adverse health outcomes in patients with diabetes: a systematic review and meta‐analysis of cohort studies. Diabetes Res Clin Pract. 2022;192:110085. doi: 10.1016/j.diabres.2022.110085 [DOI] [PubMed] [Google Scholar]
  • 3. Barzegar N, Ramezankhani A, Tohidi M, Azizi F, Hadaegh F. Long‐term glucose variability and incident cardiovascular diseases and all‐cause mortality events in subjects with and without diabetes: Tehran lipid and glucose study. Diabetes Res Clin Pract. 2021;178:108942. doi: 10.1016/j.diabres.2021.108942 [DOI] [PubMed] [Google Scholar]
  • 4. Cardoso CRL, Leite NC, Moram CBM, Salles GF. Long‐term visit‐to‐visit glycemic variability as predictor of micro‐ and macrovascular complications in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study. Cardiovasc Diabetol. 2018;17(1):33. doi: 10.1186/s12933-018-0677-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Hanssen NMJ, Kraakman MJ, Flynn MC, Nagareddy PR, Schalkwijk CG, Murphy AJ. Postprandial glucose spikes, an important contributor to cardiovascular disease in diabetes? Front Cardiovasc Med. 2020;7:570553. doi: 10.3389/fcvm.2020.570553 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Musso G, Alberto M, Mariano F, et al. Impaired postprandial GLP‐2 response enhances endotoxemia, systemic inflammation, and kidney injury in metabolic dysfunction‐associated steatohepatitis (MASH): effect of phospholipid curcumin meriva. Gut Microbes. 2024;16(1):2424907. doi: 10.1080/19490976.2024.2424907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Taheri S, Zaghloul H, Chagoury O, et al. Effect of intensive lifestyle intervention on bodyweight and glycaemia in early type 2 diabetes (DIADEM‐I): an open‐label, parallel‐group, randomised controlled trial. Lancet Diabetes Endocrinol. 2020;8(6):477‐489. doi: 10.1016/s2213-8587(20)30117-0 [DOI] [PubMed] [Google Scholar]
  • 8. Dagogo‐Jack S, Umekwe N, Brewer AA, et al. Outcome of lifestyle intervention in relation to duration of pre‐diabetes: the pathobiology and reversibility of prediabetes in a biracial cohort (PROP‐ABC) study. BMJ Open Diabetes Res Care. 2022;10(2):e002748. doi: 10.1136/bmjdrc-2021-002748 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Galaviz KI, Weber MB, Suvada KB, et al. Interventions for reversing prediabetes: a systematic review and meta‐analysis. Am J Prev Med. 2022;62(4):614‐625. doi: 10.1016/j.amepre.2021.10.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Riddle MC, Cefalu WT, Evans PH, et al. Consensus report: definition and interpretation of remission in type 2 diabetes. Diabetes Care. 2021;44(10):2438‐2444. doi: 10.2337/dci21-0034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Retnakaran R, Pu J, Shen J, Hanley AJ, Zinman B. The central role of recovery of β‐cell function in the remission of prediabetes: a prospective cohort study in Canada. Lancet Diabetes Endocrinol. 2025;13(10):829‐837. doi: 10.1016/s2213-8587(25)00163-9 [DOI] [PubMed] [Google Scholar]
  • 12. Liu X, Zhang J. Continuous glucose monitoring: a transformative approach to the detection of prediabetes. J Multidiscip Healthc. 2024;17:5513‐5519. doi: 10.2147/jmdh.S493128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Veríssimo D, Pereira BR, Vinhais J, et al. Cost‐effectiveness of inpatient continuous glucose monitoring. Cureus. 2024;16(3):e55999. doi: 10.7759/cureus.55999 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Metwally AA, Perelman D, Park H, et al. Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning. Nat Biomed Eng. 2025;9(8):1222‐1239. doi: 10.1038/s41551-024-01311-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Battelino T, Lalic N, Hussain S, et al. The use of continuous glucose monitoring in people living with obesity, intermediate hyperglycemia or type 2 diabetes. Diabetes Res Clin Pract. 2025;223:112111. doi: 10.1016/j.diabres.2025.112111 [DOI] [PubMed] [Google Scholar]
  • 16. Hall H, Perelman D, Breschi A, et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018;16(7):e2005143. doi: 10.1371/journal.pbio.2005143 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Bergenstal RM, Beck RW, Close KL, et al. Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care. 2018;41(11):2275‐2280. doi: 10.2337/dc18-1581 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Lu J, Ma X, Zhou J, et al. Association of Time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care. 2018;41(11):2370‐2376. doi: 10.2337/dc18-1131 [DOI] [PubMed] [Google Scholar]
  • 19. Zahedani AD, McLaughlin T, Veluvali A, et al. Digital health application integrating wearable data and behavioral patterns improves metabolic health. NPJ Digit Med. 2023;6(1):216. doi: 10.1038/s41746-023-00956-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Santos‐Báez LS, Diaz‐Rizzolo DA, Borhan R, et al. Predictive models of post‐prandial glucose response in persons with prediabetes and early onset type 2 diabetes: a pilot study. Diabetes Obes Metab. 2025;27(3):1515‐1525. doi: 10.1111/dom.16160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. He J, Chu N, Wan H, et al. Use of technology in prediabetes and precision prevention. J Diabetes Investig. 2025;16(7):1217‐1231. doi: 10.1111/jdi.70057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Barua S, Upadhyay D, Berube LT, et al. Weight loss is associated with improved daytime time in range in adults with prediabetes and non‐insulin‐treated type 2 diabetes undergoing dietary intervention. Diabet Med. 2025;42(8):e70052. doi: 10.1111/dme.70052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Zahalka SJ, Galindo RJ, Shah VN, Low Wang CC. Continuous glucose monitoring for prediabetes: what are the best metrics? J Diabetes Sci Technol. 2024;18(4):835‐846. doi: 10.1177/19322968241242487 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Hjort A, Iggman D, Rosqvist F. Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: a systematic review and meta‐analysis. Clin Nutr. 2024;43(4):915‐925. doi: 10.1016/j.clnu.2024.02.014 [DOI] [PubMed] [Google Scholar]
  • 25. Cichosz SL, Kronborg T, Laugesen E, et al. From stability to variability: classification of healthy individuals, prediabetes, and type 2 diabetes using glycemic variability indices from continuous glucose monitoring data. Diabetes Technol Ther. 2025;27(1):34‐44. doi: 10.1089/dia.2024.0226 [DOI] [PubMed] [Google Scholar]
  • 26. Gorst C, Kwok CS, Aslam S, et al. Long‐term glycemic variability and risk of adverse outcomes: a systematic review and meta‐analysis. Diabetes Care. 2015;38(12):2354‐2369. doi: 10.2337/dc15-1188 [DOI] [PubMed] [Google Scholar]
  • 27. Monnier L, Colette C, Owens DR. Glycemic variability: the third component of the dysglycemia in diabetes. Is it important? How to measure it? J Diabetes Sci Technol. 2008;2(6):1094‐1100. doi: 10.1177/193229680800200618 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Goh AT, Yao J, Chua XH, Whitton C, van Dam RM, Forde CG. Associations between oral processing, saliva, and bolus properties on daily glucose excursions amongst people at risk of type‐2 diabetes. Food Funct. 2023;14(4):2260‐2269. doi: 10.1039/d2fo03060h [DOI] [PubMed] [Google Scholar]
  • 29. Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079‐1094. doi: 10.1016/j.cell.2015.11.001 [DOI] [PubMed] [Google Scholar]
  • 30. Advani A. Positioning time in range in diabetes management. Diabetologia. 2020;63(2):242‐252. doi: 10.1007/s00125-019-05027-0 [DOI] [PubMed] [Google Scholar]
  • 31. Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593‐1603. doi: 10.2337/dci19-0028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Martens T, Beck RW, Bailey R, et al. Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes treated with basal insulin: a randomized clinical trial. JAMA. 2021;325(22):2262‐2272. doi: 10.1001/jama.2021.7444 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Battelino T, Alexander CM, Amiel SA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2023;11(1):42‐57. doi: 10.1016/s2213-8587(22)00319-9 [DOI] [PubMed] [Google Scholar]
  • 34. Lind N, Christensen MB, Hansen DL, Nørgaard K. Comparing continuous glucose monitoring and blood glucose monitoring in adults with inadequately controlled, insulin‐treated type 2 diabetes (Steno2tech study): a 12‐month, single‐center, randomized controlled trial. Diabetes Care. 2024;47(5):881‐889. doi: 10.2337/dc23-2194 [DOI] [PubMed] [Google Scholar]
  • 35. Reed J, Dong T, Eaton E, et al. Continuous glucose monitoring for glycaemic control and cardiovascular risk reduction in patients with type 2 diabetes not on insulin therapy: a clinical trial. Diabetes Obes Metab. 2024;26(7):2881‐2889. doi: 10.1111/dom.15608 [DOI] [PubMed] [Google Scholar]
  • 36. Bergenstal RM, Ásbjörnsdóttir B, Watt SK, et al. Continuous glucose monitoring‐based metrics and the duration of hypoglycaemia events with once‐weekly insulin icodec versus once‐daily insulin glargine U100 in insulin‐naive type 2 diabetes: an exploratory analysis of ONWARDS 1. Lancet Diabetes Endocrinol. 2024;12(11):799‐810. doi: 10.1016/s2213-8587(24)00220-1 [DOI] [PubMed] [Google Scholar]
  • 37. Lau D, Manca DP, Singh P, et al. The effectiveness of continuous glucose monitoring with remote telemonitoring‐enabled virtual educator visits in adults with non‐insulin dependent type 2 diabetes: a randomized trial. Diabetes Res Clin Pract. 2024;217:111899. doi: 10.1016/j.diabres.2024.111899 [DOI] [PubMed] [Google Scholar]
  • 38. Pasquel FJ, Davis GM, Huffman DM, et al. Automated insulin delivery in adults with type 2 diabetes: a nonrandomized clinical trial. JAMA Netw Open. 2025;8(2):e2459348. doi: 10.1001/jamanetworkopen.2024.59348 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Bergenstal RM, Ásbjörnsdóttir B, Johanning Bari T, Hulkund S, Winhofer Y, Wysham C. Continuous glucose monitoring‐based titration of once‐weekly insulin Icodec in insulin‐naive individuals with type 2 diabetes (ONWARDS 9): a phase 3b, multicenter, single‐arm, Treat‐to‐Target Clinical Trial. Diabetes Technol Ther. 2025;27(7):527‐536. doi: 10.1089/dia.2025.0050 [DOI] [PubMed] [Google Scholar]
  • 40. Martens TW, Willis HJ, Bergenstal RM, Kruger DF, Karslioglu‐French E, Steenkamp DW. A randomized controlled trial using continuous glucose monitoring to guide food choices and diabetes self‐Care in People with type 2 diabetes not taking insulin. Diabetes Technol Ther. 2025;27(4):261‐270. doi: 10.1089/dia.2024.0579 [DOI] [PubMed] [Google Scholar]
  • 41. Yost O, DeJonckheere M, Stonebraker S, et al. Continuous glucose monitoring with Low‐carbohydrate diet coaching in adults with prediabetes: mixed methods pilot study. JMIR Diabetes. 2020;5(4):e21551. doi: 10.2196/21551 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Ma Y, Deng S, Xie W, Weng M, Jia Y. Effectiveness of an individualized diabetes health education program using real‐time continuous glucose monitoring in improving blood glucose: a pilot interventional study on subjects with prediabetes. Diabetes Metab Syndr Obes. 2025;18:1939‐1948. 10.2147/dmso.s511187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Beck RW, Bergenstal RM, Riddlesworth TD, et al. Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care. 2019;42(3):400‐405. doi: 10.2337/dc18-1444 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Monnier L, Colette C, Owens D. The glycemic triumvirate and diabetic complications: is the whole greater than the sum of its component parts? Diabetes Res Clin Pract. 2012;95(3):303‐311. doi: 10.1016/j.diabres.2011.10.014 [DOI] [PubMed] [Google Scholar]
  • 45. Association AD . 6. Glycemic goals and hypoglycemia: standards of Care in Diabetes‐2024. Diabetes Care. 2024;47(Suppl 1):S111‐s125. doi: 10.2337/dc24-S006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Lean ME, Leslie WS, Barnes AC, et al. Primary care‐led weight management for remission of type 2 diabetes (DiRECT): an open‐label, cluster‐randomised trial. Lancet. 2018;391(10120):541‐551. doi: 10.1016/s0140-6736(17)33102-1 [DOI] [PubMed] [Google Scholar]
  • 47. Ehrhardt N, Al Zaghal E. Behavior modification in prediabetes and diabetes: potential use of real‐time continuous glucose monitoring. J Diabetes Sci Technol. 2019;13(2):271‐275. doi: 10.1177/1932296818790994 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Bergenstal RM, Johnson M, Passi R, et al. Automated insulin dosing guidance to optimise insulin management in patients with type 2 diabetes: a multicentre, randomised controlled trial. Lancet. 2019;393(10176):1138‐1148. doi: 10.1016/s0140-6736(19)30368-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Litchman ML, Allen NA, Colicchio VD, et al. A qualitative analysis of real‐time continuous glucose monitoring data sharing with care partners: to share or not to share? Diabetes Technol Ther. 2018;20(1):25‐31. doi: 10.1089/dia.2017.0285 [DOI] [PubMed] [Google Scholar]
  • 50. Chang TJ. Mechanisms and the strategy for remission of type 2 diabetes mellitus. J Diabetes Investig. 2023;14(3):351‐353. doi: 10.1111/jdi.13948 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data sharing is not applicable to this article, as no data were created or analysed in this study.

Articles from Diabetic Medicine are provided here courtesy of Wiley

RESOURCES