Introduction

Type 2 diabetes mellitus (T2DM) evolved from prediabetes, which is not only a necessary stage of T2DM, but also the only key period with reversibility.1 According to the World Health Organization (WHO) 19992 and American Diabetes Association (ADA) 20243 criteria, prediabetes is defined as impaired fasting glucose (IFG), a condition that is characterized by the presence of a high level of fasting glucose. Fasting glucose (IFG), impaired glucose tolerance (IGT), or their mixed state (IFG+IGT). Epidemiologic research studies have shown that globally, the number of people with IFG and IGT is projected to grow from 298 million and 464 million in 2021 to 414 million and 638 million in 2045, respectively.4 Approximately 5–10% of patients with unintervened prediabetes progress to diabetes each year,5 with a 6-year conversion rate of up to 64.5%,6 and a 30-year cumulative prevalence of up to 95.9%.7 This stage is also strongly associated with an increased risk of cardiovascular disease,8 microangiopathy,9,10 tumors,11 dementia,12 and depression.13 Therefore, early screening and management of the prediabetic population is a key strategy to reduce the prevalence of T2DM.

Effective interventions can reduce the risk of prediabetes transformation, and the main strategies include lifestyle interventions and pharmacological interventions.14,15 For those who are not effective in lifestyle interventions, pharmacological intervention with metformin is recommended, which has been shown to reduce the risk of diabetes.16 However, it remains unclear whether the preventive effect of metformin differs across prediabetes subtypes (IFG, IGT, and combined IFG+IGT).17 This meta-analysis quantitatively compares, using interaction analyses, the effects of metformin on progression to type 2 diabetes across these three subtypes. In addition, it simultaneously examines both progression to T2DM and regression to normoglycemia as co‑primary outcomes, thereby offering a more comprehensive assessment of metformin’s net benefit. Accordingly, this meta-analysis aimed to evaluate the differential effects of metformin on the progression to type 2 diabetes across the three prediabetes subtypes based on the available evidence.

Methods

The protocol for this systematic evaluation was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420251020243). The review was conducted and reported in accordance with the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.

Literature Search

This study was systematically searched in PubMed, Web of Science, CINAHL, Scopus, Embase, Cochrane, WanFang, CNKI, VIP, Sinomed and other databases on March 28, 2023, and the core keywords included metformin, prediabetes, glucose intolerance, and fasting glucose Impaired. The complete search strategies for each database are provided in Supplementary Material S1. A combination of database search and manual citation tracing was used. Through the Boolean logic combination of subject terms and free terms, we screened the clinical research and systematic evaluation literature, and limited the English literature to ensure the quality of the data. A secondary search was conducted in May 2025, focusing on the supplementation of the latest research results to ensure the timeliness and comprehensiveness of the literature.

Literature Inclusion and Exclusion Criteria

Inclusion Criteria: The intervention population was prediabetic patients,18–20 with IFG or IGT or both, and older than 18 years of age; interventions were subject to a combination of comparisons of: the efficacy of a lifestyle intervention versus a combined lifestyle intervention with metformin; and randomized controlled trials comparing the difference in the efficacy of placebo versus metformin interventions, with no specific requirement for duration of intervention or frequency or dose of metformin; and no restriction on gender, race, geographic location, or duration of disease. The duration of the intervention and the specific frequency and dose of metformin are not explicitly required; there is no restriction on gender, race, geographic location, or duration of disease. The baseline characteristics of the trial will be analyzed to confirm that there is a good balance between the experimental group and the control group in terms of demographic indicators, clinical test values and metabolic parameters before the intervention (P>0.05); outcome indicators: the number of people with T2DM or the incidence rate of T2DM in each group should be clearly reported; no language restrictions were set when searching for and screening literature.

Exclusion criteria were as follows: studies with non-clinical trial designs, including systematic reviews, observational studies, non-randomized trials, animal studies, and other non-interventional research; studies comparing metformin exclusively with lifestyle interventions or involving combination therapy with other glucose-lowering medications; duplicate publications; and studies that did not report, or did not clearly specify, the conversion rate to T2DM.

Screening of Literature Results

The literature screening process was conducted independently by two researchers to ensure methodological rigor and objectivity. Screening was performed in two stages. First, the titles and abstracts of all retrieved records were assessed against the predefined inclusion and exclusion criteria. Second, the full texts of potentially eligible studies were reviewed to determine whether the study design, interventions, outcome measures, and data reporting were consistent with the PICO framework of this systematic review. Any disagreements between the two researchers were resolved through discussion and consensus. When consensus could not be reached, a third reviewer was consulted for arbitration. The full screening process, including reasons for exclusion, was documented and presented using a PRISMA flow diagram.

Data Extraction

For further reading of the literature that met the inclusion criteria, this study used a standardized data capture template for information extraction, which was designed by the researcher and obtained the consensus of the whole author team. The data collection dimensions covered four core modules: (1) basic information of the study (including first author, sample size, and intervention time); (2) basic characteristics of the study population; (3) characteristics of the intervention; (4) indicators of the intervention effect (including the incidence of the main outcome events).

Effectiveness Indicators

The outcome indicators in this study were dichotomous variables, so Relative Risk (RR) was used as the core effect size indicator for quantitative analysis. Based on the number of events and the corresponding sample sizes of each intervention and control group, the RR and its 95% confidence interval (CI) were used for effect size estimation. In order to rigorously control the effect of potential heterogeneity among the included studies, this research team assessed heterogeneity through the dual criteria of P value and I2 statistic of Cochran’s Q test: when the Q test showed P ≥ 0.10 (suggesting that there was no statistically significant heterogeneity) and I2 < 50% (indicating a low degree of heterogeneity), a fixed-effects model was used to synthesize the parameter; and if there was a P < 0.10 (presence of statistically significant heterogeneity) or I2 ≥50% (presence of substantial clinical heterogeneity), the random-effects model analytic framework was strictly maintained. Subgroup analyses were performed for analyses in which heterogeneity existed, with P<0.05 being considered a statistically significant difference. The prespecified subgroup analysis dimensions included type of prediabetes (IFG, IGT, and mixed). The methodological quality of the literature was evaluated using Review Manager 5.4 software, and the risk of bias assessment was schematically plotted.

Heterogeneity Assessment

The Cochrane Q-test for the calculation of cardinal statistics was used to assess whether there was statistical heterogeneity in the randomized controlled trials included. A p-value of less than 0.1 in the chi-square test indicates significant heterogeneity.

Sensitivity Analysis

A leave-one-out sensitivity analysis was performed to assess the influence of individual studies on the overall pooled estimate. Specifically, each included study was sequentially excluded, and the pooled effect size was recalculated for the remaining studies. This approach evaluated whether any single study disproportionately influenced the overall results, thereby testing the robustness and reliability of the meta-analysis findings.

Statistical Analysis

This study used the risk of bias 2.0 tool (RoB 2) recommended by the Cochrane Collaboration to evaluate the methodological quality of the included randomized controlled trials. Two independent researchers conducted the assessment based on the five core domains of the tool, and the assessment criteria included: (1) completeness of the randomization process (including sequence generation and allocation concealment) (selection bias); (2) compliance of intervention implementation and subject blinding (implementation bias); (3) standardization of outcome measures and blinding status (measurement bias); (4) completeness of the data (dropout rate and implementation of the intention-to-treat analysis) (follow-up bias); and (5) complete reporting of the prespecified outcome metrics (measurement bias). (follow-up bias); and complete reporting of prespecified outcome indicators (reporting bias). Supplementary analyses were also performed for unstructured sources of bias. In addition, funnel plots were generated to assess potential publication bias in the included studies.

Results Search results and Characteristics of Included Studies

A total of 5161 records were identified through the initial database search. After systematic screening, 3890 duplicate records were removed. The remaining 1271 records underwent further eligibility assessment, and 18 randomized controlled trials were ultimately included in this systematic review and meta-analysis. No additional eligible randomized controlled trials were identified through reverse citation tracking or supplementary search strategies. The study selection process was conducted in accordance with PRISMA guidelines, and the detailed screening procedure is presented in the flow diagram in Figure 1.

Figure 1 Flow chart of literature screening.

The meta-analysis included 18 randomized controlled trials. The baseline characteristics of the included studies and participants are summarized in Table 1. Risk of bias was assessed using the ROB-2 tool. Three RCTs were rated as having a low risk of bias, six RCTs were judged to have some concerns, and the remaining studies were assessed as having a high risk of bias, as detailed in Figure 2.

Table 1 Basic Characteristics of Included Studies

Figure 2 ROB-2 plot of the meta-analysis.

Analysis of Intervention Outcome Status results Development of T2DM

The meta-analysis showed that metformin use was associated with a significantly lower risk of progression to T2DM compared with the non-metformin control group (RR = 0.65, 95% CI [0.55, 0.77], P < 0.00001), corresponding to a 35% risk reduction. Moderate heterogeneity was observed across the included studies (I2 = 58%, P = 0.001); therefore, a random-effects model was used for data synthesis (Figure 3). Visual inspection of the funnel plot and Egger’s test suggested significant publication bias (P < 0.001) (Figure 4). Accordingly, the trim-and-fill method was applied to adjust for the potential impact of publication bias.

Figure 3 Forest plot of the subgroup analysis evaluating the impact of metformin on the risk of developing T2DM.

Figure 4 Funnel plot of the meta-analysis comparing the risk of T2DM incidence between metformin and control groups.

Subgroup analysis based on the underlying classification of prediabetes revealed differential risk reductions of metformin across subtypes. In the IGT subtype, metformin was associated with a 42% reduction in the risk of T2DM compared with non-use (RR = 0.58, 95% CI [0.43, 0.79], P = 0.0004). Substantial heterogeneity was observed (I2 = 64%, P = 0.003), and thus a random-effects model was applied, as detailed in Figure 5. In the IFG subtype, the preventive effect of metformin was more pronounced, with a 62% risk reduction (RR = 0.38, 95% CI [0.24, 0.61]; P < 0.0001). No heterogeneity was detected among the combined studies (I2 = 0%, P = 0.71), and therefore a fixed-effects model was used, as shown in Figure 6. The absence of heterogeneity (I2 = 0%) across IFG studies indicates consistent effect estimates, strengthening confidence in this finding. The larger risk reduction in IFG compared to IGT is consistent with metformin’s hepatic mechanism of action, as IFG primarily reflects hepatic insulin resistance. For the combined IFG and IGT subtype, metformin also demonstrated a clear protective effect, reducing the risk by 23% (RR = 0.77, 95% CI [0.70, 0.86], P < 0.00001). This result exhibited low heterogeneity (I2 = 15%, P = 0.32), and a fixed-effects model was similarly adopted, as illustrated in Figure 7.

Figure 5 Forest Plot of Subgroup Analysis Assessing the Impact of Metformin on the Risk of T2DM Onset in Patients with the IGT Subtypes.

Figure 6 Forest Plot of Subgroup Analysis Assessing the Impact of Metformin on the Risk of Developing T2DM in Patients with IFG Subtypes.

Figure 7 Forest Plot of Subgroup Analysis on the Effect of Metformin on the Risk of T2DM Onset in Patients with Mixed Prediabetic Status.

To investigate the potential sources of substantial heterogeneity observed in the IGT subgroup (I2 = 64%), subgroup analyses were conducted according to intervention duration (≤24 months vs >24 months). The findings indicated that intervention duration was an important contributor to heterogeneity. Among studies with intervention durations of ≤24 months, metformin reduced the risk of progression to T2DM (RR = 0.27, 95% CI [0.16, 0.44], P < 0.00001). No heterogeneity was detected in this subgroup (I2 = 0%); therefore, a fixed-effects model was used for synthesis, as shown in Figure 8. In studies with intervention durations exceeding 24 months, metformin continued to demonstrate a significant preventive effect, although the magnitude of risk reduction was smaller (RR = 0.84, 95% CI [0.77, 0.92], P = 0.0001). This subgroup showed low heterogeneity (I2 = 42%), and a fixed-effects model was also applied, as presented in Figure 9. These findings suggest that the long-term preventive effect of metformin may be somewhat attenuated compared with its short-term effect.

Figure 8 Forest plot describing the incidence of T2DM for IGT subtypes with an intervention time ≤24 months.

Figure 9 Forest plot describing the incidence of T2DM among IGT subtypes with an intervention time >24 months.

To further assess the influence of intervention duration on the overall preventive effect of metformin, a subgroup analysis was conducted among all participants with prediabetes according to intervention duration (≤24 months vs >24 months). The results showed that a shorter intervention duration was associated with a greater magnitude of risk reduction. Among participants with an intervention duration of ≤24 months, metformin reduced the risk of progression to T2DM by 66% (RR = 0.34, 95% CI [0.25, 0.46], P < 0.00001). No heterogeneity was observed in the pooled studies (I2 = 0%); therefore, a fixed-effect model was applied, as shown in Figure 10. Among participants with an intervention duration of >24 months, the risk reduction was 26% (RR = 0.82, 95% CI [0.76, 0.87], P < 0.00001), with low heterogeneity across the pooled studies (I2 = 34%). A fixed-effect model was also used for this analysis, as presented in Figure 11.

Figure 10 Forest plot describing the incidence of T2DM among the overall prediabetic population with an intervention time ≤24 months.

Figure 11 Forest plot describing the incidence of T2DM among the overall prediabetic population with an intervention time >24 months.

Return to Normoglycemia

The meta-analysis showed that metformin intervention increased the proportion of individuals with prediabetes who reverted to normoglycemia. Specifically, normoglycemia restoration occurred in 32.2% of participants in the intervention group compared with 21.7% in the control group. The pooled analysis indicated that metformin was associated with a significantly higher likelihood of normoglycemia restoration than control treatment (RR = 1.80, 95% CI [1.39, 2.34], P < 0.00001), representing an 80% increase. Given the substantial heterogeneity among the included studies (I2 = 58%, P = 0.01), a random-effects model was used for the analysis, as shown in Figure 12.

Figure 12 Forest plot describing the incidence of recovery from a prediabetic state to a normoglycemic state.

To explore the substantial heterogeneity observed in the outcome of normoglycemia restoration (I2 = 58%), a subgroup analysis was conducted according to intervention duration. After excluding one study with an intervention duration exceeding 24 months, the subgroup analysis of studies with intervention durations of ≤24 months showed a marked reduction in heterogeneity. In this subgroup, metformin use was associated with a significantly higher likelihood of normoglycemia restoration among individuals with prediabetes compared with non-metformin control treatment (RR = 2.05, 95% CI [1.68, 2.51], P < 0.00001). Low heterogeneity was observed across the pooled studies (I2 = 16%, P = 0.30); therefore, a fixed-effects model was applied, as shown in Figure 13.

Figure 13 Forest plot describing the incidence of return from prediabetic to normal state at intervention time ≤24 months.

Maintained in Prediabetic State

The pooled analysis revealed no significant difference between the metformin group and the control group in delaying the progression of prediabetes. Specifically, 51.6% of the patients in the intervention group remained in the prediabetic state, which was comparable to 50.2% in the control group. A synthesis of the data indicated that the effect of metformin on maintaining the prediabetic state was not statistically significant (RR = 0.92, 95% CI [0.70, 1.21], P = 0.57). Due to the high heterogeneity observed (I2 = 82%, P = 0.00001), a random-effects model was applied, as detailed in Figure 14. Substantial heterogeneity was observed (I2 = 82%, P < 0.00001). This high heterogeneity may arise from several sources: differences in prediabetes diagnostic criteria across studies, wide variation in follow‑up duration (6 to 36 months), and diversity in baseline patient characteristics (eg, age, BMI, ethnicity). Given this substantial and unexplained heterogeneity, a random‑effects model was applied, which accounts for both within‑study and between‑study variance and provides more conservative and generalizable estimates than a fixed‑effect model.

Figure 14 Forest plot describing maintenance in a prediabetic state.

To clarify the substantial heterogeneity observed for the outcome of maintenance in the prediabetic state (I2 = 82%), a series of exploratory analyses were conducted. First, subgroup analyses were performed according to intervention duration (≤24 months vs >24 months); however, no meaningful reduction in heterogeneity was observed. Subsequent subgroup analyses based on baseline prediabetes subtype, including IFG and IGT, similarly failed to explain the heterogeneity. Finally, a leave-one-out sensitivity analysis was conducted by sequentially excluding each independent study. The pooled estimates and I2 values remained largely unchanged, indicating that the substantial heterogeneity was not driven by any single study.

Discussion

This systematic review and meta-analysis demonstrated that the preventive effect of metformin differs across prediabetes subtypes. The pooled analysis showed that metformin reduced the risk of progression to T2DM by 35%, with moderate heterogeneity across the included studies. Subgroup analyses further indicated that individuals with IFG appeared to derive a greater risk reduction than those with IGT, and this finding remained robust in sensitivity analyses. This effect size disparity consideration may be related to pathophysiological mechanisms. IFG is centrally characterized by abnormal hepatic glucose output, and metformin specifically inhibits key enzymes of gluconeogenesis, such as phosphoenolpyruvate carboxykinase (PEPCK), through activation of the AMPK pathway,39 and its hepatic drug concentration of up to 10–100-fold of the plasma40 further strengthens the targeting effect; whereas IGT mainly originates from skeletal muscle insulin resistance,41 but the limited distribution of metformin in peripheral tissues,42 resulting in a relatively weak intervention effect. In addition, analysis of intervention duration showed that ≤24 months of short-term treatment was more effective in the IGT population, suggesting differences in the duration of different targets of action. Although the rate of maintenance of prediabetes status was not statistically different between the two groups, and there was a high degree of heterogeneity, which could not be eliminated even with subtype analysis by intervention duration stratification and different stratification of prediabetes status, possibly reflecting the presence of unrecognized heterogeneous pathways in disease progression.

Comparisons across racial studies found that the results of this study were highly consistent with subgroup analyses of the Diabetes Prevention Program (DPP) study in the United States, which both showed a significant preventive effect of metformin on IFG.43 However, some Asian cohort studies showed different trends, suggesting that racial differences may influence drug response. Further analyses suggest that Asian populations have a relatively low functional reserve of β-cells, resulting in a greater susceptibility to glucose regulation dysregulation at the same level of insulin resistance, which may partially offset the hepatoprotective effects of metformin.

The clinical relevance of our subtype‑specific findings is underscored by recent global epidemiological projections. According to Rooney et al, the number of people with IFG worldwide is projected to increase from 298 million in 2021 to 414 million by 2045, and the number with IGT from 464 million to 638 million over the same period.4 Given that the global burden of both subtypes is rising substantially, our finding that metformin confers a larger risk reduction in IFG (RR = 0.38) than in IGT (RR = 0.58) has important implications. For the rapidly growing IFG population, metformin could be a highly efficient preventive intervention, especially where lifestyle modification alone is insufficient. Conversely, the even larger projected IGT population may require additional or alternative strategies beyond metformin, such as more intensive lifestyle support or combination therapies targeting peripheral insulin resistance. Thus, integrating subtype‑specific efficacy data with epidemiological forecasts can help guide resource allocation and personalized prevention strategies on a population level.

At the level of clinical translational application, this study emphasizes the necessity of prediabetes typing. For patients with simple IFG, metformin can be prioritized as an adjunctive intervention on the basis of intensive lifestyle intervention; while for patients with IGT or mixed IFG/IGT, stricter lifestyle management or the combined use of drugs to improve peripheral insulin sensitivity, such as thiazolidinediones, is needed. This type of intervention strategy is highly consistent with the concept of “individualized prevention” recommended by the latest international guidelines, which helps to optimize the allocation of resources and improve the preventive effect. It is worth noting that the reversal analysis of glucose tolerance showed a recovery rate of 32.2% in the metformin group, especially in the ≤24-month intervention, suggesting that early and short-term intervention may reshape metabolic homeostasis.

Limitations and Strengths

In this study, the robustness of the results was verified by sensitivity analyses and subgroup analyses based on antecedent diabetes subtypes (eg, IFG, IGT), with the following limitations: (1) reliance on published aggregate data and a lack of individual patient information to correct for potential confounding variables (eg, medication adherence, concurrent lifestyle changes); (2) differences in the diagnostic criteria for “prediabetes” across the publication years of the included studies, which may have affected the comparability of effect sizes; (3) inconsistency in control interventions across studies, with some trials using lifestyle intervention as the background control and others using placebo alone; (4) high heterogeneity that could not be fully explained for some outcomes – for example, the I2 value for the outcome of “maintaining a prediabetic state” was 82%, and neither subgroup analyses nor sensitivity analyses eliminated this heterogeneity. Additionally, differences in effect size comparisons were observed across studies.

Conclusion

This meta-analysis of 18 RCTs shows that metformin reduces the risk of progression to T2DM in prediabetic populations (RR = 0.65; 95% CI: 0.55–0.77). The effect is largest in the IFG subtype (RR = 0.38; 95% CI: 0.24–0.61), compared to IGT (RR = 0.58; 95% CI: 0.43–0.79) and combined IFG+IGT (RR = 0.77; 95% CI: 0.70–0.86). These subtype differences are statistically significant. The results of this study emphasize the importance of accurate metabolic phenotype‑based staging in clinical decision‑making. For individuals with IFG who do not achieve sufficient risk reduction with lifestyle interventions, metformin may be considered as an adjunct, given the larger relative risk reductions observed in this subgroup. In the future, multicenter randomized controlled trials are needed to verify the heterogeneity of drug responses among different glucose metabolism phenotypes and to explore biomarker-guided individualized prevention strategies.

Data Sharing Statement

All published studies included in this systematic review/meta-analysis are publicly available via the database links provided in the original articles. To ensure the reproducibility of this research, relevant supplementary materials have been made publicly available as follows: the data extraction template used in this study, the complete literature search strategy, and the aggregated data used for all analyses have been submitted as supplementary files alongside this paper. Additional relevant data are available from the corresponding author upon reasonable request.

Author Contributions

Xia Qian; Conceptualization, Methodology, Formal analysis, Writing - original draft.

Bing Wang; Conceptualization, Methodology, Data curation, Writing – review & editing.

Xiaohong Yang; Conceptualization, Formal analysis, Writing – review & editing.

Bo Qian; Conceptualization, Formal analysis, Writing – review & editing.

Qun Zhang; Validation, Visualization, Writing – review & editing.

Junjun Guo; Validation, Visualization, Writing – review & editing.

Guanzhen Jia; Validation, Visualization, Writing – review & editing.

Tianmei Lin; Validation, Visualization, Writing – review & editing.

All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The authors declare that no funding was received for this study.

Disclosure

The authors declare that there are no conflicts of interest in this study. A version of this manuscript was made available as a pre-print: https://www.researchsquare.com/article/rs-6620890/v1.

References

1. Tabák AG, Herder C, Rathmann W, et al. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279–14. doi:10.1016/S0140-6736(12)60283-9

2. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–553. doi:10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S

3. ElSayed NA, Aleppo G, Bannuru RR; American Diabetes Association Professional Practice Committee. 2. diagnosis and classification of diabetes: standards of care in Diabetes—2024. Diabetes Care. 2024;47(Suppl 1):S20–S42. doi:10.2337/dc24-S002

4. Rooney MR, Fang M, Ogurtsova K, et al. Global prevalence of prediabetes. Diabetes Care. 2023;46(7):1388–1394. doi:10.2337/dc22-2376

5. Chinese Society of Endocrinology, Chinese Society of Diabetes, Chinese Physicians Association of Endocrinology and Metabolism. Expert consensus on prediabetes intervention in Chinese adults (2023 edition). Chin J Diab. 2023;15(6).

6. de Vegt F, Dekker JM, Jager A, et al. Relation of impaired fasting and postload glucose with incident type 2 diabetes in a dutch population: the Hoorn study. JAMA. 2001;285(16):2109–2113. doi:10.1001/jama.285.16.2109

7. Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the china da qing diabetes prevention study: a 20-year follow-up study. Lancet. 2008;371(9626):1783–1789. doi:10.1016/S0140-6736(08)60766-7

8. Cai X, Zhang Y, Li M, et al. Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ. 2020;370:m2297. doi:10.1136/bmj.m2297

9. Chen C, Liu G, Yu X, et al. Association between prediabetes and renal dysfunction from a community-based prospective study. Int J Med Sci. 2020;17(11):1515–1521. doi:10.7150/ijms.46477

10. Gujral UP, Jagannathan R, He S, et al. Association between varying cut-points of intermediate hyperglycemia and risk of mortality, cardiovascular events and chronic kidney disease: a systematic review and meta-analysis. BMJ Open Diabetes Res Care. 2021;9(1):e001776. doi:10.1136/bmjdrc-2020-001776

11. Lin J, Tu R, Lu Z. Prediabetes and the risk of breast cancer: a meta-analysis. Front Oncol. 2023;13:1238845. doi:10.3389/fonc.2023.1238845

12. Wang Q, Li X, Teng N, et al. Prediabetes and the incidence of dementia in general population: a systematic review and meta-analysis of prospective studies. Psychogeriatrics. 2022;22(5):666–678. doi:10.1111/psyg.12869

13. Yu Y, Wan W. Association between prediabetes and depression: a meta-analysis. PLoS One. 2024;19(8):e0307428. doi:10.1371/journal.pone.0307428

14. Parajuli S, Jasmin G, Sirak H, et al. Prediabetes: adherence to nutrition visits decreases HbA1c in children and adolescents. Front Endocrinol. 2022;13:916785. doi:10.3389/fendo.2022.916785

15. Echouffo-Tcheugui JB, Perreault L, Ji L, Dagogo-Jack S. Diagnosis and management of prediabetes: a review. JAMA. 2023;329(14):1206–1216. doi:10.1001/jama.2023.4063

16. Patel D, Ayesha IE, Monson NR, et al. The effectiveness of metformin in diabetes prevention: a systematic review and meta-analysis. Cureus. 2023;15(9):e46108. doi:10.7759/cureus.46108

17. Santaguida PL, Balion C, Hunt D, et al. Diagnosis, prognosis, and treatment of impaired glucose tolerance and impaired fasting glucose. Evid Report/Technol Assessm. 2005;(128):1–11.

18. Anonymous. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 1997;20(7):1183–1197. doi:10.2337/diacare.20.7.1183

19. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care. 2003;26 Suppl 1(suppl_1):S5–20. doi:10.2337/diacare.26.2007.S5

20. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33 Suppl 1(Suppl 1):S62–69. doi:10.2337/dc10-S062

21. Ma W, Wang G, Lu F, et al. Therapeutic efficacy of metformin intervention in IGT. China Chronic Dis Prevent Control. 1999;7(3).

22. Li N, Wu S, Hu X, et al. Intervention of metformin in patients with impaired glucose tolerance. China Chronic Dis Prevent Control. 2004;12(4).

23. Wang C, Zhang L. Clinical observation of metformin in the treatment of prediabetes. Shanxi Med J. 2009;38(z1).

24. Rong QF. Observations on the intervention treatment of metformin in patients with impaired glucose tolerance. J Shanxi Med University. 2008;39(11).

25. Chen Z, Sun X, Ou Y, et al. Therapeutic effects of metformin in patients with abnormal glucose tolerance. Shandong Med. 2009;49(17).

26. Ke J. Clinical study of metformin in the treatment of impaired fasting glucose. Pharmaceut Industry News. 2006;3(17).

27. Ma L, Zhou J, Ding J. Intervention effect of metformin on obese hypoglycemia. Hebei Med. 2002;24(4):258–259.

28. Jia K, Wang L. Observation on the efficacy of metformin extended-release tablets in the treatment of impaired fasting glucose regulation. Chin J Continuing Med Educ. 2011;34(15):69–70.

29. Zhang W. Clinical observation of metformin tablets in the treatment of prediabetic cases. Chin Med Innov. 2012;9(29):34–35.

30. Pan S, Yu Z, Yan Q, et al. Intervention effect of metformin on impaired fasting glucose. Anhui Med. 2008;29(2):73–74.

31. Li CL, Pan CY, Lu JM, et al. Effect of metformin on patients with impaired glucose tolerance. Diabet Med. 1999;16(6):477–481. doi:10.1046/j.1464-5491.1999.00090.x

32. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New Engl J Med. 2002;346(6):393–403.

33. O’Brien MJ, Whitaker RC, Yu D, et al. The comparative efficacy of lifestyle intervention and metformin by educational attainment in the diabetes prevention program. Preventive Med. 2015;77:125–130. doi:10.1016/j.ypmed.2015.05.017

34. Zhang L, Zhang Y, Shen S, et al. Safety and effectiveness of metformin plus lifestyle intervention compared with lifestyle intervention alone in preventing progression to diabetes in a Chinese population with impaired glucose regulation: a multicentre, open-label, randomized controlled trial. Lancet Diab Endocrinol. 2023;11(8):567–577.

35. Umar MT, Sabir AA, Sada KB, et al. Effect of exercise versus metformin among nigerians with prediabetes: a randomized controlled trial. West Afr J Med. 2024;41(5):499–504.

36. O’Brien MJ, Perez A, Scanlan AB, et al. PREVENT-DM comparative effectiveness trial of lifestyle intervention and metformin. Am J Preventive Med. 2017;52(6):788–797. doi:10.1016/j.amepre.2017.01.008

37. Herman WH, Pan Q, Edelstein SL, et al. Impact of lifestyle and metformin interventions on the risk of progression to diabetes and regression to normal glucose regulation in overweight or obese people with impaired glucose regulation. Diabetes Care. 2017;40(12):1668–1677. doi:10.2337/dc17-1116

38. Goldberg RB, Bray GA, Marcovina SM, et al. Non-traditional biomarkers and incident diabetes in the diabetes prevention program: comparative effects of lifestyle and metformin interventions [J]. Diabetologia. 2019;62(1):58–69. doi:10.1007/s00125-018-4748-2

39. Madiraju AK, Erion DM, Rahimi Y. Metformin suppresses gluconeogenesis by inhibiting mitochondrial glycerophosphate dehydrogenase. Nature. 2014;510(7506):542–546. doi:10.1038/nature13270

40. Froldi G. View on metformin: antidiabetic and pleiotropic effec