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Impact of duration of treatments with metformin and sulfonylureas, individually or in combination, on diabetic retinopathy among newly diagnosed type 2 diabetic patients: a pooled cohort’s analysis

International Journal of Retina and Vitreous volume 11, Article number: 9 (2025) Cite this article

A Correction to this article was published on 12 March 2025

This article has been updated

Abstract

Background

This study aimed to evaluate the effect of metformin and sulfonylurea (SUs) medication time on Diabetic retinopathy (DR) among newly diagnosed patients with type 2 diabetes (T2DM) using a pooled analysis. This study aimed to evaluate the effect of metformin and SUs’ medication time on DR among newly diagnosed T2DM using a pooled analysis.

Methods

The data of 4,068 newly diagnosed DM individuals(mean age, 60.2 ± 0.85 years) from three prospective cohorts of Tehran Lipid and Glucose Study (TLGS), Multi-Ethnic Study of Atherosclerosis (MESA), and Atherosclerosis Risk in Communities (ARIC) with a mean age of 59.6 ± 08 years were pooled. The cumulative exposure to metformin, SUs, aspirin, statin, and anti-hypertensive medication was also determined using the same approach. The Cox proportional hazards (CPH) model was used to calculate the hazard ratio (HR) (95% CI) for the outcomes while adjusting for confounding factors such as fasting Blood Sugar (FBS), age, statin, aspirin, and anti-hypertensive medications.

Results

During follow-up, DR occurred in 519 DM. Metformin alone, SUs alone, and the combination of both reduced the hazard of DR by 10%, 7%, and 11% for each year of use, respectively (p < 0.05). The protective effect of metformin and SUs, individually or in combination, on DR started approximately five years after the initial treatment and continued until approximately 15 years after the initial treatment and then reached a plato.

Conclusion

Long-term treatment with metformin and SUs, individually and in combination, was associated with a reduced risk of DR in people with newly diagnosed diabetes for up to a decade compared with no treatment. These findings highlight the protective role of metformin and sulfonylureas as inexpensive and readily available drugs to prevent DR in people with newly diagnosed diabetes.

Introduction

Diabetes mellitus (DM), with 537 million adults between 20 and 79 years of age in 2021, is one of the most common endocrine diseases and is recognized as one of the major public health challenges worldwide [1,2,3,4]. According to recent studies, the incidence of diabetes is increasing, and it is predicted that this number will increase to 783 million people by 2045 [3, 5]. DM can lead to serious effects on the vital organs of the body and extensive outcomes, including the development of microvascular and Macrovascular complications [6].

Diabetic retinopathy (DR) is one of the most common microvascular complications related to DM, which is still one of the most common causes of blindness and vision loss in DM patients worldwide [7, 8]. With the increase in the prevalence of DM and the increase in years of life, the incidence of DR also increases significantly, which imposes a social and financial burden on any healthcare system [6, 7, 9,10,11]. More than 100 million people worldwide live with DR [12, 13]. DR occurs in approximately 30–40% of DM individuals [8, 13].

Many cases of vision loss caused by DR can be controlled with glycemic control [8, 14, 15]. Managing DM can decrease or postpone patient complications significantly [16]. High blood sugar and changes in metabolic pathways result in oxidative stress and the onset of nerve cell degeneration in the early phase of DR [17,18,19]. Damage to the blood vessels, the formation of small bulges in the blood vessels, and small areas of bleeding within the retina are initial indicators of non-proliferative diabetic retinopathy (NPDR). As the condition advances, the narrowing of blood vessels and blockages lead to twisted capillaries and reduced blood supply to the retina. In the late stage of DR, severe lack of oxygen leads to the growth of new blood vessels, bleeding into the vitreous, and detachment of the retina [17, 20, 21]. The growth of abnormal blood vessels in the retina can cause DR to be either proliferative (growing) or nonproliferative (not growing). Nonproliferative retinopathy, which is more common, may not necessitate treatment [22].

Metformin, the sole biguanide in oral antidiabetic (OAD) medications, is commonly utilized as the initial OAD in treating diabetic individuals due to extensive proof of its efficiency over the long term and its relatively lower occurrence of side effects, such as hypoglycemia, compared to the sulfonylurea (SUs) group [23]. Nevertheless, administering metformin alone to patients whose hemoglobin A1C (HbA1c) is < 7.5 could result in inadequate diabetes management. Thus, it should be combined with SUs drugs when prescribed to patients [24, 25]. Patients recently diagnosed with diabetes and whose condition is relatively steady are frequently managed by primary care physicians [26]. Therefore, for newly diagnosed DM patients, physician’s providers may prescribe oral antidiabetic drugs (OADs) like metformin and SUs based on various considerations [26]. These medications can benefit these patients by decreasing clinical indicators such as HbA1c levels or the risks of diabetes-related complications [27,28,29].

The efficacy of metformin and SUs has been demonstrated in various trials to decrease the microvascular complications and mortality linked to DM [30,31,32,33]. However, these trials are conducted under specific conditions and with specific population characteristics. Typically, the duration between diabetes onset and treatment initiation is uncertain in these populations, so the findings of these trials may not accurately reflect the drug’s effectiveness in the general population [34, 35]. In typical scenarios in local areas and the primary care facilities of each nation, these medications are frequently recommended for senior patients who are simultaneously dealing with multiple other illnesses for extended durations alongside other medications. The outcomes of taking these medications concurrently with other drugs in the community over the long term vary significantly from those observed in RCTs [34,35,36,37,38,39].

The effect of the duration of medication with metformin and SUs on DR by the duration of DM in long-term follow-up is still unclear. Therefore, considering the importance of this issue, this study, for the first time, evaluated the effect of medication time of metformin and SUs on DR by the duration of DM in newly diagnosed DM individuals with a pooled analysis of three prospective cohort studies.

Methods

Study design, setting, and population

The study, which accessed cohort data, was approved by the ethics committee of Shahid Beheshti University of Medical Sciences and the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). In this observational study, data from diabetic patients registered and followed up in the Tehran Lipid and Glucose Study (TLGS), Multi-Ethnic Study of Atherosclerosis (MESA), and Atherosclerosis Risk in Communities (ARIC) cohorts were extracted and combined.

During follow-up, patients with fasting blood sugar (FBS) levels greater than 126 mg/dL or those taking metformin, SUs, or a combination of both were defined as newly diagnosed DM individuals. Patients who had an FBS ≥ 126 at the first examination or were taking any antidiabetic medication were defined as known diabetes and excluded. Ultimately, 4,068 newly diagnosed DM individuals were included in the three cohorts during the follow-up period (Fig. 1).

Fig. 1
figure 1

Flowchart of cohorts included in this study

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Inclusion and exclusion criteria

Inclusion criteria include patients with a definitive diagnosis of type 2 diabetes based on FBS ≥ 126, age ≥ 40 years, use of metformin or SUs (Glibenclamide or Gliclazide) or a combination of both, knowing the time of diabetes diagnosis, knowing the time of treatment initiation, knowing the time of medication time, knowing the time of occurrence of microvascular complications. Patients whose FBS levels were missing in each phase, concurrent use of other antidiabetic drugs (other OADs such as SGLT2 inhibitors or insulin), type 1 diabetes, suffering from any eye disorders before starting use OAD, diabetes known at the first examination and incomplete medical profile of patients were defined as exclusion criteria.

Data management and extraction

Initially, a team of experts in epidemiology and endocrinology developed a three-part checklist for identifying the specific variables in the cohorts. The specific variables identified were determined based on the literature review and the study’s objectives. The definition and categorization of each variable were checked and validated across various cohorts. Because there were multiple definitions for each variable in each cohort, the same definition was used to extract each variable from all three cohorts. The most common definition between cohorts was used as the final definition to derive each variable.

The extracted variables in each cohort include four sections of demographic characteristics (age, sex, cohort start time, start time of each exam, end time of each exam, number of exams, sex, education level, smoking, body mass index (BMI), waist circumference (WC), marital status and physical activity (minutes per week)), clinical and laboratory findings (FBS, Triglyceride, cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), systolic and diastolic blood pressure, creatinine, other medication time (aspirin, statin and anti-hypertensive), cumulative exposure (metformin, SUs and combination of both medication time (Year)) and outcome (DR).

Diabetes duration calculation

The time to enter each exam in a given cohort and the interval between the two exams were calculated. Of course, the duration of each exam in the cohorts was calculated as an independent variable. The following steps were followed to calculate the desired times (the duration of diabetes until the outcomes occur and medication time). First, the duration of diabetes was calculated. Considering that all patients were healthy at the time of entry, if a person’s FBS was > 126 in the subsequent exam, the duration of diabetes was equal to 1/2 the duration of the previous exam until the outcome or the end of the cohort. For example, if a person’s FBS in exam 3 was > 126, the diabetes duration was equal to 1/2 the time of exam two until the occurrence of the outcome or the end of the cohort. Second, the time interval until the outcome occurs is calculated by subtracting the time of diabetes diagnosis from the occurrence of outcomes. Third, the time from the start of taking the medications to the time of changing or stopping the medications is defined as the medication time (cumulative exposure). For example, if a person was taking metformin in the current exam (For example, exam 2) but was taking SUs in the next exam, the metformin duration was equal to the total duration of exam 2 + 1/2 the duration of exam 3, with the assumption that the person will be half of the following exam has also taken metformin.

The method of calculating cumulative exposure and its underlying assumptions were consistent across all three cohorts and for all medications (metformin, SUs, combination of both, statin, anti-hypertensive, and aspirin).

Data for all time-dependent variables were collected for all exams, and the mean values from the time of diabetes diagnosis to the occurrence of the outcome were adjusted. This included the mean age, BMI, lipid profile (HDL, LDL, triglycerides, and cholesterol), FBS, waist circumference, physical activity, systolic and diastolic blood pressure, and creatinine up to the occurrence of any outcome or the last follow-up period. Cumulative exposure to statins, anti-hypertensive medications, and aspirin was also calculated.

Exposure and outcome

The medication time, including metformin alone, SUs alone, and combinations of both, represented the exposure in patients during the cohorts. Since all individuals entering exam one were diabetes-free, the second exam of each cohort was considered the baseline for OAD consumption. The duration of each medication was calculated individually, and the interaction between metformin and SUs was estimated separately.

Diabetic retinopathy was the study outcome. Eye damage caused by T2DM (growth of abnormal blood vessels in the retina (proliferative and nonproliferative)) in one or both eyes was defined as DR based on an ophthalmologist’s diagnosis or laser eye surgery due to diabetes [22]. Diabetic retinopathy in the cohorts was performed based on fundus photography at each site by an ophthalmologist following a standard protocol. Screening periods in the cohorts were, on average, every 2 to 3 years.

Statistical analysis

Stata statistical software version 17 was used for data analysis. Qualitative variables were described using % and frequency. Quantitative variables such as medication time (years), FBS, age, BMI, waist, lipid profile, blood pressure, disease duration, and creatinine level were presented with mean, quartiles, and standard deviation. The Cox proportional hazards (CPH) model was initially used to examine the linearity assumption in the relationship between exposure and outcomes using multivariable fractional polynomial (MFP) analysis. As a linear relationship between exposure and outcomes could not be established, the multivariable modeling with Cubic regression splines (MVRS) package was employed for the CPH analysis. Then, according to the method of P Royston et al. [40] With the fracplot command, the trend of changes in the hazard of occurrence of each outcome was plotted against the duration of medications. The number of knots was determined, and the slope of the outcome hazard changes with the medication time was estimated.

To assess the net effect of metformin and SUs, the separate impacts of each medication and the combined impact of both were computed using the Lincom package. The magnitude of the effect of exposure on each result was assessed in three different models. The first model was considered unadjusted (duration of metformin, SUs, and a combination of both). The second model included the crude model adjusted for the mean age and the mean FBS from the time of DM diagnosis to the occurrence of the outcome. The third model included model 2, adjusted for the variables of sex, smoke, waist, marital status, HDL, LDL, TG, education level, physical activity, cumulative exposure to other treatments (aspirin, statin, and anti-hypertensive), creatinine, and blood pressure. The impact of metformin and SUs medication timing on outcomes was reported using an adjusted hazard ratio (HR) with a 95% confidence interval (95% CI). A p-value of less than 0.05 indicated statistical significance.

Results

Demographic, laboratory, clinical characteristics, and mean medication time

The pooled mean age of patients at the time of DM diagnosis and during treatment in three cohorts (4,068 newly diagnosed DM) was 49.8 ± 1.3and 60.2 ± 0.85 years, respectively. 1969 (46.3%) of the patients were male. The mean FBS at the diagnosis was 159.6 ± 1.54 (range of 126.5 to 488). The mean physical activity of the patients during the follow-up was 75.2 ± 0.26 m/w. The history of aspirin and anti-hypertensive use was reported in 83.8% and 73.3% of patients, respectively. The mean medication time for metformin, SUs, and combination was 5.22 ± 0.41, 5.88 ± 0.6, and 4.88 ± 0.8, respectively. The mean FBS at the time of diagnosis and during the study was 160.6 ± 1.59and 148.6 ± 2.1 (mg/dl), respectively. (Table 1) The median follow-up was 15.84 ± 0.39 years.

Table 1 Demographic, laboratory, clinical characteristics, and duration of medication use in 4,253 newly diagnosed type 2 diabetes patients
Full size table

The mean FBS in patients who did not receive any antidiabetic medication during the study was significantly higher than the patients who received at least one antidiabetic medicine. No significant difference was observed between demographic and laboratory characteristics among the recipients of treatment regimens (Supplement 1-Table 1).

Diabetic retinopathy

During follow-up, DR occurred in 519 newly diagnosed DM individuals. The mean time from diagnosis of DM to DR was 14.9 ± 0.18 years. Metformin and SUs, both alone and in combination in models 1 and 2, significantly reduced the risk of DR. In model 3, metformin alone (HRAdj: 0.90, 95% CI: 0.82, 0.98, P: 0.011), SUs alone (HRAdj: 0.93, 95% CI: 0.87, 0.99, P: 0.034) and their combination (HRAdj: 0.89, 95% CI: 0.80, 0.98, P: 0.001) significantly reduced the risk of DR. Also, the estimated effect of exposure to the outcome in the Fine-Gray model (competing risk) and the CPH model were almost similar (Table 2).

Table 2 The hazard ratio of the duration of using metformin and sulfonylurea on diabetic retinopathy
Full size table

MVRS analysis adjusted for all variables showed that although the duration of use of all three treatment groups (individually and in combination) was significantly related to reducing the risk of DR, this relationship was not linear, and its reduction effect was different depending on the medication time. The protective effect of metformin on DR started almost 5 years after the initiation therapy, continued up to 12 years, and then remained plato (Fig. 2- Fracplot A). The protective effect of SUs medication on DR started approximately after 6 years and continued until 10 years after consumption, and then remained Plato (Fig. 2- Fracplot B). The protective effect of combination medication on DR started after 4 years and continued until 13 years after use. (Fig. 2 - Fracplot C)

Fig. 2
figure 2

Fracplots show the hazard of DR for metformin (A), sulfonyl (B), and a combination of sulfonyl and metformin (C) over time

Full size image

Assuming a direct association between exposure and outcome and adjusting for all variables, metformin use resulted in a median 15% reduction in the risk of DR during follow-up (HRAdj: 0.85, 95% CI: 0.79, 0.93, P: 0.001). Using SUs reduced the risk of DR by an average of 13%. (HRAdj: 0.87, 95% CI: 0.80, 0.95, P: 0.001).

In addition, multivariate analysis showed that increasing mean age, FBS, BMI, and smoking were significantly related to increased DR hazard. At the same time, the use of Statins had a protective role (Data not shown).

Discussion

In this study, using the data of 3 prospective cohorts, we evaluated the effect of metformin and SUs’ medication time on DR based on the duration of T2DM for the first time in 4,253 newly diagnosed DM individuals.

Our study showed that metformin and SUs, both individually and in combination, adjusted for other variables significantly reduced the risk of DR. The protective effect of metformin and SUs on DR started after a certain period of referees and continued for almost up to 10 years with different slopes. During follow-up, metformin and SUs reduced the risk of microvascular outcomes by an average of 15% and 13%, respectively. The protective effect of metformin and SUs alone and their combination on DR started approximately 5 years after initial treatment. Then, they continued with different gradients up to 15 years after initial treatment. The lack of effect of metformin and SUs (individually or in combination) on reducing the risk of DR in the first five years after the start of treatment in our study may be due to the low risk of this complication in the first years of DM. Because in this study, we evaluated newly diagnosed DM individuals, the risk of DR in these patients in the first years of the disease is intrinsically low, even without receiving medication. In the long term and with the increased risk of DR in DM, metformin and sulfonylureas had a protective effect on DR. The protective effect of metformin alone on DR started 5 years after the initiation of therapy, and its effect on reducing the risk of DR continued up to 12 years after use. The protective effect of SUs alone and their combination on reducing the risk of DR started almost 5 years after initiation therapy. The cohorts studied in this study were based on specific populations with specific characteristics, which were predominantly urban and had better access to health services than rural or deprived populations, and caution should be exercised in generalizing the results of this study to other populations. In addition to better access to health services, the patients studied in these studies may also differ in several other key variables that may be associated with the occurrence and progression of DR. Replication of these studies in other populations, especially deprived populations, may be associated with different results.

Several previous studies have reported the protective effect of metformin and SUs on diabetic retinopathy in DM [41,42,43,44,45,46]. However, according to our knowledge, the effect of these drugs based on the medication time in newly diagnosed DM individuals has not been reported. Therefore, these results still need to be discussed and are not definitive.

In line with the results of our study in 2022, Y Li et al., [42] showed that long-term use of metformin and SUs in DM significantly reduced the rate of severe nonproliferative or proliferative diabetic retinopathy (SNPDR/PDR). They showed that the odds ratio of SNPDR/PDR in patients who use metformin and SUs for a long time compared to diabetic patients who did not take antidiabetic drugs was 0.37 and 0.45, respectively. In another study, R Gabriel et al., [33] showed that glucose-lowering medications, including metformin, were significantly associated with reduced microvascular outcomes in people with prediabetes. The UK Prospective Diabetes Study [47] showed that sulfonylureas significantly reduced the risk of DR, in 3,867 newly diagnosed type 2 diabetes patients.

YP Fan et al., [43] showed that metformin significantly reduced the risk of NPDR in DM patients. They suggested that early administration of metformin can reduce the risk of DR in DM. They also showed that the protective effect of metformin in combination with other antidiabetics, including DPP-4i, was more significant in reducing the risk of NPDR. In our study, the protective effect of metformin against DR in combination with SUs was more significant than its individual effect. In a cohort study, AJ Barkmeier et al., [48] investigated the effectiveness of SUs on sight-threatening diabetic retinopathy (STDR) in 513,197 new DM patients. They showed that SUs were associated with a 39% reduction in the risk of STDR, which was consistent with the results of our study. In line with the results of our study, F Casanova et al., [49] showed that treatment with SUs and weight loss was significantly associated with a reduction in microvascular complications in DM patients.

In contrast, in a review of 19 RCTs, JG González-González et al., [30] showed that although metformin was associated with a decrease in the risk of DR compared to other anti-glucose-lowering drugs or placebo in DM, but this difference was not statistically significant and clinical evidence did not report the protective effect of metformin against DR. This incompatibility in results can be justified by the difference in the characteristics of the examined patients, sample size, follow-up period, and design of the two studies. In this review, they only examined the RCT studies that included DM with unknown disease duration and short follow-up period, while in our study, the long-term effects of metformin and SUs on microvascular complications based on the diabetes duration and medication time.

YR Chung et al., showed that metformin and SUs significantly reduced the risk of DR progression in individuals and combined with DPP4i in DM patients [50]. In this regard, JX Li et al., [45] showed that the simultaneous use of metformin and SGLT2 in the long term reduced the risk of DR progression, which confirmed the results of our study. Similar results were reported by J Hasselstrøm Jensen et al. [46]. In an umbrella review in 2024, L Tan et al., [51] showed that prescribing antidiabetic drugs on the risk of DR among people with type 2 diabetes is generally safe and may reduce the risk of DR. H Tang et al., [52] showed in a network meta-analysis, the current evidence shows that the relationship between inhibitors of SUs, DPP-4i, GLP-1RA or SGLT2 and the risk of DR in DM patients is unclear.

One of the mechanisms of reducing the risk of microvascular complications can be explained by controlling blood sugar and preventing hyperglycemia with metformin and SUs. KH Song et al., [53] showed that the reduction of mean glycaemia and dyslipidemia was significantly associated with reducing risk and preventing DR progression. Although the exact pathogenesis mechanism of DR is unclear, recent evidence suggests the prominent role of apoptosis, the involvement of vascular abnormalities, and cellular senescence in the pathogenesis of DR [17]. Additionally, disrupted autophagy contributes to the worsening of DR, highlighting the significance of autophagy in inhibiting the apoptosis or aging of retinal pigment epithelium (RPE) cells [17]. The increase in the AMP: ATP ratio under metabolic stress conditions like hypoxia and glucose deprivation triggers the AMP-activated protein kinase (AMPK) pathway, leading to the adjustment of cellular metabolism [54]. At a molecular level, metformin provides cytoprotection by activating the AMPK pathway [55]. This pathway regulates metabolism and shields cells from degradation and pathological changes linked to aging and DR. Recent findings have shown that metformin operates through both AMPK-mediated and non-AMPK-mediated pathways to produce effects extending beyond diabetes treatment, potentially preventing aging and improving conditions related to DR [56, 57]. Besides the AMPK pathway, recent findings indicate that metformin can control pathways not dependent on AMPK, including autophagy, oxidative stress, and ER stress. This helps in protecting retinal cells from vascular abnormalities, apoptosis, and cell senescence, ultimately preventing the development of DR [54, 58].

Given that the design of the three cohorts (TLGS, MESA, and ARIC) was similar in terms of the characteristics of the populations under study (new diabetic patients), the variables considered as confounders, follow-up intervals, exposure (duration of treatment with metformin and sulfonylurea), and outcome (DR), we pooled the data from these three cohorts to increase the accuracy of the study. Common definitions were used to measure the variables in all three cohorts. We also analyzed the results of all three cohorts separately, and their individual results were consistent, indicating the validity of the studies. However, there were limitations in conducting the study, as noted.

Limitations

Our study had strengths and weaknesses that should be considered. First, we were unaware of the type of SU medication in each cohort, so we couldn’t discuss the safety issue according to the SU class. Also, due to the high missing in HbA1c, we could not estimate HbA1c, and the definition of DM was based on FBS. However, given that the aim was to evaluate the effect of treatment duration on the outcome and FBS was also measured and recorded during the follow-up period in all phases, its negative effect on the results may be very limited and clinically negligible. According to the definitions of the data recorded in the cohorts, we could not separate the results based on the type of retinopathy (nonproliferative or proliferative retinopathy). Unfortunately, medication adherence in cohort studies is self-reported. However, examining the trend of FBS in different phases can indicate the level of medication adherence. Continuous follow-up of patients in a combined manner (face-to-face and telephone follow-up), shortening follow-up periods and ease of performing blood sugar tests, and using more accurate tests (e-One) in shorter follow-up periods can provide more accurate medication adherence results.

The study’s main strength was evaluating the effect of metformin and SUs’ medication time on DN and DR in a large sample with a long follow-up period while adjusting for confounders in NDM patients.

Conclusion

Among newly diagnosed diabetic patients, long-term treatment with metformin and SUs alone and in combination was associated with a reduced risk of DR for about a decade compared with no treatment (patients who did not receive any antidiabetic drug). The protective effect of metformin and SUs on DR started after a certain trial period and continued for almost 10 years with different gradients. The protective effect of metformin and SUs alone and their combination on DR started approximately 5 years after initial treatment and then continued with different slopes up to 15 years after initial treatment. The combination therapy of Metformin and SUs can still be used with reasonable effectiveness especially in the first years of diabetes diagnosis. These cohorts were mainly conducted on predominantly urban patients with specific characteristics, and caution should be exercised in generalizing the results of this study to other populations. Replication of these studies in other populations, especially disadvantaged populations, may be associated with different results.

Data availability

The datasets generated or analyzed during the current study are available from the corresponding author on reasonable request.

Change history

References

  1. Afsar B. The impact of different anthropometric measures on sustained normotension, white coat hypertension, masked hypertension, and sustained hypertension in patients with type 2 diabetes. Endocrinol Metabolism. 2013;28:199–206.

    Article  Google Scholar 

  2. Association AD. 1. Improving care and promoting health in populations: standards of medical care in diabetes-2020. Diabetes Care. 2020;43:S7–13.

    Article  Google Scholar 

  3. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract. 2019;157:107843.

    Article  PubMed  Google Scholar 

  4. Federation ID. IDF diabetes atlas, tenth. International Diabetes. 2021.

  5. Saeedi P, Salpea P, Karuranga S, Petersohn I, Malanda B, Gregg EW et al. Mortality attributable to diabetes in 20–79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas. Diabetes research and clinical practice. 2020;162:108086.

  6. Crasto W, Patel V, Davies MJ, Khunti K. Prevention of microvascular complications of diabetes. Endocrinol Metabolism Clin. 2021;50:431–55.

    Article  Google Scholar 

  7. Wykoff CC, Khurana RN, Nguyen QD, Kelly SP, Lum F, Hall R, et al. Risk of blindness among patients with diabetes and newly diagnosed diabetic retinopathy. Diabetes Care. 2021;44:748–56.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tan T-E, Wong TY. Diabetic retinopathy: looking forward to 2030. Front Endocrinol. 2023;13:1077669.

    Article  Google Scholar 

  9. Chen H-Y, Kuo S, Su P-F, Wu J-S, Ou H-T. Health care costs associated with macrovascular, microvascular, and metabolic complications of type 2 diabetes across time: estimates from a population-based cohort of more than 0.8 million individuals with up to 15 years of follow-up. Diabetes Care. 2020;43:1732–40.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Visaria J, Iyer NN, Raval AD, Kong SX, Hobbs T, Bouchard J et al. Healthcare costs of diabetes and microvascular and macrovascular disease in individuals with incident type 2 diabetes mellitus: a ten-year longitudinal study. ClinicoEconomics Outcomes Res. 2020:423–34.

  11. Scanlon PH, Nevill CR, Stratton IM, Maruti SS, Massó-González EL, Sivaprasad S, et al. Prevalence and incidence of diabetic retinopathy (DR) in the UK population of Gloucestershire. Acta Ophthalmol. 2022;100:e560–70.

    Article  PubMed  Google Scholar 

  12. Ting DSW, Cheung GCM, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol. 2016;44:260–77.

    Article  PubMed  Google Scholar 

  13. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556–64.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wong TY, Mwamburi M, Klein R, Larsen M, Flynn H, Hernandez-Medina M, et al. Rates of progression in diabetic retinopathy during different time periods: a systematic review and meta-analysis. Diabetes Care. 2009;32:2307–13.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Sabanayagam C, Yip W, Ting DS, Tan G, Wong TY. Ten emerging trends in the epidemiology of diabetic retinopathy. Ophthalmic Epidemiol. 2016;23:209–22.

    Article  PubMed  Google Scholar 

  16. Fung CSC, Wan EYF, Wong CKH, Jiao F, Chan AKC. Effect of metformin monotherapy on cardiovascular diseases and mortality: a retrospective cohort study on Chinese type 2 diabetes mellitus patients. Cardiovasc Diabetol. 2015;14:137.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang W, Lo AC. Diabetic retinopathy: pathophysiology and treatments. Int J Mol Sci. 2018;19:1816.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lechner J, O’Leary OE, Stitt AW. The pathology associated with diabetic retinopathy. Vision Res. 2017;139:7–14.

    Article  PubMed  Google Scholar 

  19. Alghadyan AA. Diabetic retinopathy–an update. Saudi J Ophthalmol. 2011;25:99–111.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Duh EJ, Sun JK, Stitt AW. Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. JCI Insight. 2017;2.

  21. Kusuhara S, Fukushima Y, Ogura S, Inoue N, Uemura A. Pathophysiology of diabetic retinopathy: the old and the new. Diabetes Metabolism J. 2018;42:364–76.

    Article  Google Scholar 

  22. Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, et al. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res. 2016;51:156–86.

    Article  PubMed  Google Scholar 

  23. Setter SM, Iltz JL, Thams J, Campbell RK. Metformin hydrochloride in the treatment of type 2 diabetes mellitus: a clinical review with a focus on dual therapy. Clin Ther. 2003;25:2991–3026.

    Article  PubMed  CAS  Google Scholar 

  24. Wong M, Sin C, Lee J. The reference framework for diabetes care in primary care settings. Hong Kong Med J. 2012;18:238–46.

    PubMed  Google Scholar 

  25. Association AD. American Diabetes Association Standards of medical care in diabetes–2017. Diabetes Care. 2017;40:S1.

    Google Scholar 

  26. Qaseem A, Barry MJ, Humphrey LL, Forciea MA, Physicians* CGCotACo. Oral pharmacologic treatment of type 2 diabetes mellitus: a clinical practice guideline update from the American College of Physicians. Ann Intern Med. 2017;166:279–90.

    Article  PubMed  Google Scholar 

  27. Sherifali D, Nerenberg K, Pullenayegum E, Cheng JE, Gerstein HC. The effect of oral antidiabetic agents on A1C levels: a systematic review and meta-analysis. Diabetes Care. 2010;33:1859–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Zhang K, Yang W, Dai H, Deng Z. Cardiovascular risk following metformin treatment in patients with type 2 diabetes mellitus: results from meta-analysis. Diabetes Res Clin Pract. 2020;160:108001.

    Article  PubMed  Google Scholar 

  29. Bahardoust M, Mousavi S, Yariali M, Haghmoradi M, Hadaegh F, Khalili D, et al. Effect of metformin (vs. placebo or sulfonylurea) on all-cause and cardiovascular mortality and incident cardiovascular events in patients with diabetes: an umbrella review of systematic reviews with meta-analysis. J Diabetes Metab Disord. 2023.

  30. González-González JG, Solis RC, González-Colmenero AD, Raygoza-Cortez K, Moreno-Peña PJ, Sánchez AL, et al. Effect of metformin on microvascular outcomes in patients with type 2 diabetes: a systematic review and meta-analysis. Diabetes Res Clin Pract. 2022;186:109821.

    Article  PubMed Central  Google Scholar 

  31. Group DPPR. Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the diabetes prevention program outcomes study. Lancet Diabetes Endocrinol. 2015;3:866–75.

    Article  Google Scholar 

  32. Kooy A, De Jager J, Lehert P, Bets D, Wulffelé MG, Donker AJ, et al. Long-term effects of metformin on metabolism and microvascular and macrovascular disease in patients with type 2 diabetes mellitus. Arch Intern Med. 2009;169:616–25.

    Article  PubMed  CAS  Google Scholar 

  33. Gabriel R, Boukichou Abdelkader N, Acosta T, Gilis-Januszewska A, Gómez-Huelgas R, Makrilakis K, et al. Early prevention of diabetes microvascular complications in people with hyperglycaemia in Europe. ePREDICE randomized trial. Study protocol, recruitment and selected baseline data. PLoS ONE. 2020;15:e0231196.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Grapow MT, von Wattenwyl R, Guller U, Beyersdorf F, Zerkowski H-R. Randomized controlled trials do not reflect reality: real-world analyses are critical for treatment guidelines! J Thorac Cardiovasc Surg. 2006;132:5–7.

    Article  PubMed  Google Scholar 

  35. Tunis SR, Stryer DB, Clancy CM. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA. 2003;290:1624–32.

    Article  PubMed  CAS  Google Scholar 

  36. Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58:323–37.

    Article  PubMed  Google Scholar 

  37. Tricco AC, Rawson NS. Manitoba and Saskatchewan administrative health care utilization databases are used differently to answer epidemiologic research questions. J Clin Epidemiol. 2008;61:192–7. e12.

    Article  PubMed  Google Scholar 

  38. Fralick M, Kesselheim AS, Avorn J, Schneeweiss S. Use of health care databases to support supplemental indications of approved medications. JAMA Intern Med. 2018;178:55–63.

    Article  PubMed  Google Scholar 

  39. Larsen MD, Cars T, Hallas J. A minireview of the use of hospital-based databases in observational inpatient studies of drugs. Basic Clin Pharmacol Toxicol. 2013;112:13–8.

    Article  PubMed  CAS  Google Scholar 

  40. Royston P, Sauerbrei W. Multivariable modeling with cubic regression splines: a principled approach. Stata J. 2007;7:45–70.

    Article  Google Scholar 

  41. Boddepalli CS, Gutlapalli SD, Lavu VK, Abdelwahab RAM, Huang R, Potla S et al. The effectiveness and safety of metformin compared to sulfonylureas in diabetic nephropathy: a systematic review. Cureus. 2022;14.

  42. Li Y, Ryu C, Munie M, Noorulla S, Rana S, Edwards P, et al. Association of metformin treatment with reduced severity of diabetic retinopathy in type 2 diabetic patients. J Diabetes Res. 2018;2018:2801450.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fan Y-P, Wu C-T, Lin J-L, Hsiung CA, Liu HY, Lai J-N, et al. Metformin treatment is associated with a decreased risk of nonproliferative diabetic retinopathy in patients with type 2 diabetes mellitus: a population-based cohort study. J Diabetes Res. 2020;2020:9161039.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Akanuma Y, Kosaka K, Kanazawa Y, Kasuga M, Fukuda M, Aoki S. Long-term comparison of oral hypoglycemic agents in diabetic retinopathy Gliclazide vs. other sulfonylureas. Diabetes Res Clin Pract. 1988;5:81–90.

    Article  PubMed  CAS  Google Scholar 

  45. Li J-X, Hung Y-T, Bair H, Hsu S-B, Hsu C-Y, Lin C-J. Sodium-glucose co-transporter 2 inhibitor add-on therapy for metformin delays diabetic retinopathy progression in diabetes patients: a population-based cohort study. Sci Rep. 2023;13:17049.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Hasselstrøm Jensen J, Vestergaard P, Hasselstrøm Jensen M. Association between glucose-lowering treatments and risk of diabetic retinopathy in people with type 2 diabetes: a nationwide cohort study. Curr Drug Saf. 2024;19:236–43.

    Article  PubMed  Google Scholar 

  47. Group UPDS. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998;352:837–53.

    Article  Google Scholar 

  48. Barkmeier AJ, Herrin J, Swarna KS, Deng Y, Polley EC, Umpierrez GE et al. Comparative effectiveness of glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter 2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas for sight-threatening diabetic retinopathy. Ophthalmology Retina.

  49. Casanova F, Gooding KM, Shore AC, Adingupu DD, Mawson D, Ball C, et al. Weight change and sulfonylurea therapy are related to 3 year change in microvascular function in people with type 2 diabetes. Diabetologia. 2020;63:1268–78.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Chung Y-R, Ha KH, Kim HC, Park SJ, Lee K, Kim DJ. Dipeptidyl peptidase-4 inhibitors versus other antidiabetic drugs added to metformin monotherapy in diabetic retinopathy progression: a real world-based cohort study. Diabetes Metabolism J. 2019;43:640–8.

    Article  Google Scholar 

  51. Tan L, Wang Z, Okoth K, Toulis KA, Denniston AK, Singh BM, et al. Associations of antidiabetic drugs with diabetic retinopathy in people with type 2 diabetes: an umbrella review and meta-analysis. Front Endocrinol. 2024;14:1303238.

    Article  Google Scholar 

  52. Tang H, Li G, Zhao Y, Wang F, Gower EW, Shi L, et al. Comparisons of diabetic retinopathy events associated with glucose-lowering drugs in patients with type 2 diabetes mellitus: a network meta‐analysis. Diabetes Obes Metabolism. 2018;20:1262–79.

    Article  CAS  Google Scholar 

  53. Song KH, Jeong JS, Kim MK, Kwon HS, Baek KH, Ko SH, et al. Discordance in risk factors for the progression of diabetic retinopathy and diabetic nephropathy in patients with type 2 diabetes mellitus. J Diabetes Invest. 2019;10:745–52.

    Article  CAS  Google Scholar 

  54. Hsu S-K, Cheng K-C, Mgbeahuruike MO, Lin Y-H, Wu C-Y, Wang H-MD, et al. New insight into the effects of metformin on diabetic retinopathy, aging and cancer: nonapoptotic cell death, immunosuppression, and effects beyond the AMPK pathway. Int J Mol Sci. 2021;22:9453.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Mihaylova MM, Shaw RJ. The AMPK signalling pathway coordinates cell growth, autophagy and metabolism. Nat Cell Biol. 2011;13:1016–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Shrikanth C, Nandini C. AMPK in microvascular complications of diabetes and the beneficial effects of AMPK activators from plants. Phytomedicine. 2020;73:152808.

    Article  PubMed  CAS  Google Scholar 

  57. Kauppinen A, Suuronen T, Ojala J, Kaarniranta K, Salminen A. Antagonistic crosstalk between NF-κB and SIRT1 in the regulation of inflammation and metabolic disorders. Cell Signal. 2013;25:1939–48.

    Article  PubMed  CAS  Google Scholar 

  58. Chen H, Ji Y, Yan X, Su G, Chen L, Xiao J. Berberine attenuates apoptosis in rat retinal Müller cells stimulated with high glucose via enhancing autophagy and the AMPK/mTOR signaling. Biomed Pharmacother. 2018;108:1201–7.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

The present study is a part of PhD thesis written by Mansour Bahardoust under the supervision of prof. Ali Delpisheh; Prof. Davood Khalili, Prof. Farzad Hadaegh and Prof. Yadollah Mehrabi.

Funding

Shahid Beheshti University of Medical Sciences, Tehran, Iran financially supported the present study.

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Authors and Affiliations

  1. Department of Epidemiology, School of Public Health & Safety, Shahid Beheshti University of Medical Sciences, Velenjak, 7th Floor, Bldg No.2 SBUMS, Arabi Ave, Tehran, Tehran Province, Iran

    Mansour Bahardoust, Yadollah Mehrabi & Ali Delpisheh

  2. Prevention of Metabolic Disorders Research Center, Division of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, Yaman St, Aarabi St, No.24, Tehran, Iran

    Farzad Hadaegh & Davood Khalili

  3. Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Ali Delpisheh

  4. Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Davood Khalili

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  1. Mansour Bahardoust
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  2. Yadollah Mehrabi
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  3. Farzad Hadaegh
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  4. Davood Khalili
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  5. Ali Delpisheh
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Contributions

MB, FH, DK, and AD designed the study. MB, DK and YM finished statistical analysis. MB, FH, and AD wrote the first draft. DK and FH reviewed and checked the manuscript. AD, DK, MB and FH modified the English. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Davood Khalili or Ali Delpisheh.

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Ethics approval and consent to participate

This study was approved by Shahid Beheshti, the University of Medical Sciences’s ethics committee (ethical code: IR. REC.PHNS.SBMU.1402.008), and the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) to access cohort data.All methods were carried out according to the Declaration of Helsinki and current ethics guidelines.

Competing interests

The authors declare no competing interests.

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The original version of this article was revised: The affiliation for Davood Khalili has been corrected.

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Bahardoust, M., Mehrabi, Y., Hadaegh, F. et al. Impact of duration of treatments with metformin and sulfonylureas, individually or in combination, on diabetic retinopathy among newly diagnosed type 2 diabetic patients: a pooled cohort’s analysis. Int J Retin Vitr 11, 9 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40942-025-00637-w

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Keywords

International Journal of Retina and Vitreous

ISSN: 2056-9920

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