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Impaired fasting glucose and sulfonylureas increased the risk of major cardiovascular events in patients with inflammatory arthritis

Diabetology & Metabolic Syndrome volume 17, Article number: 132 (2025) Cite this article

Abstract

Objectives

To evaluate the effect of impaired fasting glucose (IFG) and various anti-diabetic agents on the risk of incident major cardiovascular events (MACE) in patients with inflammatory arthritis (IA) including rheumatoid arthritis (RA) and psoriatic arthritis (PsA).

Methods

This was a population-based retrospective cohort study. Patient identification and data retrieval were conducted using a big data platform (The Hospital Authority Data Collaboration Lab) in Hong Kong. Patients with IA were recruited from Jan 2006 to Dec 2015 and followed up until the end of 2018. Time-dependent Cox proportional hazards regression models were used to analyze the association between fasting glucose (FG) levels and anti-diabetic drug use with MACE in IA patients.

Results

A total of 13,905 patients (12,233 RA and 1,672 PsA) were included. 934 patients (6.7%) developed the first MACE after a total of 119,571 patient-years of follow-up. More patients in the MACE group had IFG (FG 5.6–6.9 mmol/l) (19.4% vs. 15.2%, p < 0.001) and FG ≥ 7 mmol/l (17.6% vs. 8.1%, p < 0.001) at baseline. In the subgroup of patients who were not taking any anti-diabetic medications, a prediabetic state was found to be independently associated with a higher risk of MACE (HR 2.43, 95%CI 1.97–2.99 in CRP model and HR 2.54, 95%CI 1.50–7.71 in ESR model). On the other hand, in patients with diabetes, sulfonylurea use increased the risk of MACE development by 55% (HR 1.55, 95%CI 1.14–2.09) after adjusting for other covariates.

Conclusions

In a large cohort of patients with IA, IFG and sulfonylureas use were found to be independently associated with an increased risk of incident MACE.

Key messages

What is already known about this subject?

  • Patients with inflammatory arthritis (IA) including rheumatoid arthritis (RA) and psoriatic arthritis (PsA) have an increased risk of major cardiovascular events (MACE).

  • In the general population, impaired fasting glucose (IFG) was found to be associated with an elevated risk of all-cause mortality as well as incident cardiovascular disease.

What does this study add?

  • IA patients with IFG were found to have a significantly increased risk of incident MACE after adjusting for the effects of inflammatory burden, traditional cardiovascular risk factors and anti-rheumatic drugs.

  • The use of sulfonylurea was independently associated with a higher incidence of MACE in IA patients.

How might this impact on clinical practice or future developments?

  • These findings suggest that tight control of blood glucose levels and careful selection of anti-diabetic medication may be important in reducing the risk of MACE in patients with RA and PsA.

Introduction

Inflammatory arthritis (IA), which includes rheumatoid arthritis (RA) and psoriatic arthritis (PsA), is associated with an increased risk of major adverse cardiovascular events (MACE) [1, 2], with both conditions reportedly having a similar incidence of MACE [3]. They predispose patients to cardiovascular (CV) risk through several mechanisms, including chronic inflammatory burden, the impact of pharmacotherapies, and a high prevalence of traditional CV risk factors such as type 2 diabetes mellitus (T2DM) and hypertension [4, 5]. Approximately half of the elevated cardiovascular disease (CVD) risk in RA can be attributed to traditional CV risk factors [6]. Prediabetes is strongly linked to the development of T2DM, as evidenced by a study of two million adults showing that young individuals diagnosed with prediabetes at age 20 face a 90% lifetime risk of developing diabetes [7]. In the prediabetic state, increased lipolysis, increased β cell dysfunction and decreased endogenous levels of glucagon-like peptide 1 (GLP-1) coincide with aberrant expression of proinflammatory cytokines, creating a detrimental environment for CVD [8,9,10]. A recent meta-analysis comprising 129 studies revealed that the general population with prediabetes compared to those with normoglycaemia, exhibited an absolute risk difference of 7.36 (95% confidence interval [CI] 9.59–12.51) for all-cause mortality, 8.75 (95%CI 6.41–10.49) for composite CVD per 10,000 person-years [11]. However, the interplay of metabolic and inflammatory effects adds complexity to the interpretation of the association between prediabetes and CVD in IA. Data exploring the effect of prediabetes on the risk of CVD in patients with IA were scanty.

On top of the glucose-lowering effects, different anti-diabetic medications have differential metabolic effects [12, 13]. While metformin, as well as some newer agents, have been shown to be additionally CVD protective in DM patients [14,15,16], the roles of other commonly used drugs such as sulfonylureas and insulin are more controversial [17,18,19,20]. The CV risk associated with sulfonylureas in patients with T2DM can be analyzed from two contrasting perspectives. Sulfonylureas can cause the inhibition of ischaemic conditioning, hypoglycaemia, and weight gain, which have been postulated as potential contributors to an increased susceptibility to major adverse cardiovascular events (MACE) [18]. However, sulfonylureas also exert a glucose-lowering effect [21], thereby mitigating the risk of incident MACE. Studies examining the CVD effect of various anti-diabetic agents in patients with IA taking into account the multitude of contributing factors are lacking.

Thus, the purpose of this study was to evaluate the effect of prediabetes and various anti-diabetic medications overtime on the risk of incident MACE in patients with IA, factoring in multiple clinical variables, including other traditional CV risk factors, markers of systemic inflammation and different anti-rheumatic drugs, using a large real-world database.

Methods

Data source and patients

Data were retrieved retrospectively from the Hospital Authority Data Collaboration Lab (HADCL) in Hong Kong, which adopted an electronic health record (EHR) system that captures diagnostic and procedure codes, laboratory tests, and prescription records longitudinally [22, 23]. Patients diagnosed with RA and PsA were recruited from Jan 2006 to Dec 2015 and followed until the end of 2018. The physician’s diagnoses were recorded in the system according to the International Classification of Diseases, 9th Revision, Clinical Modification (ICD9-CM). Inclusion criteria were (1) patients who carried the diagnosis code of RA (ICD9-CM: 714) or PsA (ICD9-CM: 696); (2) without a history of MACE before the diagnosis date of RA or PsA; (3) follow-up periods lasted at least 2 years and RA/PsA diagnostic codes lasted ≥ 6 months; and (4) age of or above 18 at disease onset. The index date was defined as the date when patients first received an RA or PsA diagnosis.

Definition of impaired fasting glucose (IFG) and diabetes

The prediabetic state was represented by IFG, which was defined as a fasting plasma glucose level between 5.6–6.0 mmol/L, according to the American Diabetes Association [24], in any one available measurement record. People were identified as having a diabetes diagnosis based on the ICD9-CM code or being prescribed at least one antidiabetic medication.

Outcomes

The outcome was the first occurrence of MACE. MACE was defined as a composite of myocardial infarction (MI), unstable angina, ischemic stroke, hemorrhagic stroke, transient ischemic attack, and CV-related death (diagnosis codes listed in Supplementary Table 1).

Covariates and medication exposures

Demographic data and traditional CV risk factors (including age, gender, T2DM, hypertension, and dyslipidemia) were retrieved. The definition of CV risk factors at baseline or ever was based on the ICD9-CM and/or by the dispensed prescription records of treatment for these conditions at baseline or during follow-up (details of the treatment items are listed in Supplementary Table 2). FG, glycated hemoglobin A1c (HbA1c), and lipid profile were recorded. The atherogenic index of plasma (AIP) which is the ratio of triglycerides to high-density lipoprotein cholesterol logarithmically transformed was calculated [25]. Inflammatory burden, including arthritis disease duration, and levels of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) were also collected. All the covariates except age, gender, and arthritis disease duration were derived yearly after the index date until the end of follow-up. To handle the missing data, we used a complete-case analysis that only used the available data.

The anti-diabetic medication exposures were reported as the use of oral antihyperglycemic agents (OHAs), including metformin, sulfonylureas, thiazolidinedione, dipeptidyl peptidase-4 inhibitors (DPP4i), and sodium-glucose linked transporter-2 inhibitors (SGLT2), GLP1 receptor agonists, and the use of insulin. The anti-rheumatic drugs, including conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), biologic DMARDs, non-steroidal anti-inflammatory drugs (NSAIDs), and glucocorticoids were also recorded yearly. All the pharmacotherapies were tracked according to the British National Formulary codes (Supplementary Table 2). Drug prescriptions for combination use were analyzed separately.

Statistical analysis

Descriptive statistics were used for baseline demographic and clinical characteristics, including frequencies or means ± SD. The baseline covariates were analyzed by the Student’s t-test for continuous variables and the χ2 test for categorical variables. Time-dependent cox proportional hazards regression models were conducted to analyze the independent association between the covariates and MACE. All laboratory results were recorded as time-varying covariates with an update in a yearly interval. Each yearly record of FG, lipid profile, ESR and CRP level was calculated by taking the mean values of the available measurements during each particular year. In addition, medications dispensed during follow-up were also assessed, and the association between drugs used and MACE was analyzed at a yearly interval. The users were compared with the non-users. Intervals were discarded from the analysis if there were missing data. Univariable analysis was calculated for covariates and medication variables, and variables with a p-value less than 0.05 were included in the multivariate analysis. Further analyses were performed to assess the interactive effect of drug use and FG levels. The Kaplan–Meier curves were used to visualize the CV event-free survival between different groups. Statistical significance was determined using a p < 0.05. All analyses were performed in R version 4.0.

Results

Clinical characteristics

A total of 13,905 patients, of whom 12,233 with RA and 1672 with PsA, were recruited. The baseline demographic characteristics of the whole cohort are shown in Table 1. After 119,571 patient-years of follow-up, 934 patients (6.7%) developed incident MACE. Patients in the MACE group were older (68.9 ± 11.8 vs. 56.0 ± 14.2 years, p < 0.001) and were more likely to be of male gender (27.5% vs. 22.9%, p = 0.001). At baseline, a significantly higher proportion of them had FG level 5.6–6.9 mmol/L (19.4% vs. 15.2%, p < 0.001), FG level ≥ 7 mmol/l (17.6% vs. 8.1%, p < 0.001), or OHAs (15.0% vs 6.8%, p < 0.001) and insulin (4.2% vs 1.1%, p < 0.001). They also had higher levels of inflammatory markers but were less likely to be on anti-rheumatic medications except for glucocorticoids (Supplementary Table 3). Most IA patients have had their glucose levels checked (95.3%), and the details of patients with no glucose checked and ever glucose checked are shown in Supplementary Table 4.

Table 1 Baseline demographic and clinical characteristics, cardiovascular risk factors, and treatments between patients with different inflammatory arthritis
Full size table

IFG and the risk of MACE

In the time-varying ESR model, IFG (hazard ratio [HR] 2.54, 95%CI 1.50–7.71, p < 0.001) and FG ≥ 7 mmol/L (HR 4.47, 95%CI 3.25–6.16, p < 0.001) were associated with significantly increased the risk of MACE in IA patients without anti-diabetic drugs after controlling for other traditional CV risk factors, systemic inflammation and use of anti-rheumatic drugs. In the time-varying CRP model, IFG (HR 2.43, 95%CI 1.97–2.99, p < 0.001) and FG ≥ 7 mmol/l (HR 3.51, 95%CI 2.53–4.86, p < 0.001) were also independently associated with an elevated risk of MACE (Table 2). In IA patients who were on anti-diabetic drugs, only FG ≥ 7 mmol/l was independently associated with MACE in the ESR model (HR 1.79, 95%CI 1.18–2.70, p < 0.001) and CRP model (HR 1.57, 95%CI 1.05–3.19, p = 0.025) (Supplementary Table 5). The event-free survival curves of IA patients with different FG levels are presented in Fig. 1. A significant difference in event-free survival with different FG levels was also noted in a separate analysis of the RA or PsA cohort (both are p < 0.001) (Supplementary Fig. 1a and b). Supplementary Figs. 2a and 2b showed the significant differences in different FG levels between IA patients with and without anti-diabetic drugs (p < 0.001).

Table 2 Multivariable Cox proportional hazards regressions using the demographic variables as time fixed and the other features as time-dependent predictors (being updated at each visit) in patients without any diabetic drug use (RA and PsA cohort)
Full size table
Fig. 1
figure 1

Kaplan–Meier curves and the log-rank test showing the cardiovascular event-free survival among patients with fasting glucose < 5.6 mmol/l or 5.6–6.9 mmol/l or ≥ 7 mmol/l in the entire cohort. Number at risk indicates the number of patient-intervals (person-time) at each time point, not unique patients

Full size image

Anti-diabetic medications and the risk of MACE

In the univariate analysis, inflammation, traditional CV factors, SLZ, and steroid use were significantly associated with a higher risk of MACE (Supplementary Table 6). The use of anti-diabetic drugs at baseline and during the follow-up is shown in Supplementary Fig. 3. In the cohort of IA patients with diabetes, time-varying sulfonylurea (HR 1.77, 95% CI 1.40–2.25, p < 0.001) and insulin use (HR 5.95, 95% CI 4.68–7.57, p < 0.001) were associated with increased risk of MACE, whereas metformin was related to reduced risk (HR 0.69 95% CI 0.54–0.88, p = 0.003) in the univariate analysis (Supplementary Table 7). After adjustment for other potential covariates, including age, gender, traditional CV risk factors, inflammatory markers, and the use of anti-rheumatic medications, the use of sulfonylurea (HR 1.50, 95%CI 1.09–2.05, p = 0.012 in ESR model; HR 1.55, 95%CI 1.14–2.09, p = 0.005 in CRP model) and insulin (HR 3.11, 95%CI 2.23–4.34, p < 0.001 in ESR model; HR 3.22, 95%CI 2.33–4.45, p < 0.001 in CRP model) were significantly associated with increased risk of MACE (Table 3). In the subgroup analysis, insulin users were also found to be older and carrying more adverse CVD risk factors (Supplementary Table 8). On the other hand, the interaction of insulin use and FG ≥ 7 reduced the risk of MACE (HR 0.43, 95%CI 0.19–0.98, p = 0.044 in the ESR model; HR 0.38, 95%CI 0.18–0.84, p = 0.017 in the CRP model) (Supplementary Table 9).

Table 3 Multivariable Cox proportional hazards with time-varying diabetic treatment (being updated at each visit) in patients with ever diabetic cohort
Full size table

Discussion

The present study is the first to concurrently evaluate the impact of IFG and anti-diabetic drug use on the long-term risk of MACE independent of other traditional CV risk factors, inflammatory burden, and anti-rheumatic drug use in a large population of patients with IA. We found that pre-diabetic state and the use of sulfonylureas were risk factors of incident MACE. The findings extend our understanding of the association of glycemic control and CVD in IA, and could inform patient management in clinical practice.

It is known that IA patients exhibit a higher prevalence of T2DM or IFG compared to the general population [26, 27], potentially attributed to the systemic inflammatory burden. Endothelial dysfunction molecular markers, including intercellular adhesion molecule-1 (ICAM-1) and tumor necrosis factor-α (TNF-α), which are pivotal factors in the development and pathophysiology of atherosclerosis [28], exhibit increased levels in individuals with prediabetes [29]. A retrospective study conducted in Hong Kong, utilizing a territory-wide diabetes surveillance dataset and involving 1,630,942 individuals revealed that prediabetes exhibited a statistically significant association with an elevated age- and sex-adjusted HR of 1.13 (95%CI 1.11–1.14) for CVD [30]. Due to the presence of systemic inflammation, it is reasonable to expect that the combination of pre-diabetes and IA would lead to a significantly elevated CVD risk. A meta-analysis of 10 studies confirms that traditional CV risk factors increase the risk of CV events in RA patients, underscoring the importance of managing these risk factors similarly to non-RA patients [31]. Nonetheless, most of these studies did not analyze the effects of inflammation, either in terms of inflammatory markers or disease activity, during the follow-up period [31]. On the contrary, it has been postulated that chronic hyperglycemia could lead to the formation of irreversible advanced glycation end-products (AGEs) affecting the function of long-lived proteins, including cytokines and immunoglobulins, resulting in a “less active” immune system [32, 33]. The hypothesis was supported by a prospective study showing a lower prevalence of RA in patients with T2DM [34]. The salient finding of our study is that patients without any diabetic drug use during follow-up who had IFG carried a 2.5-fold increased risk of MACE over time compared to those with normoglycemia after adjusting for time-varying inflammatory markers, other conventional CV risk factors, and anti-rheumatic medications. Our results highlight the exaggerated CV risk of prediabetes in the background of systemic inflammatory disease, and early blood glucose control may play a crucial role in the prevention of CVD among patients with IA.

An intriguing finding of our study was that the AIP significantly increased the risk of MACE in IA patients not using any diabetic medications. However, this association was no longer statistically significant in patients who had ever used diabetic drugs. This might be attributed by the fact that patients on anti-diabetic medications often have worse metabolic profile and will more likely be prescribed with medications such as statins or antiplatelet agents. These medications could confound the association of AIP and MACE.

Our study also examined the impact of anti-diabetic drugs on MACE in patients with IA. The CV effect of sulfonylureas is still controversial. They have been reported to be associated with a higher risk of MACE compared with other anti-diabetic drugs by two systematic meta-analyses in T2DM [35, 36]. However, a recent systematic meta-analysis, specifically focused on randomized controlled trials, found no significant difference in terms of all-cause mortality and serious adverse events when comparing the use of metformin-SGLT2 inhibitors versus metformin-sulfonylureas [37]. Sulfonylureas lower blood sugar by binding to ATP-sensitive potassium (KATP) channels on pancreatic β-cells leading to the opening of voltage-gated calcium channels, which then triggers the release of insulin-containing secretory granules from the β-cells into the bloodstream [38]. Nonetheless, not only do sulfonylureas affect pancreatic KATP channels, they also impact myocardial KATP channels, potentially disrupting the cellular pathway responsible for myocardial ischemic protection [39]. Our findings are consistent with many of the previous studies, noting an increased risk of MACE in IA patients on sulfonylureas. The approximately 50% elevated risk also appeared higher than the adjusted HR of 1.3 noted in another cohort study comparing MACE in T2DM patients treated with sulfonylureas versus metformin monotherapy, again hinting the contribution of systemic inflammation [40]. However, we did not perform a separate analysis to determine whether a specific medication within the class of sulfonylureas is more likely to increase the risk of MACE.

Several studies [41, 42], but not all [43], reported that patients using exogenous injected insulin had a higher risk of CVD and all-cause mortality compared to combination therapies of OHAs. Hypoglycemia which is commonly associated with insulin therapy could stimulate inflammatory response and increase oxidative stress, leading to increased CVD [44, 45]. Experimental studies have demonstrated that iatrogenic hyperinsulinemia can elevate blood pressure and result in the retention of sodium, water, and uric acid by the kidneys [46]. The ACCORD study was stopped early because intensive glucose lowering was associated with more hypoglycemia, weight gain, and increased all-cause mortality without any MACE benefit in patients with T2DM [42]. In our study, IA patients who were on insulin had increased risk of MACE in the multivariable model. Interestingly, an opposite relationship was observed when an interaction term of insulin use and FG ≥ 7 mmol/l was incorporated into the regression model. It may suggest that the hazard associated with insulin use is driven by excessive glycemic control, consistent with previous studies. Further studies examining the effects of hypoglycemia on CV outcomes in IA patients are encouraged.

There are some limitations in this study. Firstly, our study only identifies associations, and causal relationships cannot be definitively established due to its retrospective observational nature. Secondly, the lack of data regarding patient adherence to prescribed therapies, medication dosages, alternative medications, and other consumed agents restricts our analysis. In addition, certain traditional CV risk factors, including smoking status, obesity, and family history of CVD, were not incorporated into the analysis due to their unavailability in the database. Thirdly, the generalizability of our findings may be limited by the specific population studied (East Asians), which may not represent all patients with RA and PsA. Fourthly, due to the absence of certain clinical parameters, validated disease activity scores could not be included in the analysis. Inflammatory markers were utilized as a surrogate measure for disease activity in their place. Lastly, the usage of some OHAs was relatively small, such as DPP4i (total person-time intervals: 860 years) and thiazolidinedione (total person-time intervals: 165 years), and almost no IA patients were on GLP1 receptor agonists and SGLT2 inhibitors. With future updates of our database, we will have access to additional data that will facilitate a more comprehensive analysis of the effects of these OHAs on CVD outcomes.

In conclusion, IA patients with IFG had an excess risk of incident MACE before they were diagnosed with DM. The use of sulfonylureas was associated with MACE development in DM patients. To optimize CV outcomes in IA patients, close blood sugar monitoring, earlier treatments and careful selection of drugs considering their differential effects on CV risk are recommended.

Availability of data and materials

No datasets were generated or analysed during the current study.

References

  1. Lindhardsen J, Ahlehoff O, Gislason GH, Madsen OR, Olesen JB, Torp-Pedersen C, et al. The risk of myocardial infarction in rheumatoid arthritis and diabetes mellitus: a Danish nationwide cohort study. Ann Rheum Dis. 2011;70(6):929–34.

    Article  PubMed  Google Scholar 

  2. Polachek A, Touma Z, Anderson M, Eder L. Risk of cardiovascular morbidity in patients with psoriatic arthritis: a meta-analysis of observational studies. Arthritis Care Res (Hoboken). 2017;69(1):67–74.

    Article  PubMed  Google Scholar 

  3. Meng H, Lam SH, So H, Tam LS. Incidence and risk factors of major cardiovascular events in rheumatoid arthritis and psoriatic arthritis: a population-based cohort study. Semin Arthritis Rheum. 2024;65: 152416.

    Article  CAS  PubMed  Google Scholar 

  4. Bengtsson K, Forsblad-d’Elia H, Lie E, Klingberg E, Dehlin M, Exarchou S, et al. Are ankylosing spondylitis, psoriatic arthritis and undifferentiated spondyloarthritis associated with an increased risk of cardiovascular events? A prospective nationwide population-based cohort study. Arthritis Res Ther. 2017;19(1):1–12.

    Article  Google Scholar 

  5. Kerola AM, Kazemi A, Rollefstad S, Lillegraven S, Sexton J, Wibetoe G, et al. All-cause and cause-specific mortality in rheumatoid arthritis, psoriatic arthritis and axial spondyloarthritis: a nationwide registry study. Rheumatology. 2022;61:4656–66.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Crowson CS, Rollefstad S, Ikdahl E, Kitas GD, Van Riel PL, Gabriel SE, et al. Impact of risk factors associated with cardiovascular outcomes in patients with rheumatoid arthritis. Ann Rheum Dis. 2018;77(1):48–54.

    Article  CAS  PubMed  Google Scholar 

  7. Zhang X, Wu H, Fan B, Shi M, Lau ES, Yang A, et al. Lifetime risk of developing diabetes in Chinese people with normoglycemia or prediabetes: a modeling study. PLoS Med. 2022;19(7): e1004045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Toft-Nielsen M-B, Damholt MB, Madsbad S, Hilsted LM, Hughes TE, Michelsen BK, et al. Determinants of the impaired secretion of glucagon-like peptide-1 in type 2 diabetic patients. J Clin Endocrinol Metab. 2001;86(8):3717–23.

    Article  CAS  PubMed  Google Scholar 

  9. Kanat M, DeFronzo RA, Abdul-Ghani MA. Treatment of prediabetes. World J Diabetes. 2015;6(12):1207.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Brannick B, Dagogo-Jack S. Prediabetes and cardiovascular disease: pathophysiology and interventions for prevention and risk reduction. Endocrinol Metab Clin. 2018;47(1):33–50.

    Article  Google Scholar 

  11. Cai X, Zhang Y, Li M, Wu JH, Mai L, Li J, et al. Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ. 2020;370:m2297.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Vieira R, Souto SB, Sánchez-López E, López Machado A, Severino P, Jose S, et al. Sugar-lowering drugs for type 2 diabetes mellitus and metabolic syndrome—Review of classical and new compounds: part-I. Pharmaceuticals. 2019;12(4):152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Patti AM, Rizvi AA, Giglio RV, Stoian AP, Ligi D, Mannello F. Impact of glucose-lowering medications on cardiovascular and metabolic risk in type 2 diabetes. J Clin Med. 2020;9(4):912.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Roumie CL, Chipman J, Min JY, Hackstadt AJ, Hung AM, Greevy RA, et al. Association of treatment with metformin vs sulfonylurea with major adverse cardiovascular events among patients with diabetes and reduced kidney function. JAMA. 2019;322(12):1167–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tanaka A, Sata M, Okada Y, Teragawa H, Eguchi K, Shimabukuro M, et al. Effect of ipragliflozin on carotid intima-media thickness in patients with type 2 diabetes: a multicenter, randomized, controlled trial. Eur Heart J Cardiovasc Pharmacother. 2023;9(2):165–72.

    Article  PubMed  Google Scholar 

  16. Masson W, Lavalle-Cobo A, Lobo M, Masson G, Molinero G. Novel antidiabetic drugs and risk of cardiovascular events in patients without baseline metformin use: a meta-analysis. Eur J Prev Cardiol. 2021;28(1):69–75.

    Article  PubMed  Google Scholar 

  17. Phung O, Schwartzman E, Allen R, Engel S, Rajpathak S. Sulphonylureas and risk of cardiovascular disease: systematic review and meta-analysis. Diabet Med. 2013;30(10):1160–71.

    Article  CAS  PubMed  Google Scholar 

  18. Abdelmoneim A, Eurich D, Light P, Senior P, Seubert J, Makowsky M, et al. Cardiovascular safety of sulphonylureas: over 40 years of continuous controversy without an answer. Diabetes Obes Metab. 2015;17(6):523–32.

    Article  CAS  PubMed  Google Scholar 

  19. Gamble J-M, Chibrikov E, Twells LK, Midodzi WK, Young SW, MacDonald D, et al. Association of insulin dosage with mortality or major adverse cardiovascular events: a retrospective cohort study. Lancet Diabetes Endocrinol. 2017;5(1):43–52.

    Article  CAS  PubMed  Google Scholar 

  20. Rados DV, Falcetta MRR, Pinto LC, Leitao CB, Gross JL. All-cause mortality and cardiovascular safety of basal insulin treatment in patients with type 2 diabetes mellitus: a systematic review with meta-analysis and trial sequential analysis. Diabetes Res Clin Pract. 2021;173: 108688.

    Article  CAS  PubMed  Google Scholar 

  21. 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(9131):837–53.

    Article  Google Scholar 

  22. Wong GLH, Hui VWK, Tan Q, Xu J, Lee HW, Yip TCF, et al. Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis. JHEP Rep. 2022;4(3): 100441.

    Article  PubMed  PubMed Central  Google Scholar 

  23. So H, Lam TO, Meng H, Lam SHM, Tam L-S. Time and dose-dependent effect of systemic glucocorticoids on major adverse cardiovascular event in patients with rheumatoid arthritis: a population-based study. Ann Rheum Dis. 2023;82(11):1387–93.

    Article  CAS  PubMed  Google Scholar 

  24. Genuth S, Alberti K, Bennett P, Buse J, DeFronzo R, Kahn R, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care. 2003;26(11):3160–8.

    Article  PubMed  Google Scholar 

  25. Dobiasova M. AIP–atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice. Vnitr Lek. 2006;52(1):64–71.

    CAS  PubMed  Google Scholar 

  26. Ruscitti P, Ursini F, Cipriani P, Ciccia F, Liakouli V, Carubbi F, et al. Prevalence of type 2 diabetes and impaired fasting glucose in patients affected by rheumatoid arthritis: results from a cross-sectional study. Medicine. 2017;96(34): e7896.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dal Bello G, Gisondi P, Idolazzi L, Girolomoni G. Psoriatic arthritis and diabetes mellitus: a narrative review. Rheumatol Ther. 2020;7:271–85.

    Article  Google Scholar 

  28. Medina-Leyte DJ, Zepeda-García O, Domínguez-Pérez M, González-Garrido A, Villarreal-Molina T, Jacobo-Albavera L. Endothelial dysfunction, inflammation and coronary artery disease: potential biomarkers and promising therapeutical approaches. Int J Mol Sci. 2021;22(8):3850.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang Z, Chen C, Li S, Kong F, Shan P, Huang W. Serum markers of endothelial dysfunction and inflammation increase in hypertension with prediabetes mellitus. Genet Test Mol Biomark. 2016;20(6):322–7.

    Article  CAS  Google Scholar 

  30. Zhang X, Wu H, Fan B, Shi M, Lau ES, Yang A, et al. The role of age on the risk relationship between prediabetes and major morbidities and mortality: analysis of the Hong Kong diabetes surveillance database of 2 million Chinese adults. Lancet Reg Health West Pac. 2022;30:100599. https://doi.org/10.1016/j.lanwpc.2022.100599.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Baghdadi LR, Woodman RJ, Shanahan EM, Mangoni AA. The impact of traditional cardiovascular risk factors on cardiovascular outcomes in patients with rheumatoid arthritis: a systematic review and meta-analysis. PLoS ONE. 2015;10(2): e0117952.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Thornalley PJ. Cell activation by glycated proteins. AGE receptors, receptor recognition factors and functional classification of AGEs. Cell Mol Biol (Noisy-le-Grand). 1998;44(7):1013–23.

    CAS  PubMed  Google Scholar 

  33. Jaleel A, Halvatsiotis P, Williamson B, Juhasz P, Martin S, Nair KS. Identification of Amadori-modified plasma proteins in type 2 diabetes and the effect of short-term intensive insulin treatment. Diabetes Care. 2005;28(3):645–52.

    Article  CAS  PubMed  Google Scholar 

  34. Tentolouris A, Thanopoulou A, Tentolouris N, Eleftheriadou I, Voulgari C, Andrianakos A, et al. Low prevalence of rheumatoid arthritis among patients with pre-existing type 2 diabetes mellitus. Ann Transl Med. 2018;6(20):399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Powell WR, Christiansen CL, Miller DR. Meta-analysis of sulfonylurea therapy on long-term risk of mortality and cardiovascular events compared to other oral glucose-lowering treatments. Diabetes Ther. 2018;9(4):1431–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Dave CV, Kim SC, Goldfine AB, Glynn RJ, Tong A, Patorno E. Risk of cardiovascular outcomes in patients with type 2 diabetes after addition of SGLT2 inhibitors versus sulfonylureas to baseline GLP-1RA therapy. Circulation. 2021;143(8):770–9.

    Article  CAS  PubMed  Google Scholar 

  37. Gebrie D, Getnet D, Manyazewal T. Cardiovascular safety and efficacy of metformin-SGLT2i versus metformin-sulfonylureas in type 2 diabetes: systematic review and meta-analysis of randomized controlled trials. Sci Rep. 2021;11(1):137.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Aquilante CL. Sulfonylurea pharmacogenomics in Type 2 diabetes: the influence of drug target and diabetes risk polymorphisms. Expert Rev Cardiovasc Ther. 2010;8(3):359–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Scognamiglio R, Avogaro A, Vigili de Kreutzenberg S, Negut C, Palisi M, Bagolin E, et al. Effects of treatment with sulfonylurea drugs or insulin on ischemia-induced myocardial dysfunction in type 2 diabetes. Diabetes. 2002;51(3):808–12.

    Article  CAS  PubMed  Google Scholar 

  40. Wilke T, Mueller S, Groth A, Berg B, Hammar N, Tsai K, et al. Effectiveness of sulphonylureas in the therapy of diabetes mellitus type 2 patients: an observational cohort study. J Diabetes Metab Disord. 2015;15:1–10.

    Article  Google Scholar 

  41. Duckworth W, Abraira C, Moritz T, Reda D, Emanuele N, Reaven PD, et al. Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med. 2009;360(2):129–39.

    Article  CAS  PubMed  Google Scholar 

  42. Group AtCCRiDS. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008;358(24):2545–59.

    Article  Google Scholar 

  43. King P, Peacock I, Donnelly R. The UK prospective diabetes study (UKPDS): clinical and therapeutic implications for type 2 diabetes. Br J Clin Pharmacol. 1999;48(5):643.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Aroor AR, McKarns S, DeMarco VG, Jia G, Sowers JR. Maladaptive immune and inflammatory pathways lead to cardiovascular insulin resistance. Metabolism. 2013;62(11):1543–52.

    Article  CAS  PubMed  Google Scholar 

  45. Echouffo-Tcheugui JB, Daya N, Lee AK, Tang O, Ndumele CE, Windham BG, et al. Severe hypoglycemia, cardiac structure and function, and risk of cardiovascular events among older adults with diabetes. Diabetes Care. 2021;44(1):248–54.

    Article  PubMed  Google Scholar 

  46. Herman ME, O’Keefe JH, Bell DS, Schwartz SS. Insulin therapy increases cardiovascular risk in type 2 diabetes. Prog Cardiovasc Dis. 2017;60(3):422–34.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors are grateful to all the staff of the Hospital Authority Data Collaboration Laboratory (HADCL) for their assistance. We would also like to acknowledge the S. H. Ho Foundation for its support.

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Author notes

  1. Huan Meng and Ho So contributed equally as co-first author.

Authors and Affiliations

  1. Department of Medicine and Therapeutics, The Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong

    Huan Meng, Ho So, Steven H. Lam & Lai-Shan Tam

  2. Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University, and Liverpool Heart and Chest Hospital, Liverpool, UK

    Steven H. Lam

Authors
  1. Huan Meng
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  2. Ho So
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Contributions

Ho So, LS Tam, Steven Lam, and Huan Meng were involved in the study design. Steven Lam and Huan Meng performed data collection, conducted data analysis, and prepared all figures. Huan Meng and Ho So were responsible for drafting the manuscript, and Ho So and LS Tam reviewed the manuscript for finalization. All authors reviewed the manuscript.

Corresponding author

Correspondence to Lai-Shan Tam.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (Ref. No.: 2021.024).

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

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The authors declare no competing interests.

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Meng, H., So, H., Lam, S.H. et al. Impaired fasting glucose and sulfonylureas increased the risk of major cardiovascular events in patients with inflammatory arthritis. Diabetol Metab Syndr 17, 132 (2025). https://doi.org/10.1186/s13098-025-01689-6

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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