Skip to main content
Download PDF
Download ePub

Cardiovascular risk of gabapentin and pregabalin in patients with diabetic neuropathy

Cardiovascular Diabetology volume 21, Article number: 170 (2022) Cite this article

Abstract

Background

Gabapentin and pregabalin are commonly prescribed medications to treat pain in patients with diabetic neuropathy. Gabapentin and pregabalin can cause fluid retention, which is hypothesized to be associated with cardiovascular diseases. However, whether long-term use of gabapentin and pregabalin is associated with adverse cardiovascular diseases remains unknown. This study aims to examine the association between gabapentin use, pregabalin use and several adverse cardiovascular events.

Methods

This retrospective cohort study used propensity score matching within patient electronic health records (EHRs) from a multicenter database with 106 million patients from 69 health care organizations in the US. The study population comprised 210,064 patients who had a diagnosis of diabetic neuropathy and were prescribed diabetic neuropathy medications in their EHRs. The exposure cohort comprised patients who were prescribed gabapentin or pregabalin to treat diabetic neuropathy. The comparison cohort comprised patients who were not prescribed either gabapentin or pregabalin but were prescribed other drugs to treat diabetic neuropathy. The outcomes of interest were myocardial infarcts, strokes, heart failure, peripheral vascular disease, and venous thromboembolic events. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for 3-month and 5-year risk for adverse cardiovascular events between the propensity score-matched cohorts.

Results

Both gabapentin and pregabalin were associated with increased risk of 5-year adverse cardiovascular events compared with the comparison group. In patients prescribed gabapentin, the highest risk was observed for deep venous thrombosis (HR: 1.58, 95% CI 1.37–1.82), followed by pulmonary embolism (HR: 1.5, 95% CI 1.27–1.76), peripheral vascular disease (HR: 1.37, 95% CI 1.27–1.47), stroke (HR: 1.31, 95% CI 1.2–1.43), myocardial infarction (HR: 1.25, 95% CI 1.14–1.38) and heart failure (HR: 1.14, 95% CI 1.07–1.21). In patients prescribed pregabalin, the highest risk was observed for deep venous thrombosis (HR: 1.57, 95% CI 1.31–1.88), followed by peripheral vascular disease (HR: 1.35, 95% CI 1.22–1.49), myocardial infarction (HR: 1.29, 95% CI 1.13–1.47), pulmonary embolism (HR: 1.28, 95% CI 1.04–1.59), stroke (HR: 1.26, 95% CI 1.12–1.42), and heart failure (HR: 1.2, 95% CI 1.11–1.3). There were significant associations between short-term (3 month) gabapentin use and heart failure, myocardial infarction, peripheral vascular disease, deep venous thrombosis, and pulmonary embolism. Short-term (3 month) pregabalin use was associated with deep venous thrombosis, peripheral vascular disease.

Conclusion

In patients with diabetic neuropathy who were prescribed gabapentin and pregabalin, there is an increased risk for heart failure, myocardial infarction, peripheral vascular disease, stroke, deep venous thrombosis, and pulmonary embolism with long-term use. Our findings suggest that increased risk for adverse cardiovascular events, along with other side effects, the efficacy of pain control and the degree of tolerance of the patient, should be considered when prescribing gabapentin and pregabalin long-term in patients with diabetic neuropathy.

Introduction

The American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) provide annual guidance for clinicians. The standard of medical care in 2022 highlights cardiovascular disease management in patients with diabetes [1], as well as management of complications of diabetes, including diabetic neuropathy [2]. Neuropathic pain in diabetes can be severe and can impact quality of life [2]. Pregabalin and gabapentin are recommended as pharmacologic treatments for diabetic neuropathy [2]. While both medications were initially developed as anticonvulsants, pregabalin was approved to treat diabetic neuropathy in 2014 and gabapentin is often prescribed “off label” to treat diabetic neuropathy [3].

Both gabapentin and pregabalin can cause fluid retention by altering arterial myogenic tone [4]. Although fluid retention is not a recognized risk factor for cardiovascular disease, every medical condition that causes fluid retention—heart failure, nephrotic syndrome, cirrhosis, hypothyroidism, obstructive sleep apnea and Cushing’s syndrome—is associated with an increased risk of myocardial infarcts, strokes, and venous thromboembolic disease [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. Moreover, previous studies have demonstrated that a number of medications that cause fluid retention are associated with an increased risk of adverse cardiovascular events. The list includes cyclo-oxygenase-2 inhibitors (COX-2 inhibitors) [20,21,22,23], nonselective non-steroidal anti-inflammatory drugs (NSAIDs) [22, 24, 25], hormone replacement therapy with estrogens and progestins [26, 27], oral contraceptives[28], insulins[29] and sulfonylureas [30, 31].

The consensus from multiple guidelines and systematic reviews is that gabapentinoids, serotonin norepinephrine reuptake inhibitors and tricyclic antidepressants have the best evidence to support their use in the treatment of diabetic neuropathic pain but comparative effectiveness studies to inform the best choice are lacking [32]. Opioid analgesics should be avoided due to their serious adverse effects and potential for addiction [3]. Long-term use of gabapentin and pregabalin is generally well tolerated and provides sustained efficacy [33, 34]. However, currently it remains unknown whether long-term use of gabapentin and pregabalin in patients with diabetic neuropathy is associated with increased risk for adverse cardiovascular events, including myocardial infarcts, strokes, heart failure, peripheral vascular disease, and venous thromboembolic events.

Methods

Database description

TriNetX research network is a de-identified population-level database with 106 million patients from 69 health care organizations, mostly large academic medical institutions with both inpatient and outpatient facilities at multiple locations across 50 states in the US, covering diverse geographic locations, age groups, racial and ethnic groups, income levels and insurance types [35]. Built-in statistical functions within TriNetX Analytics Platform perform statistical analyses on patient-level data and reports population level data and results without including protected health information (PHI) identifiers. MetroHealth System, Cleveland OH, Institutional Review Board (IRB) has determined that any research using TriNetX is not Human Subject Research and therefore exempt from IRB review.

Study population

The study population comprised of 210,064 patients who had a diagnosis of diabetic neuropathy and were prescribed with diabetic neuropathy medications in their EHRs. The study population was divided into two cohorts: (1) an Exposure cohort consisting of patients who were prescribed gabapentin or pregabalin to treat diabetic neuropathy (International Classification of Diseases (ICD-10) diagnosis code: E11.40 Type 2 diabetes mellitus with diabetic neuropathy, unspecified) and (2) a Comparison cohort consisting of patients who were not prescribed either gabapentin or pregabalin but were prescribed other commonly-prescribed drugs (topiramate, duloxetine, tapentadol, capsaicin, nortriptyline, carbamazepine, venlafaxine, amitriptyline, and mexiletine) to control diabetic neuropathy [36, 37]. Patients who took no prescription medication for diabetic neuropathy were excluded since they were likely to have relatively mild diabetic neuropathy. We controlled for severity of diabetic neuropathy by including only patients in both the exposure cohort and the comparison cohort who were taking at least one prescription medication for diabetic neuropathy. To investigate the long-term effect, the long-term users were defined with the additional requirement that they had another drug prescription recorded more than 3 years after the first prescription.

Statistical analysis

Outcome measures

The outcomes of interest were diagnoses of cardiovascular diseases including heart failure (ICD-10: I50 Heart failure), myocardial infarction (ICD-10: I21 Acute myocardial infarction), peripheral vascular disease (ICD-10: I73.9 Peripheral vascular diseases, unspecified), stroke (ICD-10: I63 Cerebral infarction), deep venous thrombosis (ICD-10: I82.40 Acute embolism and thrombosis of unspecified deep veins of lower extremity), and pulmonary embolism (ICD-10: I26 Pulmonary embolism).

Covariates

Covariates included demographics (age, gender, race, and ethnicity), risk factors of cardiovascular disease, initial drug indications including approved, and off-label uses, related medications, adverse socioeconomic circumstances, and specific risk factors for each disease.

The risk factors of cardiovascular diseases were hypertension (ICD-10: I10-I16 Hypertensive diseases), high cholesterol (ICD-10: E78.0 Pure hypercholesterolemia), obesity (ICD-10: E66 Overweight and obesity), diabetes (ICD-10: E08-E13 Diabetes mellitus), alcohol abuse (ICD-10: F10.1 Alcohol abuse), tobacco use (ICD-10: Z72.0 Tobacco use), use of NSAID (ICD-10: Z79.1 long-term (current) use of non-steroidal anti-inflammatories), obstructive sleep apnea [ICD-10: G47.33 Obstructive sleep apnea (adult) (pediatric)], end stage renal disease (ICD-10: N18.6 End stage renal disease), pre-existing heart failure (ICD-10: I50 Heart failure) and pre-existing peripheral vascular disease (ICD-10: I73.9 Peripheral vascular diseases, unspecified). Adverse socioeconomic circumstances were determined by incorporating ICD-10: Z55-Z65 Persons with potential health hazards related to socioeconomic and psychosocial circumstances. The medication list, presented in Additional file 1: Table S2, includes the medications for diabetic neuropathy, medications that can cause fluid retention and medications that can raise the blood pressure.

The initial indications were diabetic neuropathy (ICD-10: E11.40 Type 2 diabetes mellitus with diabetic neuropathy, unspecified), seizure (ICD-10: G40 Epilepsy and recurrent seizures), postherpetic neuralgia (ICD-10: B02.29 Other postherpetic nervous system involvement), fibromyalgia (ICD-10: M79.7 Fibromyalgia), pain (ICD-10: R52 Pain, unspecified), neuropathic pain (ICD-10: M79.2 Neuralgia and neuritis, unspecified), restless leg syndrome (ICD-10: G25.81 Restless leg syndrome).

The specific risk factors and complications for heart failure (ICD-10: I50 Heart failure) were myocardial infarction (ICD-10: I21 Acute myocardial infarction), myocarditis (ICD-10: I51.4 Myocarditis, unspecified), and arrhythmias (ICD-10: I49 Other cardiac arrhythmias); for myocardial Infarction (ICD-10: I21 Acute myocardial infarction) were sudden cardiac arrest (ICD-10: Z86.74 Personal history of sudden cardiac arrest), and metabolic syndrome (ICD-10: E88.81 Metabolic syndrome); for stroke (ICD-10: I63 Cerebral infarction) were atrial fibrillation (ICD-10: I48 Atrial fibrillation and flutter), arrhythmias (ICD-10: I49 Other cardiac arrhythmias), and depression (ICD-10: F32 Depressive episode), for peripheral vascular disease (ICD-10: I73.9 Peripheral vascular diseases, unspecified) were stroke (ICD-10: I63 Cerebral infarction), myocardial infarction (ICD-10: I21 Acute myocardial infarction), and atherosclerosis (ICD-10: I70 Atherosclerosis); for deep venous thrombosis (ICD-10: I82.40 Acute embolism and thrombosis of unspecified deep veins of lower extremity) were inflammatory bowel disease (ICD-10: K51.9 Ulcerative colitis, unspecified), and cancer (ICD-10: C80.1 Malignant (primary) neoplasm, unspecified); for pulmonary embolism (ICD-10: I26 Pulmonary embolism) was cancer (ICD-10: C80.1 Malignant (primary) neoplasm, unspecified). The full list of covariates, their standardized names, codes, and data types in TriNetX are described in Additional file 1: Table S2.

The Exposure cohort and Comparison cohort were propensity-score matched (1:1 matching using the nearest neighbor greedy matching algorithm) for covariates described above. Kaplan–Meier analysis was performed to estimate the risk of each cardiovascular adverse event in the Exposure and Comparison cohorts. The time index for the exposure and the comparison is the drug first prescription date. 3-month outcomes and 5-year outcomes are determined by following patients with studied outcomes within 3-month and 5-year time frame since the drug prescription. Hazard ratios, 95% confidence interval and p-value were calculated at significance set at p-value < 0.05 (two-sided).

Results

Patient characteristics

Table 1 and Additional file 1: Table S3 presents characteristics of the gabapentin and comparison groups for long-term use before and after propensity score matching for covariates of deep venous thrombosis. Data for pregabalin and patient characteristics for short-term drug use are in the Supplement (Additional file 1: Table S4–S6). Patients with diabetic neuropathy who were prescribed gabapentin were younger than those prescribed comparison drugs (60.5 versus 62.8), comprised fewer women, more African Americans, Hispanics and had fewer comorbidities. After propensity-score matching, the two cohorts were balanced.

Table 1 The characteristics of the patients who were prescribed gabapentin and comparison drugs for long term before and after propensity-score matching for covariates of deep venous thrombosis
Full size table

Risk of 5-year adverse cardiovascular events associated with gabapentin and pregabalin in patients with diabetic neuropathy

As shown in Fig. 1, both gabapentin and pregabalin were associated with increased risk of 5-year adverse cardiovascular events compared with the comparison group. In patients prescribed gabapentin, the highest risk was observed for deep venous thrombosis (HR: 1.58, 95% CI 1.37–1.82), followed by pulmonary embolism (HR: 1.5, 95% CI 1.27–1.76), peripheral vascular disease (HR: 1.37, 95% CI 1.27–1.47), stroke (HR: 1.31, 95% CI 1.2–1.43), myocardial infarction (HR: 1.25, 95% CI 1.14–1.38) and heart failure (HR: 1.14, 95% CI 1.07–1.21). In patients prescribed pregabalin, the highest risk was observed for deep venous thrombosis (HR: 1.57, 95% CI 1.31–1.88), followed by peripheral vascular disease (HR: 1.35, 95% CI 1.22–1.49), myocardial infarction (HR: 1.29, 95% CI 1.13–1.47), pulmonary embolism (HR: 1.28, 95% CI 1.04–1.59), stroke (HR: 1.26, 95% CI 1.12–1.42), and heart failure (HR: 1.2, 95% CI 1.11–1.3).

Fig. 1
figure 1

Risk of 5-year outcomes in propensity-score matched exposure groups and comparison groups in the patients who had taken the drugs long term. (ARI: absolute risk increase, NNH: number need to harm, MI: myocardial infarction, PVD: peripheral vascular disease, DVT: deep venous thrombosis, PE: pulmonary embolism)

Full size image

Risk of 3-month adverse cardiovascular events associated with gabapentin and pregabalin in patients with diabetic neuropathy

As shown in Fig. 2, gabapentin was associated with an increased risk of deep venous thrombosis (HR: 1.39, 95% CI 1.18–1.65), pulmonary embolism (HR: 1.27, 95% CI 1.05–1.54), peripheral vascular disease (HR: 1.17, 95% CI 1.08–1.27), myocardial infarction (HR: 1.15, 95% CI 1.02–1.3), and heart failure (HR: 1.1, 95% CI 1.04–1.16) compared with the comparison group within the 3-month time frame following the drug treatment. There was no significant association between gabapentin and risk of stroke (HR: 1.05, 95% CI 0.96–1.16). Pregabalin was associated with an increased risk of 3-month adverse cardiovascular events for deep venous thrombosis (HR: 1.27, 95% CI 1.05–1.54), peripheral vascular disease (HR: 1.18, 95% CI 1.08–1.29). There was no association between pregabalin and risk of 3-month adverse cardiovascular events for pulmonary embolism (HR: 1.2, 95% CI 0.97–1.5), myocardial infarction (HR: 1.01, 95% CI 0.88–1.15), stroke (HR: 0.9, 95% CI 0.8–1.02), and heart failure (HR: 1.01, 95% CI 0.95–1.08).

Fig. 2
figure 2

Risk of 3-month outcomes in propensity-score matched exposure groups and comparison groups in the patients who had ever taken the drugs. (ARI: absolute risk increase, NNH: number need to harm, MI: myocardial infarction, PVD: peripheral vascular disease, DVT: deep venous thrombosis, PE: pulmonary embolism)

Full size image

Discussion

Using the large-scale nation-wide database of patient de-identified TriNetX EHRs, our study found that there is an increased risk of adverse cardiovascular events, including heart failure, myocardial infarction, peripheral vascular disease, stroke, deep venous thrombosis, and pulmonary embolism in patients with diabetic neuropathy, following long-term use of gabapentin and pregabalin, with highest risk for deep venous thrombosis. There were significant associations between short-term (3 month) gabapentin use and heart failure, myocardial infarction, peripheral vascular disease, deep venous thrombosis, and pulmonary embolism. Short-term (3 month) pregabalin use was associated with deep venous thrombosis and peripheral vascular disease.

Associations between gabapentin, pregabalin and heart failure have been reported in case reports [38, 39], but not in observational studies [40, 41]. These case reports and observational studies are limited to short-term follow up [38,39,40,41]. To the best of our knowledge, this study is among the first to report that long-term use of gabapentin and pregabalin is associated with increased risk for adverse cardiovascular events in patients with diabetic neuropathy. While the cardiovascular effects of gabapentin and pregabalin need to be verified in other populations that use these drugs, our findings call for thoughtful consideration of risk and benefit when deciding to use gabapentin and pregabalin long-term in patients with diabetic neuropathy, and possibly in other populations as well.

One possible explanation for the findings is that gabapentin and pregabalin can alter arterial myogenic tone and cause fluid retention [4]. Fluid retention causes either an increase in cardiac output or an increase in blood pressure [42]. Velocity of blood flow increases either way, thereby increasing turbulence of blood flow [43]. Increased turbulence reduces shear stress upon arterial walls, thereby increasing endothelial dysfunction, which is a well-established cardiovascular risk factor [44]. However, our finding cannot rule out other potential mechanisms. Future research is necessary to further understand the underlying mechanism of the observed increased risk of CVDs among patients with diabetic neuropathy who were prescribed gabapentin and pregabalin. Additional work is needed to determine if other medications that cause fluid retention are also associated with increased adverse cardiovascular events. Anti-hypertensive medications that cause fluid retention, notably calcium channel blockers, alpha receptor antagonists and hydralazine, have favorable cardiovascular effects. Aromatase inhibitors and androgen deprivation therapy cause fluid retention but increase the longevity of cancer patients. For medications that do not improve longevity, do not lower blood pressure (BP) and that are used long-term, such as androgens, corticosteroids (when used to treat non-life-threatening conditions), mineralocorticoids, pramipexole and ropinirole, patients and clinicians need to know if there is an increased risk of cardiovascular disease. For androgens, the research literature is inconclusive concerning cardiovascular risk: some studies have documented an increased risk of adverse cardiovascular events while other studies have failed to do so [45, 46]. Cardiovascular safety data is needed for medications that cause fluid retention but do not increase longevity or lower BP.

If an increased risk of adverse cardiovascular events is also associated with other medications that cause fluid retention, then our study has implications for the pharmaceutical industry and for the entities responsible for regulating pharmaceutical products: the Food and Drug Administration in the United States, the European Medicines Agency in the European Union, the Medicines and Healthcare Products Regulatory Agency in Britain, and the Health Products and Food Branch of Health Canada. If fluid retention is a risk factor for cardiovascular disease, then data regarding the fluid retention properties of medications would be relevant to the drug approval process. In that circumstance, for medications that do not extend life, do not lower BP and are intended to be used long term, regulatory agencies should require new drug applications to provide fluid retention data. Cardiovascular safety data would be warranted for medications that cause fluid retention.

Many patients take medications that cause fluid retention. One implication of our study is that it may be possible to mitigate the cardiovascular consequences of medications that cause fluid retention, at least in part, by simultaneously prescribing a diuretic. This is a potential area for future research.

Numerous risk factors for cardiovascular diseases (CVDs) have been recognized, including age, gender, smoking status, hypertension, diabetes, hypercholesterolemia, physical inactivity, obstructive sleep apnea and overweight [16, 47]. Findings from this study suggest that long-term use of gabapentin/pregabalin is another risk factor for CVDs in patients with diabetic neuropathy. Future research is needed to understand whether these risk factors act synergistically or independently in increasing the risk for CVDs in specific patient populations.

Several limitations of this study should be noted. First, the patients in the TriNetX EHR database represent people who had medical encounters with the 69 contributing health care systems. The generalizability of the results needs to be validated in other national data resources. Second, there is no data linkage between the drug prescriptions and diagnosis codes. We assumed that if the diagnosis of diabetic neuropathy happened before the drug prescription, then the patients were taking the drug for treating diabetic neuropathy. Nonetheless, there might be other indications for prescribing these drugs. We addressed this problem by controlling the other initial uses in propensity score matching. Third, to control for severity of diabetic neuropathy, we excluded patients not taking medications for treatment of diabetic neuropathy since they might have relatively mild diabetic neuropathy. The control cohort included patients taking other medications to treat diabetic neuropathy. The exposure group could also take other medications to treat diabetic neuropathy, in addition to gabapentin and pregabalin (“polypharmacy”). For propensity-score matching, the exposure and control cohorts were matched for specific drugs that are commonly prescribed to treat diabetic neuropathy. Fourth, due to the nature of EHR data, TriNetX platform captures patient information that is entered during patients’ encounter with healthcare organization. Consequently, there is no guarantee of completeness for specific research purposes. For example, other events (e.g., diet, certain lifestyle factors, employment) that happened outside of patient medical encounters may not be captured in patient EHRs. However, because we compared exposure and control cohorts all from TriNetX database, selection bias should not substantially affect the relative risk analyses. Though the exposure and control cohorts were extensively propensity-score matched for demographics, original disease indications, risk factors, and other commonly prescribed medications, we acknowledge the limitation of unmeasured confounders that are not included in patient EHRs. Fifth, drug duration was an important covariate. We used a computerized database of medical information to define exposure to gabapentin and pregabalin. The database identifies only that the medications were prescribed, not that they were consumed, and electronic health records cannot provide information about how long patients actually take any given drug. Nonetheless, automated pharmacy records are generally reliable sources of information on drug use [48, 49]. We addressed this problem by defining long-term users with an additional query for the patients who have another drug prescription recorded more than 3 years after the first drug prescription. Sixth, we could not determine the doses of gabapentin or pregabalin that were prescribed. It is possible that the cardiovascular risk of these medications is dose dependent such that low doses may not be associated with an increased risk of adverse cardiovascular events.

Conclusion

We identified an increased risk of adverse cardiovascular events associated with use of gabapentin and pregabalin in patients with diabetic neuropathy. Our findings need to be verified in other populations of gabapentin and pregabalin users and with other medications that cause fluid retention. If our results are generalizable, increased risk of adverse cardiovascular outcomes, along with other side effects, the efficacy of pain control and the degree of tolerance of the patient, should inform the decision-making process when prescribing gabapentin, pregabalin, and perhaps other medications that cause fluid retention.

Availability of data and materials

The data that support the findings of this study are available from TriNetX [35] (https://trinetx.com/).

Abbreviations

EHRs:

Electronic health records

CVDs:

Cardiovascular diseases

HRs:

Hazard ratios

CIs:

Confidence intervals

NSAIDs:

Nonselective non-steroidal anti-inflammatory drugs

BP:

Blood pressure

SMD:

Standardized mean difference

References

  1. American Diabetes Association Professional Practice Committee. 10. Cardiovascular disease and risk management: standards of medical care in diabetes—2022. Diabetes Care. 2021;45(Supplement_1):S144–74. https://doi.org/10.2337/dc22-S010.

    Article  Google Scholar 

  2. American Diabetes Association Professional Practice Committee. 12. Retinopathy, neuropathy, and foot care: standards of medical care in diabetes—2022. Diabetes Care. 2021;45(Supplement_1):S185–94. https://doi.org/10.2337/dc22-S012.

    Article  Google Scholar 

  3. Goodman CW, Brett AS. A clinical overview of off-label use of gabapentinoid drugs. JAMA Intern Med. 2019;179(5):695. https://doi.org/10.1001/jamainternmed.2019.0086.

    Article  PubMed  Google Scholar 

  4. Largeau B, et al. Gabapentinoid-induced peripheral edema and acute heart failure: a translational study combining pharmacovigilance data and in vitro animal experiments. Biomed Pharmacother. 2022;149: 112807. https://doi.org/10.1016/j.biopha.2022.112807.

    Article  CAS  PubMed  Google Scholar 

  5. Hjalmarsson C, et al. Risk of stroke in patients with heart failure and sinus rhythm: data from the Swedish Heart Failure Registry. ESC Heart Fail. 2021;8(1):85–94. https://doi.org/10.1002/ehf2.13091.

    Article  PubMed  Google Scholar 

  6. Greenberg B, et al. Ejection fraction, B-type natriuretic peptide and risk of stroke and acute myocardial infarction among patients with heart failure. Clin Cardiol. 2019;42(2):277–84. https://doi.org/10.1002/clc.23140.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Berger JS, et al. Risk of ischemic stroke in patients newly diagnosed with heart failure: focus on patients without atrial fibrillation. J Cardiac Fail. 2019;25(6):436–47. https://doi.org/10.1016/j.cardfail.2018.03.012.

    Article  Google Scholar 

  8. Kang S-H, et al. Risk of stroke in congestive heart failure with and without atrial fibrillation. Int J Cardiol. 2017;248:182–7. https://doi.org/10.1016/j.ijcard.2017.07.056.

    Article  PubMed  Google Scholar 

  9. Fanola CL, et al. Incident heart failure and long-term risk for venous thromboembolism. J Am Coll Cardiol. 2020;75(2):148–58. https://doi.org/10.1016/j.jacc.2019.10.058.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Go AS, et al. Primary nephrotic syndrome and risks of ESKD, cardiovascular events, and death: the Kaiser permanente nephrotic syndrome study. JASN. 2021;32(9):2303–14. https://doi.org/10.1681/ASN.2020111583.

    Article  CAS  PubMed  Google Scholar 

  11. Kerlin BA, Ayoob R, Smoyer WE. Epidemiology and pathophysiology of nephrotic syndrome-associated thromboembolic disease. Clin J Am Soc Nephrol. 2012;7(3):513–20. https://doi.org/10.2215/CJN.10131011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ambrosino P, et al. The risk of venous thromboembolism in patients with cirrhosis. Thromb Haemost. 2017;26(01):139–48.

    Article  Google Scholar 

  13. Xiong J, et al. Cirrhosis and risk of stroke: a systematic review and meta-analysis. Atherosclerosis. 2018;275:296–303.

    Article  CAS  Google Scholar 

  14. Ning Y, et al. What is the association of hypothyroidism with risks of cardiovascular events and mortality? A meta-analysis of 55 cohort studies involving 1,898,314 participants. BMC Med. 2017;15(1):1–15.

    Article  Google Scholar 

  15. Danescu LG, et al. Venous thromboembolism in patients hospitalized with thyroid dysfunction. Clin Appl Thromb Hemost. 2009;15(6):676–80.

    Article  Google Scholar 

  16. Shah NA, Yaggi HK, Concato J, Mohsenin V. Obstructive sleep apnea as a risk factor for coronary events or cardiovascular death. Sleep and Breathing. 2010;14(2):131–6.

    Article  Google Scholar 

  17. Parish JM, Somers VK. Obstructive sleep apnea and cardiovascular disease. Mayo Clin Proc. 2004;79(8):1036–46.

    Article  Google Scholar 

  18. Lippi G, Mattiuzzi C, Franchini M. Sleep apnea and venous thromboembolism. Thromb Haemost. 2015;114(11):958–63.

    Article  Google Scholar 

  19. van der Pas R, Leebeek F, Hofland L, De Herder W, Feelders R. Hypercoagulability in C ushing’s syndrome: prevalence, pathogenesis and treatment. Clin Endocrinol. 2013;78(4):481–8.

    Article  Google Scholar 

  20. Bombardier C, et al. Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. N Engl J Med. 2000;343(21):1520–8. https://doi.org/10.1056/NEJM200011233432103.

    Article  CAS  PubMed  Google Scholar 

  21. Nussmeier NA, Langford RM, Boyce SW. Complications of the COX-2 inhibitors parecoxib and valdecoxib after cardiac surgery. N Engl J Med. 2005. https://doi.org/10.1056/NEJMoa050330.

    Article  PubMed  Google Scholar 

  22. McGettigan P, Henry D. Cardiovascular risk and inhibition of cyclooxygenase: a systematic review of the observational studies of selective and nonselective inhibitors of cyclooxygenase 2. JAMA. 2006;296(13):1633–44.

    Article  CAS  Google Scholar 

  23. Jüni P, Nartey L, Reichenbach S, Sterchi R, Dieppe PA, Egger M. Risk of cardiovascular events and rofecoxib: cumulative meta-analysis. Lancet. 2004;364:2021.

    Article  Google Scholar 

  24. Antman EM, Bennett JS, Daugherty A, Furberg C, Roberts H, Taubert KA. Use of nonsteroidal antiinflammatory drugs: an update for clinicians: a scientific statement from the american heart association. Circulation. 2007;115(12):1634–42. https://doi.org/10.1161/CIRCULATIONAHA.106.181424.

    Article  PubMed  Google Scholar 

  25. Cooper C, et al. Safety of oral non-selective non-steroidal anti-inflammatory drugs in osteoarthritis: what does the literature say? Drugs Aging. 2019;36(S1):15–24. https://doi.org/10.1007/s40266-019-00660-1.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Rossouw JE. Writing Group for the Women’s Health Initiative Investigators. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. JAMA. 2002;288:321–33.

    Article  CAS  Google Scholar 

  27. Anderson GL, et al. Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women’s Health Initiative randomized controlled trial. JAMA. 2004;291(14):1701–12.

    Article  CAS  Google Scholar 

  28. Roach REJ, Helmerhorst FM, Lijfering WM, Stijnen T, Algra A, Dekkers OM. Combined oral contraceptives: the risk of myocardial infarction and ischemic stroke. Cochrane Database Syst Rev. 2015. https://doi.org/10.1002/14651858.CD011054.pub2.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Roumie CL, et al. Association between intensification of metformin treatment with insulin vs sulfonylureas and cardiovascular events and all-cause mortality among patients with diabetes. JAMA. 2014;311(22):2288. https://doi.org/10.1001/jama.2014.4312.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Pantalone KM, et al. The risk of developing coronary artery disease or congestive heart failure, and overall mortality, in type 2 diabetic patients receiving rosiglitazone, pioglitazone, metformin, or sulfonylureas: a retrospective analysis. Acta Diabetol. 2009;46(2):145–54. https://doi.org/10.1007/s00592-008-0090-3.

    Article  CAS  PubMed  Google Scholar 

  31. Schramm TK, et al. Mortality and cardiovascular risk associated with different insulin secretagogues compared with metformin in type 2 diabetes, with or without a previous myocardial infarction: a nationwide study. Eur Heart J. 2011. https://doi.org/10.1093/eurheartj/ehr077.

    Article  PubMed  Google Scholar 

  32. Feldman EL, et al. Diabetic neuropathy. Nat Rev Dis Primers. 2019;5(1):42. https://doi.org/10.1038/s41572-019-0097-9.

    Article  PubMed Central  Google Scholar 

  33. Putzke JD, Richards JS, Kezar L, Hicken BL, Ness TJ. Long-term use of gabapentin for treatment of pain after traumatic spinal cord injury. Clin J Pain. 2002;18(2):116–21. https://doi.org/10.1097/00002508-200203000-00007.

    Article  PubMed  Google Scholar 

  34. Onouchi K, Koga H, Yokoyama K, Yoshiyama T. An open-label, long-term study examining the safety and tolerability of pregabalin in Japanese patients with central neuropathic pain. J Pain Res. 2014;7:439–47. https://doi.org/10.2147/JPR.S63028.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. “TriNetX,” TriNetX. https://trinetx.com/. Accessed 1 Jun 2022.

  36. Azhary H, Farooq MU, Bhanushali M, Majid A, Kassab MY. Peripheral neuropathy: differential diagnosis and management. Am Fam Physician. 2010;81(7):6.

    Google Scholar 

  37. Drugs used to treat Diabetic Peripheral Neuropathy. Drug.com. https://www.drugs.com/condition/diabetic-neuropathy.html?submitted=true&category_id=&include_rx=true&include_otc=true&show_off_label=true&only_generics=true#sortby. Accessed 1 June 2022.

  38. Erdoğan G, Ceyhan D, Güleç S. Possible heart failure associated with pregabalin use: case report. Agri. 2011;23:80.

    PubMed  Google Scholar 

  39. Murphy N, Mockler M, Ryder M, Ledwidge M, McDonald K. Decompensation of chronic heart failure associated with pregabalin in patients with neuropathic pain. J Cardiac Fail. 2007;13(3):227–9. https://doi.org/10.1016/j.cardfail.2006.11.006.

    Article  Google Scholar 

  40. Lund M, Poulsen G, Pasternak B, Worm Andersson N, Melbye M, Svanström H. Use of pregabalin and worsening heart failure: a nationwide cohort study. Drug Saf. 2020;43(10):1035–44. https://doi.org/10.1007/s40264-020-00969-6.

    Article  CAS  PubMed  Google Scholar 

  41. Ho JM-W, et al. Pregabalin and heart failure: a population-based study. Pharmacoepidemiol Drug Saf. 2017;26(9):1087–92. https://doi.org/10.1002/pds.4239.

    Article  CAS  PubMed  Google Scholar 

  42. Blankfield RP. Implications of calculated intravascular volume changes upon atherosclerotic cardiovascular disease. Clin Hemorheol Microcirc. 2008;38(2):75–81.

    PubMed  Google Scholar 

  43. Blankfield RP. Calculated effect of fluid retention upon velocity of blood flow and turbulence: implications for atherosclerosis. Clin Hemorheol Microcirc. 2011;47(2):79–86.

    Article  CAS  Google Scholar 

  44. Hadi HA, Carr CS, Al Suwaidi J. Endothelial dysfunction: cardiovascular risk factors, therapy, and outcome. Vasc Health Risk Manag. 2005;1(3):183–98.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Basaria S, et al. Adverse events associated with testosterone administration. N Engl J Med. 2010;363(2):109–22.

    Article  CAS  Google Scholar 

  46. Kloner RA, Carson C, Dobs A, Kopecky S, Mohler ER. Testosterone and cardiovascular disease. J Am Coll Cardiol. 2016;67(5):545–57.

    Article  CAS  Google Scholar 

  47. Wood D. Established and emerging cardiovascular risk factors. Am Heart J. 2001;141(2):S49–57. https://doi.org/10.1067/mhj.2001.109951.

    Article  CAS  PubMed  Google Scholar 

  48. West SL, Savitz DA, Koch G, Strom BL, Guess HA, Hartzema A. Recall accuracy for prescription medications: self-report compared with database information. Am J Epidemiol. 1995;142(10):1103–12.

    Article  CAS  Google Scholar 

  49. Leister KA, Drew Edwards WA, Christensen DB, Clark H. A comparison of patient drug regimens as viewed by the physician, pharmacist and patient”. Med Care. 1981;19:658–64.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

We acknowledge fundings from National Institute on Aging (Grants nos. AG057557, AG061388, AG062272, AG076649), National Institute on Alcohol Abuse and Alcoholism (Grant no. AA029831), the Clinical and Translational Science Collaborative (CTSC) of Cleveland (Grant no. TR002548), National Cancer Institute Case Comprehensive Cancer Center (CA221718, CA043703, CA2332216). The funders have no roles in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Yiheng Pan & Rong Xu

  2. Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Pamela B. Davis

  3. The Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, OH, USA

    David C. Kaebler

  4. Department of Family Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Robert P. Blankfield

Authors
  1. Yiheng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pamela B. Davis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David C. Kaebler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert P. Blankfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YP, RPB, and RX conceived and designed this study. YP implemented the statistical analysis. DCK facilitated access to TriNetX. RX supervised the study and obtained the funding. PBD critically contributed to study design, results interpretation, and manuscript preparation. All the authors read and approved the manuscript.

Corresponding authors

Correspondence to Robert P. Blankfield or Rong Xu.

Ethics declarations

Ethics approval and consent to participate

The MetroHealth System, Cleveland, Ohio, institutional review board has determined that any research using TriNetX is not human participants research and therefore is exempt from institutional review board review.

Consent for publication

Not applicable.

Competing interests

RPB has a patent for the concept that one can use markers of fluid retention to gauge the cardiovascular risk of a drug. No other disclosures were reported.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Outcomes and their standardized names, codes and data types that are used in the TriNetX database. Table S2. Covariates and their standardized names, codes and data types that are used in the TriNetX database. Table S3. The characteristics of the patients who were prescribed gabapentin and comparison drugs for long term before and after propensity-score matching for covariates of deep venous thrombosis. Table S4. The characteristics of the patients who were prescribed pregabalin and comparison drugs for long term before and after propensity-score matching for covariates of deep venous thrombosis. Table S5. The characteristics of the patients who were prescribed gabapentin and comparison drugs before and after propensity-score matching for covariates of deep venous thrombosis. Table S6. The characteristics of the patients who were prescribed pregabalin and comparison drugs before and after propensity-score matching for covariates of deep venous thrombosis.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, Y., Davis, P.B., Kaebler, D.C. et al. Cardiovascular risk of gabapentin and pregabalin in patients with diabetic neuropathy. Cardiovasc Diabetol 21, 170 (2022). https://doi.org/10.1186/s12933-022-01610-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12933-022-01610-9

Keywords

Cardiovascular Diabetology

ISSN: 1475-2840