You are here

Article Text

Article menu

Download PDFPDF

Cardiovascular medicine
Original research
Investigating the relationship between opium use and coronary slow flow: a propensity score matched case–control study
  1. Leili Abdan1,
  2. Soroush Nematollahi1,
  3. Farzad Masoudkabir1,
  4. Arash Jalali1,2,
  5. Mina Pashang1,
  6. Ali Vasheghani-Farahani3,
  7. Kaveh Hosseini1,
  8. Hamidreza Pourhosseini1
  1. 1Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
  2. 2Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
  3. 3Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
  1. Correspondence to Dr Farzad Masoudkabir; farzad.masoudkabir{at}gmail.com

Abstract

Objectives Recent studies have suggested a potential link between opium consumption and microvascular dysfunction in coronary arteries, which may contribute to the development of coronary slow-flow syndrome. This study aims to investigate the relationship between opium use and coronary slow-flow syndrome.

Design and setting This retrospective study analysed medical records of patients who underwent coronary angiography at the Tehran Heart Center from 2006 to 2020. It focused on those with coronary slow flow phenomenon (CSFP) or non-obstructive coronary artery disease, excluding patients with significant left ventricular dysfunction (left ventricular ejection fraction <40%), previous coronary revasculariation, arrhythmias or coronary artery ectasia. The coronary slow flow was assessed using thrombolysis in myocardial infarction flow grade and frame count. Propensity score matching and inverse probability weighting were applied to minimise confounding variables. Conditional logistic regression and logistic regression models were then used to examine the association between opium use and coronary slow flow, controlling for potential confounders.

Results Among 21 835 patients with normal coronary angiograms, 767 were identified with CSFP and matched with 3068 controls. The mean age of CSFP patients was 51.59 years, with 64.6% male. Opium use was similar, reported in 8.9% of CSFP patients and 9.5% of controls (p=0.96). Within the CSFP group, opium use was associated with more extensive coronary artery involvement (33.3% vs 18.9%, p=0.03). Initial analysis indicated a higher risk for CSFP in opium users (OR: 1.74, p=0.001), but after adjustments, no significant association was found (OR: 1.06, p=0.70; OR: 1.15, p=0.55).

Conclusion Our study indicates that opium use is not an independent risk factor for CSFP but may exacerbate the severity of coronary artery involvement, noted by a higher incidence of multivessel disease among users. This suggests that opium may affect the extent of coronary artery issues rather than cause CSFP directly.

  • Coronary intervention
  • Coronary heart disease
  • Tobacco Use
  • Substance misuse

Data availability statement

Data are available on reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2024-090471

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    STRENGTHS AND LIMITATIONS OF THIS STUDY

    • Comprehensive assessment of 21 835 medical records and a relatively large sample size.

    • Use of propensity score matching and inverse probability weighting to adjust for confounding factors.

    • Retrospective nature of the study, relying on patient self-reports for opium use without laboratory confirmation, which may lead to under-reporting and misreporting due to social stigma.

    • Lack of information on the method of opium consumption, purity and duration of use, which could impact the results.

    • Study focused on a single centre, limiting the generalisability.

    Introduction

    Coronary artery disease (CAD) remains the primary cause of mortality in developed nations and is rapidly becoming a significant health concern in developing countries.1 Current estimates indicate that approximately 126 million individuals are affected by CAD globally,2 with projections suggesting a prevalence of 1845 per 100 000 by 2030.2 Atherosclerosis is a major contributor to CAD and is responsible for a significant proportion of deaths in high-stress societies, affecting around 11 million people and accounting for 44% of deaths in the USA.3 4

    The coronary slow flow phenomenon (CSFP) is characterised by diminished blood flow in the coronary arteries, potentially causing chest pain despite the absence of significant arterial blockage.5 Initially described in 1972 by Tambe et al, who noted delayed vessel opacification during angiography,6 CSFP has since been recognised as a distinct clinical entity, also known as coronary slow flow syndrome or initial slow flow of coronary arteries.6–8 Various theories have been proposed to explain this syndrome, including vascular endothelial dysfunction, flow dysfunction, microvascular and platelet dysfunction.7 8 Specific blood markers such as C reactive protein (CRP) and endothelin-1 have been used to characterise coronary vessels in this condition.9

    Two primary approaches have been used to define the threshold contrast flow indicative of ‘slow flow’. The thrombolysis in myocardial infarction (TIMI) flow grade is a semiquantitative index that scores epicardial artery contrast flow from TIMI-0 (no flow) to TIMI-3 (normal flow), with TIMI-2 flow involving the delayed filling of the distal vessels, considered present when three or more heartbeats are required to opacify the distal vasculature. This approach is frequently used in diagnosing the no-reflow phenomenon and has also been applied to diagnose CSFP.10 An alternative approach is the TIMI frame count method. This quantitative measure assesses the number of cine frames required to opacify the distal vasculature, with a reference value established at 21±3 frames by Gibson et al.11

    Several risk factors increase the risk of CAD, including dyslipidaemia (DLP), cigarette smoking, hypertension (HTN), diabetes mellitus (DM), age, obesity and gender.12–14 Identifying the risk factors of CAD is crucial to prevent further complications. Drug addiction, a pervasive issue in many countries, leads to behavioural and social disorders, adversely affecting physical health and causing substantial economic losses. It also contributes to a more stressful societal environment.15 16 While some substances such as cocaine appear to enhance cardiovascular performance,17 similar beliefs exist regarding opium. Notably, during initial myocardial infarction treatment, medications such as oxygen, nitrates, morphine, aspirin and beta-blockers are administered, leading some to believe in the cardiovascular benefits of morphine and its derivatives.18 19

    However, extensive research has not supported these claims. Opium and morphine can alter physiological functions, weaken the immune system,20 increase infection and mortality risks,21 induce biochemical changes22 and decrease oxygen consumption. Additionally, morphine exposure significantly reduces the expression of various interferons (IFN-α, IFN-γ) in macrophages, potentially leading to cardiovascular complications.23 Moreover, opium can impair blood flow in coronary microcirculation. Some studies suggest that opium may exacerbate microvascular angina, potentially leading to CSFP.24 25 A recent meta-analysis revealed that opium consumption did not significantly affect mortality rates or ejection fraction (EF), leaving the true impact of opium on CAD outcomes still uncertain.26

    Opium is the most commonly used substance among the population of Tehran. According to the Tehran Cohort Study, the prevalence of substance use in Tehran is estimated at 5.6%, with opium accounting for 95.8% of those cases.27 Given the relatively high prevalence of smoking and opium use in Iranian society, it is imperative to assess the relationship between opium consumption and the development of diseases such as CSFP. Understanding the mechanisms and interactions between these factors and improving patient outcomes are critical goals of this study.

    Methods and materials

    Study design and population

    We reviewed the medical records of all patients who underwent coronary angiography (CAG) at the Tehran Heart Center from 2006 to 2020. We included patients with slow blood flow or less than 25% narrowing in their major heart arteries. Patients with a left ventricular ejection fraction of less than 40%, a history of coronary revascularisation, heart rhythm problems or coronary artery enlargement were excluded. Figure 1 illustrates the step-by-step process of participant inclusion and exclusion.

    Figure 1
    Figure 1

    The flow chart of inclusion and exclusion process. CSFP, coronary slow flow phenomenon, LVEF, left ventricle ejection fraction; TIMI, thrombolysis in myocardial infarction.

    Instruments and materials

    The baseline characteristics and demographics of the patients, including age, gender, history of cardiovascular disease and smoking history, were extracted from medical records. Information about opium use was obtained through self-reported records. Individuals currently using opium were classified as ‘users’ and ‘non-users’ referred to patients who are not currently using opium, regardless of whether they quit or never used. However, details regarding the duration, purity and method of administration of opium consumption were not available.

    All patients underwent standard left and right CAG via the femoral approach. The angiograms were assessed, and coronary flow quantification was performed using the corrected thrombolysis in myocardial infarction (TIMI) frame count (TFC) method described by Gibson et al.11 For TIMI frame counting, the first frame was defined as the frame in which dye filled the entrance of the artery with antegrade flow, and the last frame was identified when the dye reached the distal landmark branch. Normal TFC values were established as 36.2±2.6 (range: 32–41) for the left anterior descending (LAD), 22.2±4.1 (range: 16–31) for the left circumflex (LCx) and 20.4±3.0 (range: 16–26) for the right coronary artery (RCA).11 28 The coronary slow flow was assessed using the TIMI flow and the corrected TIMI frame count. The frame counts in the LAD were adjusted by a factor of 1.7 to account for its increased length. A frame count exceeding 27 indicated slow coronary flow (SCF), based on criteria from Gibson’s study. The results of the CAG, along with baseline information and clinical features, were obtained from the patient’s medical records using a prepared checklist. An experienced interventional cardiologist, blinded to the study population’s clinical details, performed the assessment.11 28

    Patients and public involvement

    None.

    Statistical analysis

    We presented the data using means and SDs for continuous variables. We assessed the normality of these variables by visually inspecting histograms and examining descriptive statistics. We used independent sample t-tests to analyse the differences between the two groups. Categorical variables were summarised using frequencies and percentages; and analysed using the χ2 test to identify any significant associations between groups.

    We used 1:4 propensity score matching (PSM) as well as stabilised inverse probability weighting (IPW) to balance potential confounding factors between the groups. PSM helped balance baseline characteristics such as age, gender and cigarette smoking by matching each CSFP case with four controls, minimising confounding effects by ensuring comparable groups (figure 2). IPW involved calculating propensity scores based on participants’ likelihood of opium use and then assigning weights to each patient. We analysed the PSM data using conditional logistic regression. Furthermore, the relationship between opium and CSFP was assessed using a logistic regression model considering IPWs in the calculation of the SE of the effect. The effects were reported via ORs with the corresponding 95% CIs. All statistical analyses were conducted using STATA statistical software, release V.14.2 (College Station, TX: StataCorp).

    Figure 2
    Figure 2

    Propensity scores matching coverage plot based on age, gender and cigarette smoking. CSFP, coronary slow flow phenomenon.

    Results

    We analysed a cohort of 21 835 patients who had normal epicardial coronary angiograms and identified 915 cases suspected of having the CSFP. After applying exclusion criteria, we confirmed that 767 of these cases were indeed CSFP, with 69 (9.0%) of them reporting current opium use. The process of patient selection is detailed in figure 1. To correct for the imbalance between the CSFP cases and the control group, we used PSM at a 4:1 ratio, resulting in 3068 matched controls. To explore the relationship between opium and CSFP, we conducted a three-stage analysis:

    1. We performed univariate logistic regression to estimate the unadjusted effect of opium use on the entire cohort.

    2. We examined the association between opium use and CSFP among the 767 confirmed CSFP cases and their 3068 matched controls using conditional logistic regression.

    3. We applied IPW to estimate the adjusted effect of opium use on CSFP, accounting for potential confounding variables. The average age of patients with CSFP was 51.59±11.26 years, closely matching that of the control group (51.57±11.25 years, p=0.91). The composition of males was 64.6% in the CSFP group and 63.4% in the control group (p=0.98). No significant differences were observed in risk factors such as HTN, DM, smoking, family history of cardiovascular disease or DLP between the groups. The prevalence of opium use was similar between the CSFP patients and controls (8.9% vs 9.5%, p=0.96). Detailed demographic and baseline characteristics are presented in table 1. The LAD artery was the most commonly affected vessel in both opium users and non-users (97.6% vs 97.9%, p>0.999). The involvement of the LCx and RCA was more frequent among opium users, though these differences were not statistically significant (p=0.052 and p=0.060, respectively). A higher proportion of opium users showed involvement in all three vessels (33.3% vs 18.9%, p=0.03). The CAG results for CSFP patients are detailed in table 2.

    View this table:
    Table 1

    Baseline demographics and characteristics of the participants

    View this table:
    Table 2

    Angiographic features of the coronary arteries in patients with CSFP

    Coronary artery blood flow was assessed using TIMI flow and TIMI frame count metrics (table 2). The TIMI frame count showed no significant differences between the CSFP and control groups across all three major coronary vessels (LAD, LCx, RCA, p=0.23, 0.79, 0.47, respectively). Similarly, the two groups observed no significant differences in TIMI flow rates (p=0.12, 0.91, 0.27, respectively). Our analysis initially suggested a potential association between current opium use and increased risk of CSFP (OR: 1.74, 95% CI: 1.31 to 2.35, p=0.001). However, after adjustments using PSM (OR: 1.06, 95% CI: 0.76 to 1.49, p=0.70) and IPW (OR: 1.15, 95% CI: 0.70 to 1.90, p=0.55), this association was not statistically significant (table 3).

    View this table:
    Table 3

    The relationship between opium consumption and CSFP

    Discussion

    The association between opium and CSFP is not clearly understood. Our study explored the prevalence and characteristics of CSFP among opium users compared with non-users, contributing to the ongoing discourse on the cardiovascular effects of opium. The demographic analysis revealed no significant differences in age or gender between CSFP patients and controls, suggesting that these factors do not markedly influence the occurrence of CSFP in the context of opium use.

    Both groups predominantly affected the LAD artery, consistent with its common involvement in CAD. In contrast, the LCx and RCA arteries showed a trend towards more frequent involvement among opium users. However, these differences did not reach statistical significance (p=0.052 and 0.06, respectively). While these findings may be attributed to the limited sample size of opium users (n=69), they could also reflect the complex interplay of factors contributing to the CSFP. The multifactorial nature of CSFP, involving endothelial dysfunction, inflammation and vascular remodelling, may obscure the detection of significant differences in coronary artery involvement between opium users and non-users.

    Objective measures of coronary blood flow, such as TIMI flow and frame count, showed no significant differences between groups, indicating that while opium use is associated with a higher prevalence of CSFP, it does not alter coronary blood flow dynamics detectable by these metrics.

    The initial analysis indicated a potential link between opium use and CSFP, but this was not supported after adjusting for confounders, highlighting the importance of considering underlying factors that may influence observed relationships in epidemiological research. The lack of a significant association after adjustments suggests that other unmeasured factors related to opium use, rather than a direct causative effect, may explain the initial findings.

    Previous studies have shown that using opium has significantly contributed to the development of CAD.29–32 A cross-sectional study conducted by Esmaeili Nadimi et al examined the relationship between opium consumption and microvascular coronary dysfunction, particularly CSFP. The study included 250 patients and found that there was a significant relationship between using opium and CSFP (OR: 3.57, p=0.006).25 In another study by Moazenzadeh et al, 46 patients with normal CAG results and documented CSFP were compared with 92 control individuals. After matching for age and gender, opium was found to be an independent predictor for CSFP (OR: 3.807, p=0.006).33 However, cigarette smoking is also a critical cofounder that needs to be noticed in this study.

    On the other hand, a case–control study conducted by Javadi et al compared 44 males with documented CSFP with 134 control groups and found no significant relationship between opium consumption and CSFP (p=0.107).34 A recent study performed by Rashidinejad et al revealed that there was no significant difference between individuals with SCF and normal cases in terms of opium addiction (p=0.431).35

    The exact mechanism by which opium causes CSFP is not yet fully understood. However, some potential explanations include opium causing vasospasm, reducing coronary blood flow and reducing coronary flow reserve. Opium use can lead to vasospasm in various ways, such as by decreasing the production of nitric oxide, increasing the production of reactive oxygen species (ROS) and activating smooth muscle cells, leading to the release of endothelin-1 and finally resulting in vasospasm. A reduced coronary flow reserve can also lead to CSFP. Although these effects may increase the risk for CAD and CSFP, our results showed that opium consumption is not an independent predictor for CSFP in the presence of confounding factors.

    The relationship between opium and CSFP is complex and influenced by various confounding factors. Demographic variables such as age, gender and socioeconomic status can skew results if not controlled. Lifestyle factors, mainly smoking, along with comorbid conditions such as HTN and diabetes, also impact coronary health. Psychosocial elements such as mental health and stress further complicate this relationship. Statistical methods such as PSM and IPW help mitigate these confounding effects. While initial findings suggest a potential link between opium use and CSFP, adjustments often show no significant association, highlighting the need for comprehensive analyses in epidemiological research.

    The findings of this study have important implications for clinical practice and public health. While opium use did not show a direct causative relationship with CSF after adjustment for confounders, the higher prevalence of multivessel involvement among users indicates a need for targeted cardiovascular risk assessment and management in this population. Healthcare providers should be aware of the potential cardiovascular risks associated with opium use and consider comprehensive cardiovascular evaluation and intervention strategies.

    Our study faced limitations which must be acknowledged. The retrospective nature of the study, the self-reporting mechanism and the social stigma of opium consumption might result in a misreporting and inaccurate representation of the true prevalence of opium use.

    In addition to that, opium consumption was defined as actively consuming opium, regardless of the dosage and the duration. We were unable to provide the purity of opium, the duration of substance use and the route of administration in our population. These factors may influence the outcome and should be considered in the analysis and study design.

    We recommend future studies with a multicenter design, considering different factors of opium use (purity, duration and administration) to uncover this relation more thoroughly.

    Conclusion

    While a direct link between opium and CSFP was not confirmed after adjustments, the patterns of coronary involvement in opium users suggest a need for further investigation and management. The study’s retrospective design and reliance on self-reported data introduce biases, and the lack of detailed opium consumption information limits understanding. Future research should use longitudinal designs to explore the mechanisms of opium’s effects on coronary and cardiovascular health.

    Data availability statement

    Data are available on reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and has been approved by the Research Ethics Committees of the Tehran Heart Center (IR.TUMS.THC.REC.1399.033). Participants gave informed consent to participate in the study before taking part.

    Acknowledgments

    The authors would like to extend their sincere gratitude to the hospital staff who contributed to the collection and management of the data used in this study. Additionally, we would like to acknowledge the use of Grammarly, a writing tool that significantly enhanced the clarity, structure and overall quality of our manuscript by identifying and correcting errors in spelling, grammar and syntax.

    References

    1. ↵
      1. Hackam DG,
      2. Anand SS
      . Emerging risk factors for atherosclerotic vascular disease: a critical review of the evidence. JAMA 2003;290:932–40. doi:10.1001/jama.290.7.932
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. Khan MA,
      2. Hashim MJ,
      3. Mustafa H, et al
      . Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study. Cureus 2020;12:e9349. doi:10.7759/cureus.9349
    3. ↵
      Novel risk factors for atherosclerosis. Mayo Clinic Proceedings. Elsevier. 2000.
    4. ↵
      1. Verhamme P,
      2. Quarck R,
      3. Hao H, et al
      . Dietary cholesterol withdrawal reduces vascular inflammation and induces coronary plaque stabilization in miniature pigs. Cardiovasc Res 2002;56:135–44. doi:10.1016/s0008-6363(02)00515-1
      OpenUrlCrossRefPubMed
    5. ↵
      1. Wang X,
      2. Nie S-P
      . The coronary slow flow phenomenon: characteristics, mechanisms and implications. Cardiovasc Diagn Ther 2011;1:37–43. doi:10.3978/j.issn.2223-3652.2011.10.01
      OpenUrlPubMed
    6. ↵
      1. Tambe AA,
      2. Demany MA,
      3. Zimmerman HA, et al
      . Angina pectoris and slow flow velocity of dye in coronary arteries--a new angiographic finding. Am Heart J 1972;84:66–71. doi:10.1016/0002-8703(72)90307-9
      OpenUrlCrossRefPubMedWeb of Science
    7. ↵
      1. Goel PK,
      2. Gupta SK,
      3. Agarwal A, et al
      . Slow coronary flow: a distinct angiographic subgroup in syndrome X. Angiol Open Access 2001;52:507–14. doi:10.1177/000331970105200801
      OpenUrl
    8. ↵
      1. Mangieri E,
      2. Macchiarelli G,
      3. Ciavolella M, et al
      . Slow coronary flow: clinical and histopathological features in patients with otherwise normal epicardial coronary arteries. Cathet Cardiovasc Diagn 1996;37:375–81. doi:10.1002/(SICI)1097-0304(199604)37:4<375::AID-CCD7>3.0.CO;2-8
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      1. Li J-J,
      2. Xu B,
      3. Li Z-C, et al
      . Is slow coronary flow associated with inflammation? Med Hypotheses 2006;66:504–8. doi:10.1016/j.mehy.2005.09.028
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      1. Beltrame JF
      . Defining the coronary slow flow phenomenon. Circ J 2012;76:818–20. doi:10.1253/circj.cj-12-0205
      OpenUrlCrossRefPubMed
    11. ↵
      1. Gibson CM,
      2. Cannon CP,
      3. Daley WL, et al
      . TIMI frame count: a quantitative method of assessing coronary artery flow. Circulation 1996;93:879–88. doi:10.1161/01.cir.93.5.879
      OpenUrlAbstract/FREE Full Text
    12. ↵
      1. Braunwald E,
      2. Zipes D,
      3. Libby P
      . Heart disease, 6th edn. Philladelphia WB Saunders Company, 2001:1180–2.
    13. ↵
      1. Fruchart J-C,
      2. Nierman MC,
      3. Stroes ESG, et al
      . New risk factors for atherosclerosis and patient risk assessment. Circulation 2004;109:III15–9. doi:10.1161/01.CIR.0000131513.33892.5b
      OpenUrlPubMed
    14. ↵
      1. Khot UN,
      2. Khot MB,
      3. Bajzer CT, et al
      . Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 2003;290:898–904. doi:10.1001/jama.290.7.898
      OpenUrlCrossRefPubMedWeb of Science
    15. ↵
      1. Frick S,
      2. Kramell R,
      3. Kutchan TM
      . Metabolic engineering with a morphine biosynthetic P450 in opium poppy surpasses breeding. Metab Eng 2007;9:169–76. doi:10.1016/j.ymben.2006.10.004
      OpenUrlCrossRefPubMed
    16. ↵
      1. Karam GA,
      2. Reisi M,
      3. Kaseb AA, et al
      . Effects of opium addiction on some serum factors in addicts with non-insulin-dependent diabetes mellitus. Addict Biol 2004;9:53–8. doi:10.1080/13556210410001674095
      OpenUrlCrossRefPubMed
    17. ↵
      1. Núñez BD,
      2. Miao L,
      3. Klein MA, et al
      . Acute and chronic cocaine exposure can produce myocardial ischemia and infarction in Yucatan swine. J Cardiovasc Pharmacol 1997;29:145–55. doi:10.1097/00005344-199702000-00001
      OpenUrlCrossRefPubMedWeb of Science
    18. ↵
      1. Frssdorf J,
      2. Weber NC,
      3. Obal D, et al
      . Morphine Induces Late Cardioprotection in Rat Hearts In Vivo: The Involvement of Opioid Receptors and Nuclear Transcription Factor??B. Anesthesia & Analgesia 2005;101:934–41. doi:10.1213/01.ane.0000172130.70274.84
      OpenUrlCrossRefPubMedWeb of Science
    19. ↵
      1. Weihrauch D,
      2. Krolikowski JG,
      3. Bienengraeber M, et al
      . Morphine Enhances Isoflurane-Induced Postconditioning Against Myocardial Infarction: The Role of Phosphatidylinositol-3-Kinase and Opioid Receptors in Rabbits. Anesthesia & Analgesia 2005;101:942–9. doi:10.1213/01.ane.0000171931.08371.a2
      OpenUrlCrossRefPubMedWeb of Science
    20. ↵
      1. Franchi S,
      2. Moschetti G,
      3. Amodeo G, et al
      . Do All Opioid Drugs Share the Same Immunomodulatory Properties? A Review From Animal and Human Studies. Front Immunol 2019;10:2914. doi:10.3389/fimmu.2019.02914
    21. ↵
      1. Khademi H,
      2. Malekzadeh R,
      3. Pourshams A, et al
      . Opium use and mortality in Golestan Cohort Study: prospective cohort study of 50,000 adults in Iran. BMJ 2012;344:e2502. doi:10.1136/bmj.e2502
    22. ↵
      1. Ayatollahi-Mousavi SA,
      2. Asadikaram G,
      3. Nakhaee N, et al
      . The Effects of Opium Addiction on the Immune System Function in Patients with Fungal Infection. Addict Health 2016;8:218–26.
      OpenUrl
    23. ↵
      1. Roayaei P,
      2. Aminorroaya A,
      3. Vasheghani-Farahani A, et al
      . Opium and cardiovascular health: A devil or an angel? Indian Heart J Teach Ser 2020;72:482–90. doi:10.1016/j.ihj.2020.10.003
      OpenUrl
    24. ↵
      1. Khalili P,
      2. Ayoobi F,
      3. Mohamadi M, et al
      . Effect of opium consumption on cardiovascular diseases - a cross- sectional study based on data of Rafsanjan cohort study. BMC Cardiovasc Disord 2021;21:2. doi:10.1186/s12872-020-01788-4
      OpenUrl
    25. ↵
      1. Esmaeili Nadimi A,
      2. Pour Amiri F,
      3. Sheikh Fathollahi M, et al
      . Opium addiction as an independent risk factor for coronary microvascular dysfunction: A case–control study of 250 consecutive patients with slow–flow angina. Int J Cardiol 2016;219:301–7. doi:10.1016/j.ijcard.2016.06.034
      OpenUrl
    26. ↵
      1. Nakhaee S,
      2. Amirabadizadeh A,
      3. Qorbani M, et al
      . Opium use and cardiovascular diseases: a systematic review and meta-analysis. Crit Rev Toxicol 2020;50:201–12. doi:10.1080/10408444.2020.1740972
      OpenUrl
    27. ↵
      1. Masoudkabir F,
      2. Shafiee A,
      3. Heidari A, et al
      . Epidemiology of substance and opium use among adult residents of Tehran; a comprehensive report from Tehran cohort study (TeCS). BMC Psychiatry 2024;24:132. doi:10.1186/s12888-024-05561-1
    28. ↵
      1. Sanati H,
      2. Kiani R,
      3. Shakerian F, et al
      . Coronary Slow Flow Phenomenon Clinical Findings and Predictors. Res Cardiovasc Med 2016;5:e30296. doi:10.5812/cardiovascmed.30296
    29. ↵
      1. Masoumi M,
      2. Shahesmaeili A,
      3. Mirzazadeh A, et al
      . Opium addiction and severity of coronary artery disease: a case-control study. J Res Med Sci 2010;15:27–32.
      OpenUrlPubMed
    30. ↵
      1. Masoudkabir F,
      2. Sarrafzadegan N,
      3. Eisenberg MJ
      . Effects of opium consumption on cardiometabolic diseases. Nat Rev Cardiol 2013;10:733–40. doi:10.1038/nrcardio.2013.159
      OpenUrlCrossRef
    31. ↵
      1. Hosseini SK,
      2. Masoudkabir F,
      3. Vasheghani-Farahani A, et al
      . Opium consumption and coronary atherosclerosis in diabetic patients: a propensity score-matched study. Planta Med 2011;77:1870–5. doi:10.1055/s-0031-1280017
      OpenUrlPubMedWeb of Science
    32. ↵
      1. Rahimi Darabad B,
      2. Vatandust J,
      3. Pourmousavi Khoshknab MM, et al
      . Survey of the Effect of Opioid Abuse on the Extent of Coronary Artery Diseases. GJHS 2014;6:83. doi:10.5539/gjhs.v6n7p83
    33. ↵
      1. Moazenzadeh MM,
      2. Azimzadeh BS,
      3. Zare J, et al
      . Clinical Features and Main Determinants of Coronary Slow Flow Phenomenon in Iranian Patients. Eur J Cardiovasc Med 2010;1.
    34. ↵
      1. Javadi HR,
      2. Mirakbari SM,
      3. Allami A, et al
      . Factors Determining Primary Coronary Slow Flow Phenomenon among Opium Users and Non-users: A Case Control Study in Northern Iran. Addict Health 2022;14:224–8. doi:10.34172/ahj.2022.1211
      OpenUrl
    35. ↵
      1. Rashidinejad H,
      2. Mohseni M,
      3. Safizadeh H, et al
      . Slow Coronary Flow and the Serum Vitamin D Level. Iran Heart J 2019;20:71–8.
      OpenUrl
    View Abstract

    Footnotes

    • Contributors LA conducted the investigation, SN wrote the original draft and edited and revised the manuscript, FM conceptualised, supervised and revised the manuscript, AJ performed the formal analysis and revised the manuscript, MP provided the resources and data curation, AV-F, KH and HP reviewed and edited the manuscript. FM is the guarantor. We would like to acknowledge the use of Grammarly to enhanced the clarity, structure and overall quality of our manuscript by identifying and correcting errors in spelling, grammar and syntax.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • 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.

    • Provenance and peer review Not commissioned; externally peer reviewed.