Skip to main content

Advertisement

SpringerLink
Log in

Impact of lifetime marijuana use on fasting plasma insulin levels and HOMA-IR score in obese adults with and without insulin resistance

  • Original Article
  • Published:
Acta Diabetologica Aims and scope Submit manuscript

Abstract

Aims

To explore the association of marijuana use with mean plasma fasting insulin levels and homeostasis model assessment of insulin resistance (HOMA-IR) score in obese adults with different HOMA-IR.

Methods

The National Health and Nutrition Examination Survey (NHANES) is a survey research program designed to assess the health and nutrition status of individuals in the United States and to track changes over time. We abstracted data from NHANES 2009–2016. We estimated the minimal lifetime marijuana use (MLU) using the duration of regular exposure and the frequency of use. We assessed the association of MLU and both plasma fasting insulin and HOMA-IR score using log-linear regression models.

Results

A total of 65,209 obese individuals aged 18 to 59 years were included. In obese individuals who never used marijuana (reference), the mean value (± standard deviation) was 19.0 (± 12.8) μU/mL for plasma fasting insulin and 4.78 (± 3.49) for HOMA-IR. In individuals with HOMA-IR < 2.13 or ≥ 5.72, we found no association of marijuana use with HOMA-IR. In those with HOMA-IR < 5.72, the highest tertile of MLU (i.e., ≥ 1799 times) was associated with 12% decrease (95% confidence intervals, 4–19%) in the fasting insulin and 10% decrease in HOMA-IR (95% CI 1–19%), as compared with their counterparts who never used marijuana. In those with HOMA-IR ≥ 2.13, we found a marked impact of marijuana use only in adults who used marijuana ≥ 1799 times, with 13% decrease (95% CI 5–19%) in fasting insulin and 10% decrease (95% CI 3–18%) in HOMA-IR score.

Conclusions

Marijuana use is associated with reduced fasting insulin levels and HOMA-IR score in US obese adults with HOMA-IR ≥ 2.13, but not in those with HOMA-IR < 2.13 or ≥ 5.72. The impact of marijuana use is the greatest after long-term exposure and is independent of BMI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF (2010) Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr 8:29

    Article  Google Scholar 

  2. American Diabetes Association (2013) Economic costs of diabetes in the U.S. in 2012. Diabetes Care 36:1033–1046

    Article  Google Scholar 

  3. Shrestha SS, Zhang P, Hora IA, Gregg EW (2018) Trajectory of excess medical expenditures 10 years before and after diabetes diagnosis among U.S. Adults Aged 25–64 Years, 2001–2013. https://doi.org/10.2337/dc17-2683. Diabetes Care

    Article  Google Scholar 

  4. Johnston LD, O’Malley PM, Bachman JG (2003) Monitoring the future national survey results on drug use, 1975–2002. National Institute on Drug Abuse, Bethesda

    Google Scholar 

  5. Han B, Compton WM, Blanco C, Jones CM (2018) Trends in and correlates of medical marijuana use among adults in the United States. Drug Alcohol Depend 186:120–129

    Article  Google Scholar 

  6. Rotermann M, Macdonald R (2018) Analysis of trends in the prevalence of cannabis use in Canada, 1985 to 2015. Health Rep 29:10–20

    PubMed  Google Scholar 

  7. Alshaarawy O, Anthony JC (2015) Cannabis smoking and diabetes mellitus results from meta-analysis with eight independent replication samples. Epidemiology 26:597–600

    Article  Google Scholar 

  8. Rajavashisth TB, Shaheen M, Norris KC et al (2012) Decreased prevalence of diabetes in marijuana users: cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) III. BMJ Open 2:e000494. https://doi.org/10.1136/bmjopen-002011-000494

    Article  PubMed  PubMed Central  Google Scholar 

  9. Imtiaz S, Rehm J (2018) The relationship between cannabis use and diabetes: results from the national epidemiologic survey on alcohol and related conditions III. Drug Alcohol Rev 37:897–902

    Article  Google Scholar 

  10. Danielsson AK, Lundin A, Yaregal A, Östenson CG, Allebeck P, Agardh EE (2016) Cannabis use as risk or protection for type 2 diabetes: a longitudinal study of 18,000 swedish men and women. J Diabetes Res 2016:6278709

    Article  CAS  Google Scholar 

  11. Bancks MP, Pletcher MJ, Kertesz SG, Sidney S, Rana JS, Schreiner PJ (2015) Marijuana use and risk of prediabetes and diabetes by middle adulthood: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Diabetologia 58:2736–2744

    Article  CAS  Google Scholar 

  12. Ngueta G, Bélanger RE, Laouan-Sidi EA, Lucas M (2015) Cannabis use in relation to obesity and insulin resistance in the Inuit population. Obesity 23:290–295

    Article  CAS  Google Scholar 

  13. Le Strat Y, Le Foll B (2011) Obesity and cannabis use: results from 2 representative national surveys. Am J Epidemiol 174:929–933

    Article  Google Scholar 

  14. Hayatbakhsh MR, O’Callaghan MJ, Mamun AA, Williams GM, Clavarino A, Najman JM (2010) Cannabis use and obesity and young adults. Am J Drug Alcohol Abuse 36:350–356

    Article  Google Scholar 

  15. Penner EA, Buettner H, Mittleman MA (2013) The impact of marijuana use on glucose, insulin, and insulin resistance among US adults. Am J Med 126:583–589

    Article  CAS  Google Scholar 

  16. Gallant M, Odei-Addo F, Frost CL, Levendal RA (2009) Biological effects of THC and a lipophilic cannabis extract on normal and insulin resistant 3T3-L1 adipocytes. Phytomedicine 16:942

    Article  CAS  Google Scholar 

  17. Frisher MS, White S, Varbiro G et al (2010) The role of cannabis and cannabinoids in diabetes. Br J Diabetes Vasc Dis 10:267–273

    Article  CAS  Google Scholar 

  18. Makki K, Froguel P, Wolowczuk I (2013) Adipose tissue in obesity-related inflammation and insulin resistance: cells, cytokines, and chemokines. ISRN Inflamm. https://doi.org/10.1155/2013/139239

    Article  PubMed  PubMed Central  Google Scholar 

  19. Blüher M (2016) Adipose tissue inflammation: a cause or consequence of obesity-related insulin resistance? Clin Sci 130:1603–1614

    Article  Google Scholar 

  20. Ota T (2014) Obesity-induced inflammation and insulin resistance. Front Endocrinol 5:204. https://doi.org/10.3389/fendo.2014.00204

    Article  Google Scholar 

  21. Juan-Picó P, Fuentes E, Bermúdez-Silva FJ et al (2006) Cannabinoid receptors regulate Ca(2 +) signals and insulin secretion in pancreatic beta-cell. Cell Calcium 39:155–162

    Article  Google Scholar 

  22. Li C, Bowe JE, Huang GC, Amiel SA, Jones PM, Persaud SJ (2011) Cannabinoid receptor agonists and antagonists stimulate insulin secretion from isolated human islets of Langerhans. Diabetes Obes Metab 13:903–910

    Article  CAS  Google Scholar 

  23. Kim MK, Reaven GM, Chen YD, Kim E, Kim SH (2015) Hyperinsulinemia in individuals with obesity: role of insulin clearance. Obesity 23:2430–2434

    Article  CAS  Google Scholar 

  24. Kim MK, Reaven GM, Kim SH (2017) Dissecting the relationship between obesity and hyperinsulinemia: role of insulin secretion and insulin clearance. Obesity 25:378–383

    Article  CAS  Google Scholar 

  25. U.S.CDC (2017) Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey. Hyattsville, MD: U.S.: Department of Health and Human Services, Centers for Disease Control and Prevention; Available on http://www.cdc.gov/nchs/nhanes.htm Accessed 8 Jan 2019

  26. Thomas GN, Critchley JA, Tomlinson B, Anderson PJ, Lee ZS, Chan JC (2000) Obesity, independent of insulin resistance, is a major determinant of blood pressure in normoglycemic Hong Kong Chinese. Metabolism 49:1523–1528

    Article  CAS  Google Scholar 

  27. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419

    Article  CAS  Google Scholar 

  28. Hanley AJ, Williams K, Stern MP, Haffner SM (2002) Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease: the San Antonio Heart Study. Diabetes Care 25:1177–1184

    Article  Google Scholar 

  29. U.S.CDC (2018) Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey. Hyattsville, MD: U.S.: Department of Health and Human Services, Centers for Disease Control and Prevention; Available on https://www.cdc.gov/nchs/data/nhanes/nhanes_09_10/labcomp_f.pdf Accessed 8 Jan 2019

  30. U.S.CDC (2018) Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey. Hyattsville, MD: U.S.: Department of Health and Human Services, Centers for Disease Control and Prevention. Serum, Plasma and Urine specimens. Available on https://www.cdc.gov/nchs/nhanes/biospecimens/serum_plasma_urine.htm Accessed 8 Jan 2019

  31. Allison PD (2000) Multiple imputation for missing data: a cautionary tale. Sociol Methods Res 28:301–309

    Article  Google Scholar 

  32. Carpenter JR, Kenward MG (2008) MAR methods for quantitative data. In: Missing data in randomised controlled trials— a practical guide. Birmingham: National Institute for Health Research

  33. Textor J, Hardt J, Knüppel S (2011) DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology 5:745

    Article  Google Scholar 

  34. Textor J, Liskiewicz M (2011) Adjustment criteria in causal diagrams: an algorithmic perspective.In: Proceedings of the 27th conference on uncertainty in artificial intelligence (UAI 2011) 14-17 July 2011. Barcelona, Spain, UAI press681-688. Available: http://arxiv.org/ftp/arxiv/papers/1202/1202.3764.pdf Accessed 8 Jan 2019

  35. Textor J, van der Zander B, Gilthorpe MK, Liskiewicz G, Ellison TH (2016) Robust Causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol 45:1887–1894

    PubMed  Google Scholar 

  36. Glymour M, Greenland S (2008) Causal diagram. In: Rothman K, Greenland S, Lash T (eds) Modern epidemiology, 3rd edn. Lippincott Williams & Wilkins, Philadelphia, pp 184–209

    Google Scholar 

  37. Kandula NR, Diez-Roux AV, Chan C et al (2008) Association of acculturation levels and prevalence of diabetes in the multi-ethnic study of atherosclerosis (MESA). Diabetes Care 31:1621–1628

    Article  Google Scholar 

  38. Li C, Bwe JE, Huang GC, Amiel SA, Jones PM, Persaud SJ (2011) Cannabinoid receptor agonists and antagonists stimulate insulin secretion from isolated human islets of Langerhans. Diabetes Obes Metab 13:903–910

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to all the study participants and acknowledge the contributions of all their collaborators for their valuable help.

Funding

Dr. Ngueta received a postdoctoral fellowship (No. 331786) from Canadian Institutes of Health Research.

Author information

Authors and Affiliations

  1. National Public Health Institute of Quebec, 945 Avenue Wolfe, Quebec, QC, G1V 5B3, Canada

    Gerard Ngueta

  2. Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Quebec, QC, Canada

    Gerard Ngueta

  3. Faculté de Pharmacie, Université Laval, Quebec, QC, Canada

    Gerard Ngueta

Authors
  1. Gerard Ngueta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GN takes responsibility for the accuracy of data analysis. He conceptualized and designed the study and analyzed and interpreted the results. He also wrote the manuscript.

Corresponding author

Correspondence to Gerard Ngueta.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Ethical approval

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

Informed consent

Informed consent was obtained from all patients for being included in the current study.

Additional information

Managed by Massimo Federici.

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 390 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ngueta, G. Impact of lifetime marijuana use on fasting plasma insulin levels and HOMA-IR score in obese adults with and without insulin resistance. Acta Diabetol 57, 133–140 (2020). https://doi.org/10.1007/s00592-019-01390-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00592-019-01390-x

Keywords

Search

Navigation