Correlation between NARX Score and Food Addictive Behavioral Patterns in Chronic Pain Patients

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Leon Margolin Daniel Margolin Jeremy Luchins Michelle Margolin Sanford Lefkowitz


Setting and Objective.  With still rising drug overdose deaths already at unprecedented alarming levels, reliable indicators of addiction and addiction-vulnerability are urgently needed.  In the U.S., NARX scores are widely accepted as providing objective measures / predictors of drug-addiction risk.  NARX scores are deemed especially useful when informing a broad patient-profile.  Such profiles may to good advantage be enriched by patients’ answers to standard health questionnaires dealing with drug usage, but the advantage is blunted by questionable candor of patients’ answers.  Use of questionnaires – and, thereby, NARX scores – would be enhanced by questions eliciting more honest answers. 

Design and Participants.  Our research explores the utility of questions relating to food-addictive behaviors as proxies for and/or adjuncts to standard questionnaires.  Our questions’ respondents were 100+ chronic pain patients with well-developed patient-profiles, including up-to-date NARX scores.  The patients responded to the same areas of inquiry found on standard questionnaires directly probing patients’ drug exposure / use / abuse / addiction, but with food categories as selection-choices: Questions regarding what a patient would intake for improvement of mood; in the absence of which, the patient experiences withdrawal; intake of which, diminishes participation in normal activities; etc., were followed by selection-choices of such foods as ‘Chocolate’ and ‘Meat’ in place of selection-choices of drugs – with a total of eight questions, each presenting an identical set of four food selection-choices.  Our questionnaire elicited over 800 question-selection pairs (e.g., mood-Chocolate; mood-Meat; withdrawal-Chocolate; withdrawal-Meat).  Relationships between NARX scores and respondents’ choices were assessed by linear regression and t-distribution analyses. 

Results.  For particular question-selection pairings, the statistical analyses demonstrated strong correlations between risk factors reflected in NARX scores and food-addictive behavioral patterns.  Notably, Meat as the selection for those high-correlation questions was associated with the chronic pain patients with the highest NARX scores (i.e., at highest risk); Cheese, the lowest.  Other foods reported with high frequency were sodas and sweets, underscoring the role of sugar in chronic pain syndromes. 

Conclusions.  Questionnaires probing selected food-addictive behaviors, with higher expectation than drug-related questions of eliciting honest answers, may serve to complement patient-profiles with regard to addiction-vulnerability and, thereby, enhance the use of NARX scores in confronting current rising tides of drug addiction, such as those currently manifested in the growing opioid epidemic.  We note the utility of such food-centric questionnaires in building addiction profiles in demographics that may not have informative NARX scores, such as recent immigrants.  We advocate further clinical studies exploring food-addictive behaviors as proxies for and/or adjuncts to drug-addictive behaviors.

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How to Cite
MARGOLIN, Leon et al. Correlation between NARX Score and Food Addictive Behavioral Patterns in Chronic Pain Patients. Medical Research Archives, [S.l.], v. 11, n. 4, may 2023. ISSN 2375-1924. Available at: <>. Date accessed: 19 may 2024. doi:
Research Articles


1. Huizenga J.E., Breneman B.C., Patel V.R., et al. (2016) NARxCHECK® Score as a Predictor of Unintentional Overdose Death. Appriss, Inc.

2. Cochran G., Brown J., Yu Z., et al. (2021) Validation and threshold identification of a prescription drug monitoring program clinical opioid risk metric with the WHO alcohol, smoking, and substance involvement screening test. Drug Alcohol Depend. 228: 109067. PMID: 34610516

3. Volkow N.D., Tomasi D., Wang G-J., et al. (2014) Stimulant-induced dopamine increases are markedly blunted in active cocaine abusers. Mol Psychiatry 19: 1037-1043. DOI: 10.1038/mp.2014.58. PMID: 24912491

4. Fernández-Espejo E. (2000) Cómo funciona el nucleus accumbens? [How does the nucleus accumbens function?]. Rev Neurol. 30(9):845-9. Spanish. PMID: 10870199

5. Kelley A.E. (2004) Ventral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning. Neurosci Biobehav Rev. 27(8):765-76. PMID: 15019426.

6. Baik J-H. (2013) Dopamine signaling in food addiction: role of dopamine D2 receptors. BMB Rep. 46: 519-526. DOI: 10.5483/BMBRep.2013.46.11.207 PMID: 24238362

7. Choi K.W., Somers T.J., Babyak M.A., et al. (2014) The relationship between pain and eating among overweight and obese individuals with osteoarthritis: An ecological momentary study. Pain Res Manag 19: e159-e163. DOI: 10.1155/2014/598382

8. Mishra A., Singh S., Shukla S. (2018) Physiological and Functional Basis of Dopamine Receptors and Their Role in Neurogenesis: Possible Implication for Parkinson's disease. J Exp Neurosci. 12:1179069518779829. DOI: 10.1177/1179069518779829. PMID: 29899667

9. Volkow N.D., Wang G-J., Fowler J.S., et al. (1999) Prediction of reinforcing responses to psychostimulants in humans by brain dopamine D2 receptor levels. Am J Psychiatry 156: 1440-1443. DOI: 10.1176/ajp.156.9.1440

10. Arthritis Foundation (2019) Arthritis Diet.

11. Lee K.MC., Zhang Z., Achuthan, A., et al. (2020) IL-23 in arthritic and inflammatory pain development in mice. Arthritis Res Ther 22, 123. DOI: 10.1186/s13075-020-02212-0

12. Vedder D., Gerritsen M., Duvvuri B, et al. (2020) Neutrophil activation identifies patients with active polyarticular gout. Ibid., 148. DOI: 10.1186/s13075-020-02244-6

13. Liu Q., Hebert J.R., Shivappa N., et al. (2020) Inflammatory potential of diet and risk of incident knee osteoarthritis: a prospective cohort study. Ibid., 209. DOI: 10.1186/s13075-020-02302-z

14. Wu Y., He X., Huang N, et al. (2020) A20: a master regulator of arthritis. Ibid., 220. DOI: 10.1186/s13075-020-02281-1



17. State of Ohio Board of Pharmacy (2019) Ohio Automated RX Reporting System (



20. https://www.

21. (accessed Sep. 21, 2022)

22. Florence C., Luo F., Rice K. (2020) The economic burden of opioid use disorder and fatal opioid overdose in the United States, 2017. Drug and Alcohol Dependence 218:108350. DOI: 10.1016/j.drugalcdep.2020.108350 PMID: 33121867

23. Jennings K. (2022) Economic Toll Of Opioid Epidemic: $1.3 Trillion A Year Forbes Feb. 4, 2022


25. (accessed Sep. 21, 2022)




29. Boggs J. (2021) Ohio drug overdose deaths jumped 26% in a year. Spectrum News 1 Dec. 8, 2021 (

30. Nissen N. (2021) Opioid overdoses 29% higher in 2020 than before the pandemic: Study

31. Shuda, N. (2023) Five things to know about Franklin County's 2022 drug overdose death numbers. The Columbus Dispatch April 21, 2023

32. Kube C. and Boigon M. (2022) Every branch of the military is struggling to make its 2022 recruiting goals, officials say. NBC News June 27, 2022

33. Janke E.A., Kozak A. (2012) "The more pain I have, the more I want to eat": obesity in the context of chronic pain. Obesity (Silver Spring) 20: 2027-2034. DOI: 10.1038/oby.2012.39