---
title: "Nicotine metabolism rate and myocardial infarction risk in people with HIV who smoke"
id: "plos-one-12-rate-of-nicotine-metabolism-on-risk-of-myocardial-infarction-among-people-with"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-12-rate-of-nicotine-metabolism-on-risk-of-myocardial-infarction-among-people-with"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0356296"
published_at: "2026-08-18T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Nicotine metabolism rate and myocardial infarction risk in people with HIV who smoke
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-12-rate-of-nicotine-metabolism-on-risk-of-myocardial-infarction-among-people-with
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0356296)
- **Published At:** 2026-08-18T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- People with HIV (PWH) smoke at higher rates than the general population and cigarette use increases myocardial infarction (MI) risk in PWH. The study evaluated whether the rate of **nicotine metabolism**, measured by the nicotine metabolite ratio (**NMR**), is associated with incident MI in PWH who smoke. - The authors performed a matched nested case-control study within the CNICS cohort, using plasma samples and clinical data from seven CNICS sites. Cases were PWH who reported regular cigarette use and had an adjudicated MI with an available aviremic plasma sample; controls were selected by incidence density sampling and matched on site, age, race, birth sex, viral load status, and calendar time. - Nicotine metabolites (cotinine and 3-hydroxycotinine) were measured by LC-MS and used to calculate the NMR. Cotinine > 10 ng/mL was used to confirm cigarette use. NMR was analyzed on the log scale due to non-normal distribution. - The analytic sample included 135 MI cases and 252 matched smoking controls. Median NMR was higher in cases [0.51 (IQR 0.36–0.73)] than controls [0.47 (IQR 0.30–0.70)]. - Conditional logistic regression estimated an odds ratio (OR) of 1.4 for the association between high NMR and MI (95% CI 0.97–1.9), which did not reach statistical significance. Adjustment for statin use, hypertension, and diabetes produced a similar OR (1.4, 95% CI 0.92–2.0). - The authors conclude there is a small-magnitude, non-significant association between faster nicotine metabolism and MI in PWH. They propose **NMR** as a potential biomarker of cardiovascular risk in this population but state that further study is needed to precisely estimate the effect. - Data are derived from CNICS and are not publicly available without approval. The study used stored plasma samples and matched adjudicated MI outcomes from 2003–2020. The findings highlight the need for additional investigation into how nicotine clearance may influence cardiovascular risk among PWH who smoke.
## Clinical Analysis & Structured Key Points
Rate of nicotine metabolism on risk of myocardial infarction among people with HIV who smoke cigarettes | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Background The prevalence of cigarette use in people with HIV (PWH) is 2–3 times higher than the general population. Cigarette use increases risk of myocardial infarction (MI). Faster nicotine metabolism, quantified by the nicotine metabolite ratio (NMR) is associated with greater risk for nicotine dependence and lung cancer, but its association with MI is unknown. Methods We conducted a nested case-control study within the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Cases were PWH who reported cigarette use with incident adjudicated MI between 2003 and 2020 and available plasma samples; cigarette-smoking controls were selected by incidence density sampling, matched on age, race, birth sex, and plasma HIV RNA level. Conditional logistic regression was used to estimate odds ratios (OR) for the association of NMR and MI. Results We identified 135 cases with MI and 252 controls. Median (IQR) NMR was greater in cases [0.51 (0.36, 0.73)] than in controls [0.47 (0.30, 0.70)]. In conditional logistic regression, the odds of having a high NMR were 1.4 times greater among MI cases than controls, but not at a statistically significant level (OR: 1.4, 95% CI: 0.97–1.9). This estimate did not substantively change after further adjustment for statin use, hypertension, and diabetes (OR: 1.4, 95% CI: 0.92–2.0). Conclusions There is a small-magnitude association between NMR and MI, which was not statistically significant. NMR remains a potential biomarker for MI risk among PWH. Further investigation is needed to estimate the precise effect of NMR on MI in PWH. Citation: Ahmed D, Bilker WB, Han X, Tyndale RF, Merlin J, Kimmel SE, et al. (2026) Rate of nicotine metabolism on risk of myocardial infarction among people with HIV who smoke cigarettes. PLoS One 21(8): e0356296. https://doi.org/10.1371/journal.pone.0356296 Editor: Jorddy Neves Cruz, Universidade Federal do Para, BRAZIL Received: March 17, 2026; Accepted: July 31, 2026; Published: August 18, 2026 Copyright: © 2026 Ahmed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data used in this study are derived from CNICS and are subject to participant confidentiality protections and data-use agreements that prohibit public deposition of the dataset. Therefore, the data cannot be made publicly available. Datasets contain potentially identifiable patient information and public release could pose a risk of participant re-identification. However, de-identified data may be made available to qualified researchers upon reasonable request and subject to applicable institutional, ethical, and CNICS data access approvals. Information regarding available CNICS data elements can be found on the CNICS Data Elements webpage ( https://sites.uab.edu/cnics/cnics-data-elements/) . Investigators may submit data requests through the CNICS LEAF portal ( https://leaf.cnics.cirg.uw.edu/) . Additional information regarding CNICS resources, governance, and scientific impact is available at https://sites.uab.edu/cnics/cnics-impact/ . Access to individual-level CNICS data is subject to applicable ethical, regulatory, and data-use requirements and is not publicly available. Funding: This work was funded by R01-HL151292 and supported by K24 DA045244, core services and support from the Penn Center for AIDS Research (CFAR) (P30 AI 045008), Penn Mental Health AIDS Research Center (PMHARC) (P30 MH 097488) and through core services and support from the a Canada Research Chair in Pharmacogenomics, the Canadian Institutes of Health Research (FDN-154294), the Centre for Addiction and Mental Health at the University of Toronto and CNICS which is an NIH-funded program (R24 AI067039) made possible by the National Institute of Allergy and Infectious Diseases (NIAID). CNICS is funded by NIH grant R24 AI067039. The CFAR sites involved in CNICS include Univ of Alabama at Birmingham (P30 AI027767), Univ of Washington (P30 AI027757), Univ of California San Diego (P30 AI036214), Univ of California San Francisco (P30 AI027763), Case Western Reserve Univ (P30 AI036219), Johns Hopkins Univ (P30 AI094189, U01 DA036935), Fenway Health/Harvard (P30 AI060354), Univ of North Carolina Chapel Hill (P30 AI50410), Vanderbilt Univ (P30 AI110527)*, and Univ of Miami (P30 AI073961)*. Competing interests: WB is a consultant for Genentech for unrelated products. RG serves on DSMBs for Pfizer and Spyre Therapeutics for medications unrelated to smoking or HIV. All other authors reports no disclosures relevant to the manuscript. Introduction Cigarette use [ 1 ] and HIV [ 2 ] are well-established risk factors for myocardial infarction (MI). Among PWH, the prevalence of cigarette use is approximately twice that of the general population [ 3 ], and cigarette use doubles the risk of cardiovascular complications in PWH [ 4 ]. The reasons for increased cigarette use are not fully understood, but may relate to nicotine metabolism [ 5 ]. Nicotine dependence is related to the rate of nicotine metabolism [ 6 ]. Nicotine is primarily metabolized by CYP2A6 to cotinine (80%) and then to 3-hydroxycotinine (3-HC) [ 7 ]. Enzymatic activity of CYP2A6 is measured using the nicotine metabolite ratio (NMR), calculated as the cotinine concentration divided by 3-HC concentration [ 8 ]. The NMR reflects both genetic and environmental influences on CYP2A6 and thus nicotine clearance [ 8 ]. Measurement of NMR using plasma is reliable and valid, and stable over time [ 9 – 11 ]. A higher NMR indicates faster nicotine metabolism, which is associated with higher cigarette use, lower cessation rates, greater total puff volume and higher levels of carcinogens [ 12 – 14 ] Additionally, among PWH who use cigarettes, NMR increases after viral suppression, with doubling among those on efavirenz-based regimens [ 5 , 15 ] The faster nicotine clearance may therefore increase exposure to toxins in cigarette that exacerbate cardiovascular risk. If an association between faster nicotine metabolism and higher MI rates in PWH were identified, NMR could become a novel biomarker of cardiovascular risk and would further underscore the importance of smoking cessation in this population. We aimed to determine whether NMR is associated with MI in PWH. Materials and methods Study design We conducted a matched nested case-control study to assess the association between NMR and MI in people who use cigarettes with HIV. We used data from the Center for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) cohort. Initiated in January 1995, CNICS is a prospective clinical cohort of > 49,000 PWH receiving care at ten CFAR sites nationally [ 16 ]. Seven of the CNICS sites were included in these analyses: Case Western Reserve University, Johns Hopkins University, University of Alabama at Birmingham, University of California, San Diego, University of California, San Francisco, University of North Carolina at Chapel Hill, and University of Washington. CNICS captures clinical data and self-reported measures. Plasma samples in the CNICS have been stored at −80 C since its inception. Participants were selected from individuals with self-reported regular cigarette use, defined as smoking on at least two consecutive time points, a minimum of one year apart, as well as at all assessed intervening time points. Additionally, they had to have aviremic plasma sample available for NMR testing. Cases included all individuals who had an MI and had a plasma sample when they reported cigarette use. Controls were selected using incidence density sampling. Controls were matched to the cases based on site, age, race, sex, viral load status (at time of MI), and calendar time (within the same 365-day period) when cases had MI. MI has been adjudicated in the CNICS cohort [ 17 ]. Individuals diagnosed with MI, which could not be confirmed or with a reported cardiovascular disease equivalent, which had not been validated in CNICS, were excluded as cases or controls. Outcome and covariate measurement and definitions Standard liquid chromatography-tandem mass spectrometry (LC-MS) was used to measure both cotinine and 3-HC [ 10 ]. A cotinine value > 10 ng/mL confirmed cigarette use [ 18 ]. The NMR was treated as a continuous measure, and the log of NMR was reported because the NMR was not normally distributed. We examined demographic characteristics, including age, race, ethnicity, and birth sex, clinical characteristics including CD4 count, history of hypertension, diabetes, and statin medication use. Given the association between the duration and level of exposure to HIV viremia with adverse outcomes, we estimated the HIV viremia copy years for each participant at the time of MI or censored using the trapezoid method applied to all available viral loads (See Supplementary Information for the trapezoid method) [ 19 ]. To account for the effect of efavirenz’s on the NMR, we calculated the NMR as a weighted average which was computed by summing the NMR on efavirenz x time on efavirenz + NMR off efavirenz x time off efavirenz divided by total cigarette use duration, which we assumed to be the individual’s current age minus 16 [ 5 , 15 ]. Cigarette use was imputed for all participants to have begun at age 16 [ 20 ]. We considered the pre-ART NMR to be the same as the non-efavirenz containing regimen NMR given the relatively small increases in NMR in individuals initiating non-efavirenz containing regimens [ 5 ]. Statistical analysis and sample size Baseline characteristics were compared using chi-squared tests for categorical variables and t tests or rank sum tests for continuous variables. Normality was assessed using the Shapiro-Wilk and histograms. Conditional logistic regression was used to compare NMR between those with and without MI. Potential confounders were selected based on biological plausibility. We targeted a sample size of 145 cases with a goal of 2:1 control to cases for 80% power to detect a 0.1 value difference in NMR between cases and controls with a p-value of 0.05. Sensitivity analysis was conducted to assess the impact of excluding participants with missingness and test the assumptions used in the weighted NMR calculation. Ethics statement The study was reviewed and approved by the University of Pennsylvania and University of Toronto institutional review board. The required for written informed consent was waived by the Ethics Committee. All data and specimens were anonymized by CNICS and therefore, the authors had no access to identifiable personal. Data access for research purposes was obtained on August 01, 2019. Additional data were obtained afterward to ensure that the enrollment included individuals who had MIs in 2020. Results Characteristics of cases and controls We identified 135 participants (mean age 51; 76% male) with myocardial infarction (MI) occurring between 2003 and 2020. We identified 252 matched controls (mean age 50 years; 76% male) and were unable to find a suitable second matched control for 18 cases. Two controls samples were excluded due to missing NMR. There was little missingness in the data with diastolic and systolic pressure having <2% and total cholesterol with 22% missingness in cases and 23% in controls. Table 1 summarizes participants’ baseline demographic characteristics. Cases and controls did not differ significantly at baseline, except for cases having higher rates of hypertension, diabetes, and statin medication use, compared with controls. Download: PNG larger image TIFF original image Table 1. Participant demographic characteristics. https://doi.org/10.1371/journal.pone.0356296.t001 NMR and Myocardial infarction Median (IQR) NMR was greater in those with MI [0.51 (0.36 0.73)] than controls [0.47 (0.30, 0.70)]. In the conditional logistic regression model, patients with an MI had higher NMR than controls, but the difference was not statistically significant ( Table 2 ). Patients with an MI also had a greater history of statin use, hypertension, and diabetes than controls. Adjusting for these factors did not substantively change the NMR-MI relationship (OR: 1.4, 95% CI: 0.92–2.00). Download: PNG larger image TIFF original image Table 2. Conditional logistic regression analysis for factors associated with MI. https://doi.org/10.1371/journal.pone.0356296.t002 The relationship between MI and NMR did not exhibit a simple and consistent pattern. Therefore, to further investigate this, we categorized NMR into four quartiles, with each group containing the same number of people and ranked from lowest to highest. The odds of having NMR in the third versus first quartile were 1.79 times higher among cases than controls ( S1 Table ), although not a statistically significant effect (OR: 1.79, 95% CI: 0.92–3.5). Sensitivity analysis To assess the robustness of our main findings, we conducted sensitivity analyses using an alternative definition of NMR ( S3 Table ). The effect size of NMR risk on MI remained similar (OR: 1.3, 95 CI: 0.9–1.9). Excluding participants missing total cholesterol did not change the study conclusions ( S2 Table ). Discussion We examined the relationship between MI and NMR among PWH who use cigarette. Although the association between MI and NMR was not statistically significant, the elevated risk and wide confidence interval indicate substantial variability in the data. Therefore, the findings remain inconclusive. Our findings may reflect the complex interplay between MI, HIV, ART, and cigarette use, as well as some of the characteristics of our study design. A systematic review and meta-analysis found that HIV infection, low CD4, high plasma viral load, and cumulative ART use in general were associated with increased risk of MI. Cigarette use was notably not considered due to inconsistent reporting in included studies [ 21 ]. Similarly, others have reported on the association between the duration and level of exposure to HIV viremia with adverse outcomes [ 19 ]. To account for this, we estimated the HIV viremia copy years for each participant at the time of MI or censor for all available viral loads. However, we lacked data on viral load from the time of infection to the study enrollment, as the duration of HIV prior to enrollment in the CNICS database was unknown. Consequently, we could not account for differences arising from variations in the length of HIV infections. Nevertheless, given that the majority of our population has suppressed viral load, indicating adherence to ART, it is likely that they were at least somewhat protected against MI. Another challenge is ART, medications are known to increase NMR, in particular efavirenz [ 5 , 15 ]. This suggests that the choice of ART may impact nicotine metabolism. To adjust for differences in NMR that might be due to ART regimen, we calculated the NMR as a weighted average. Since cigarette use duration is estimated, this measure of NMR is potentially imperfect, as it does not fully account for the actual time spent on each ART regimen. Our study has several potential limitations. We did not have NMR measurements before participants started ART. Our assumptions of the weighted average of NMR prior to and during ART may have introduced bias, as it assumed smoking at age 16 and may not capture NMR differences due to ART variations. Reassuringly, our sensitivity analysis to explore the impact of these assumptions did not affect the overall finding. Despite our sensitivity analysis, the lack of a clear dose-response and wide confidence intervals from categorized NMR results suggests these findings may reflect limited power, potentially due to strict matching leading to exclusion of two controls, difficulty disentangling MI, HIV, ART, cigarette use, or noted study design limitations. Despite these limitations, the study has several strengths. The MI cases had been adjudicated, and it is unlikely that a control followed in CNICS had an undetected MI [ 17 ]. Including all participants who reported cigarette use eliminates confounding by cigarette use status. Matching on key variables reduced confounding, enhancing the isolation of NMR effects. Sensitivity analysis excluding participants missing total cholesterol showed results were not substantially impacted by missing data. Conclusions Our study does not provide strong evidence of a clear association between MI and NMR. However, given the higher NMR value among the cases, future studies are needed to investigate potential biological mechanisms underlying cardiovascular risk in PWH who smoke. Supporting information S1 Table. Conditional logistic regression analysis for factors associated with MI using NMR quartiles. https://doi.org/10.1371/journal.pone.0356296.s001 (DOCX) S2 Table. Sensitivity analysis for total cholesterol: Impact of missing data on NMR estimate. https://doi.org/10.1371/journal.pone.0356296.s002 (DOCX) S3 Table. Conditional logistic regression analysis for factors associated with mi using difference weighted NMR. https://doi.org/10.1371/journal.pone.0356296.s003 (DOCX) S1 File. Trapezoidal rule. https://doi.org/10.1371/journal.pone.0356296.s004 (DOCX) References 1. Prescott E, Hippe M, Schnohr P, Hein HO, Vestbo J. Smoking and risk of myocardial infarction in women and men: longitudinal population study. BMJ. 1998;316(7137):1043–7. View Article Google Scholar 2. Freiberg MS, Chang C-CH, Kuller LH, Skanderson M, Lowy E, Kraemer KL, et al. HIV infection and the risk of acute myocardial infarction. JAMA Intern Med. 2013;173(8):614–22. pmid:23459863 View Article PubMed/NCBI Google Scholar 3. Asfar T, Perez A, Shipman P, Carrico AW, Lee DJ, Alcaide ML, et al. National Estimates of Prevalence, Time-Trend, and Correlates of Smoking in US People Living with HIV (NHANES 1999–2016). Nicotine & Tobacco Research. 2021;23(8):1308–17. View Article Google Scholar 4. Calvo-Sánchez M, Perelló R, Pérez I, Mateo MG, Junyent M, Laguno M, et al. Differences between HIV-infected and uninfected adults in the contributions of smoking, diabetes and hypertension to acute coronary syndrome: two parallel case-control studies. HIV Med. 2013;14(1):40–8. pmid:23088307 View Article PubMed/NCBI Google Scholar 5. Bien-Gund CH, Bilker W, Schnoll RA, Tyndale RF, Ho JI, Bremner R. Nicotine metabolism ratio increases in HIV-positive smokers on effective antiretroviral therapy: a cohort study. J Acquir Immune Defic Syndr. 2022;89(4):428–32. View Article Google Scholar 6. Tanner J-A, Tyndale RF. Variation in CYP2A6 Activity and Personalized Medicine. J Pers Med. 2017;7(4):18. pmid:29194389 View Article PubMed/NCBI Google Scholar 7. Chenoweth MJ, Novalen M, Hawk LW Jr, Schnoll RA, George TP, Cinciripini PM, et al. Known and novel sources of variability in the nicotine metabolite ratio in a large sample of treatment-seeking smokers. Cancer Epidemiol Biomarkers Prev. 2014;23(9):1773–82. pmid:25012994 View Article PubMed/NCBI Google Scholar 8. Dempsey D, Tutka P, Jacob P 3rd, Allen F, Schoedel K, Tyndale RF, et al. Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity. Clin Pharmacol Ther. 2004;76(1):64–72. pmid:15229465 View Article PubMed/NCBI Google Scholar 9. Hamilton DA, Mahoney MC, Novalen M, Chenoweth MJ, Heitjan DF, Lerman C, et al. Test-Retest Reliability and Stability of the Nicotine Metabolite Ratio Among Treatment-Seeking Smokers. Nicotine Tob Res. 2015;17(12):1505–9. pmid:25732567 View Article PubMed/NCBI Google Scholar 10. Tanner J-
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