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Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

ABSTRACT: Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.

SUBMITTER: Rabat Y

PROVIDER: S-EPMC8773102 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

Rabat Yolaine Y Chanraud Sandra S Abdallah Majd M Sibon Igor I Berthoz Sylvie S

Biology 20211227 1

Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation tre ...[more]

PMID: 35053034

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Project description:IntroductionBy improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli ("C > P") are more likely to relapse than smokers with the opposite brain reactivity profile ("P > C").Aim and methodThe goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm's classification outcomes in an independent data set where we assessed both smokers' electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers.ResultsThe predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials.ConclusionThese results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm.ImplicationsWe assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

Project description:IntroductionSmoking cessation is an efficient approach to reducing disease burden. Traditional Chinese Medicine (TCM) therapies such as acupuncture, acupressure, and herbal drugs are often used to help quit smoking. However, there is a lack of overarching bibliometric analysis of the clinical research on smoking cessation focusing on TCM. The aim of our study is to explore the current patterns and trends of TCM therapy for smoking cessation through bibliometric methods with visual presentation.MethodsThis study is an assessment of academic publications retrieved from the Scopus database on smoking cessation using TCM therapy published in the period 2005-2021. Sankey diagram, word-cloud, network analysis, thematic maps, tree-maps, and the collaborative work of authors, institutions and countries, were used to identify research trends on TCM therapy for smoking cessation. The total cited index and H-index (for journals, authors, countries, organizations) were used to identify the trends of worldwide development by R Package and Excel 2016.ResultsThere was an upward trend, with some fluctuations, of 1908 articles from 2005 to 2021. The most productive country was China. The top institution in this field was Beijing University. The dominant author that contributed to TCM therapy for smoking cessation was Wang Y, who has the highest H-Index. The most productive cited journals were Evidence-Based Complementary and Alternative Medicines and the Chinese Journal of Clinical Rehabilitation. Liu L, (2011, STROKE) had the highest centrality. The keywords 'acupuncture', 'traditional Chinese medicine', 'colitis', 'hypertension', 'chronic obstructive pulmonary disease', 'risk factors' and 'alternative medicine' ranked highest in frequency. The diseases of healthy people concerned mainly cardiovascular, cancer, diabetes, hypertension and pregnancy. The diseases of the patients concerned mainly cancer, diabetes, hematopathy, stroke, cardiovascular, diabetes, lung disease, and hypertension. Treatment methods were mainly traditional Chinese medicine and acupuncture. The research methods mainly included randomized controlled trials that were multi-center and double-blind.ConclusionsA substantial number of articles on TCM therapy for smoking cessation, mainly focusing on TCM and acupuncture were identified. It is worth noting that research that focused on TCM therapy for smoking cessation also was related to COVID-19.

Project description:IntroductionPain and cigarette smoking are highly prevalent and frequently co-occurring conditions that interact in the manner of a positive feedback loop. Despite initial evidence that smokers with co-occurring pain may experience greater difficulty quitting, we are unaware of previous research that has tested prospective associations between pain status and the attainment of smoking cessation milestones.Aims and methodsThis study examined past 2-week pain status as a predictor of cessation milestones among current smokers who were motivated to quit (Sample 1; N = 301) and smokers who recently initiated a cessation attempt (Sample 2; N = 242). Cessation milestones included initiation of a quit attempt and 7-day point prevalence abstinence (PPA; Sample 1), lapse/relapse (Sample 2), and 7-day PPA at 2-month follow-up (both samples). Indirect associations between pain status and cessation milestones via confidence in quitting and nicotine withdrawal were also examined.ResultsSmokers with pain (vs. no pain) were as follows: less likely to initiate a quit attempt and achieve 7-day PPA; more likely to lapse and/or relapse; and less likely to report 7-day PPA at follow-up. Pain status was indirectly associated with latency cessation milestones via confidence in quitting and with latency to lapse via withdrawal severity.ConclusionsThis study demonstrated that pain status can predict smoking cessation outcomes. Clinical implications include the need to assess pain in the context of quitting and that smokers with co-occurring pain may benefit from tailored/integrated cessation interventions.ImplicationsA growing empirical literature indicates that the presence of co-occurring pain probably contributes to the maintenance of cigarette dependence. The current results provide novel evidence that smokers with co-occurring past 2-week pain are less likely to initiate a quit attempt and maintain smoking abstinence than smokers without co-occurring pain. These findings suggest that smokers with pain face unique barriers to quitting and underscore the utility of assessing and addressing pain among all smokers who are planning a smoking cessation attempt.

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Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

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Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

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