The role of personality, disability and physical activity in the development of medication-overuse headache: a prospective observational study

Radiation-induced rhinosinusitis in patients with nasopharyngeal carcinoma

BACKGROUND

Radiotherapy (RT) is the primary treatment for nasopharyngeal carcinoma (NPC), while its impact on sinonasal mucosa is unclear. We aimed to investigate the progression of sinus mucosal inflammation pre- and post-RT in NPC patients and aid in the prevention and treatment of radiation-induced rhinosinusitis (RiRS).

METHODS

This study was based on a prospective cohort with 1410 NPC patients. RiRS was evaluated by the Lund-Mackay score (LMS). A prediction model incorporating clinicopathological factors, radiomics features, and dosimetric parameters was developed using least absolute shrinkage regression and validated internally and externally using the area under the receiver operating characteristic curve, calibration tests, and decision curve analysis. LMS trajectories were analyzed using latent-class growth analysis.

RESULTS

1017 patients with a median follow-up of 106.5 months were enrolled. Before RT, 7.5 % of patients (n = 76) had rhinosinusitis, which has increased to 47.2 % after RT. The V30Gy of the sinuses (relative sinus volume receiving doses > 30 Gy), pre-RT LMS abnormalities, and tumor invasion of sphenoidal base independently influenced RiRS. The prediction model based on these three factors exhibited good discrimination and calibration. Three distinct trajectories of RiRS were identified: increasing LMS, decreasing LMS with a high starting score, and decreasing LMS with a low starting score. V70Gy showed significant association with the LMS trajectories.

CONCLUSION

RiRS is a common complication in NPC patients, influenced by radiation dose in the sinuses, baseline sinus mucosa status, and tumor invasion. Patients with large sinus volumes receiving high radiation doses are more likely to develop RiRS.

Role of Psychological Factors in Migraine

Migraine, marked by moderate to severe headaches, is frequently accompanied by reversible neurological symptoms. Recent studies have revealed a complex and significant relationship between psychological factors and the onset and progression of migraine. Personality traits, such as neuroticism and harm avoidance, play a crucial role in the development, progression, and treatment outcomes of migraines. Migraine patients often experience comorbid mental health conditions, such as depression and anxiety, which contribute to a diminished quality of life by exacerbating migraine-related disability and impaired occupational functioning. In particular, high levels of pain catastrophizing and anxiety sensitivity in migraine sufferers reflect their negative cognitive beliefs, which are closely linked to both their personality traits and vulnerability to mental disorders. This article explores the pathophysiological mechanisms underlying the relationship between migraine and psychological factors, including genetic influences, overlapping brain regions, 5-hydroxytryptamine (5-HT) dysfunction, and neurogenic inflammation. While traditional pharmacological treatments for migraine are often influenced by psychological factors and may have limited efficacy, psychotherapy, such as cognitive behavioral therapy and positive thinking therapy, has been increasingly recognized for its role in alleviating the psychological symptoms associated with migraine and enhancing overall therapeutic outcomes.

Prediction models for identifying medication overuse or medication overuse headache in migraine patients: a systematic review

BACKGROUND

Migraine is a debilitating neurological disorder that presents significant management challenges, resulting in underdiagnosis and inappropriate treatments, leaving patients at risk of medication overuse (MO). MO contributes to disease progression and the development of medication overuse headache (MOH). Predicting which migraine patients are at risk of MO/MOH is crucial for effective management. Thus, this systematic review aims to review and critique available prediction models for MO/MOH in migraine patients.

METHODS

A systematic search was conducted using Embase, Scopus, Medline/PubMed, ACM Digital Library, and IEEE databases from inception to April 22, 2024. The risk of bias was assessed using the prediction model risk of bias assessment tool.

RESULTS

Out of 1,579 articles, six studies with nine models met the inclusion criteria. Three studies developed new prediction models, while the remaining validated existing scores. Most studies utilized cross-sectional and prospective data collection in specific headache settings and migraine types. The models included up to 53 predictors, with sample sizes from 17 to 1,419 participants. Traditional statistical models (logistic regression and least absolute shrinkage and selection operator regression) were used in two studies, while one utilized a machine learning (ML) technique (support vector machines). Receiver operating characteristic analysis was employed to validate existing scores. The area under the receiver operating characteristic (AUROC) for the ML model (0.83) outperformed the traditional statistical model (0.62) in internal validation. The AUROCs ranged from 0.84 to 0.85 for the validation of existing scores. Common predictors included age and gender; genetic data and questionnaire evaluations were also included. All studies demonstrated a high risk of bias in model construction and high concerns regarding applicability to participants.

CONCLUSION

This review identified promising results for MO/MOH prediction models in migraine patients, although the field remains limited. Future research should incorporate important risk factors, assess discrimination and calibration, and perform external validation. Further studies with robust designs, appropriate settings, high-quality and quantity data, and rigorous methodologies are necessary to advance this field.

Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment

Background: The selection strategy of non-steroidal anti-inflammatory drugs (NSAIDs) for migraine is hard to judge whether it is effective, leading to unnecessary exposure to insufficient or lengthy treatment trials. The goal of the study was to investigate potential predictors of NSAIDs efficacy in migraine therapy and to explore their influence on efficacy. Methods: 610 migraine patients were recruited and assigned into responders and non-responders. Potential predictors among demographic and clinical characteristics for NSAIDs efficacy were extracted using multivariable logistic regression (LR) analysis, and were applied to construct prediction models via machine learning (ML) algorithms. Finally, Cochran-Mantel-Haenszel tests were used to examine the impact of each predictor on drug efficacy. Results: Multivariate LR analysis revealed migraine-related (disease duration, headache intensity and frequency) and psychiatric (anxiety, depression and sleep disorder) characteristics were predictive of NSAIDs efficacy. The accuracies of ML models using support vector machine, decision tree and multilayer perceptron were 0.712, 0.741, and 0.715, respectively. Cochran-Mantel-Haenszel test showed that, for variables with homogeneity of odds ratio, disease duration, frequency, anxiety, and depression and sleep disorder were associated with decreased likelihood of response to all NSAIDs. However, the variabilities in the efficacy of acetaminophen and celecoxib between patients with mild and severe headache intensity were not confirmed. Conclusion: Migraine-related and psychiatric parameters play a critical role in predicting the outcomes of acute migraine treatment. These models based on predictors could optimize drug selection and improve benefits from the start of treatment.

Disrupted Functional Connectivity of the Amygdala Predicts the Efficacy of Non-steroidal Anti-inflammatory Drugs in Migraineurs Without Aura

Machine learning (ML) has been largely applied for predicting migraine classification. However, the prediction of efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine is still in the early stages. This study aims to evaluate whether the combination of machine learning and amygdala-related functional features could help predict the efficacy of NSAIDs in patients with migraine without aura (MwoA). A total of 70 MwoA patients were enrolled for the study, including patients with an effective response to NSAIDs (M-eNSAIDs, n = 35) and MwoA patients with ineffective response to NSAIDs (M-ieNSAIDs, n = 35). Furthermore, 33 healthy controls (HCs) were matched for age, sex, and education level. The study participants were subjected to resting-state functional magnetic resonance imaging (fMRI) scanning. Disrupted functional connectivity (FC) patterns from amygdala-based FC analysis and clinical characteristics were considered features that could promote classification through multivariable logistic regression (MLR) and support vector machine (SVM) for predicting the efficacy of NSAIDs. Further, receiver operating characteristic (ROC) curves were drawn to evaluate the predictive ability of the models. The M-eNSAIDs group exhibited enhanced FC with ipsilateral calcarine sulcus (CAL), superior parietal gyrus (SPG), paracentral lobule (PCL), and contralateral superior frontal gyrus (SFG) in the left amygdala. However, the M-eNSAIDs group showed decreased FC with ipsilateral caudate nucleus (CAU), compared to the M-ieNSAIDs group. Moreover, the M-eNSAIDs group showed higher FC with left pre-central gyrus (PreCG) and post-central gyrus (PoCG) compared to HCs. In contrast, the M-ieNSAIDs group showed lower FC with the left anterior cingulate cortex (ACC) and right SFG. Furthermore, the MwoA patients showed increased FC with the left middle frontal gyrus (MFG) in the right amygdala compared to HCs. The disrupted left amygdala-related FC patterns exhibited significant correlations with migraine characteristics in the M-ieNSAIDs group. The MLR and SVM models discriminated clinical efficacy of NSAIDs with an area under the curve (AUC) of 0.891 and 0.896, sensitivity of 0.971 and 0.833, and specificity of 0.629 and 0.875, respectively. These findings suggest that the efficacy of NSAIDs in migraine could be predicted using ML algorithm. Furthermore, this study highlights the role of amygdala-related neural function in revealing underlying migraine-related neuroimaging mechanisms.

The importance of an early onset of migraine preventive disease control: A roundtable discussion

Background:

Newly approved migraine preventive therapies have allowed for rapid control of migraine activity, offering potential to minimize the burden of migraine. This report summarizes a roundtable discussion convened to analyze evidence for early onset of prevention, ascertain its clinical relevance, and provide guidance for healthcare professionals in crafting goals and treatment expectations for patients with migraine initiating preventive therapy.

Methods:

A virtual roundtable meeting of migraine clinicians, researchers, and patient advocates convened in October 2020. Participants reviewed and discussed data summarizing patient and healthcare professional perceptions of migraine prevention and evidence from the peer-reviewed and gray literature to develop corresponding recommendations.

Summary:

Evidence from clinical studies of anti-calcitonin gene-related peptide monoclonal antibodies (erenumab, fremanezumab, galcanezumab, and eptinezumab) and the chemodenervation agent onabotulinumtoxinA indicate that patients may experience reduction of migraine activity within 7 days of drug administration and early attainment of disease control is associated with improvements in clinically important outcomes. The roundtable of experts proposes that early onset be defined as demonstration of preventive benefits within 1 week of treatment initiation. We recommend focusing discussion with patients around “disease control” and potential benefits of early onset of prevention, so patients can set realistic preventive therapy goals and expectations.

Candidate Genes and Proteomic Biomarkers of Serum and Urine in Medication-Overuse Headache

Chronic headache is a topical problem of neurology, psychiatry and general practice. The medication-overuse headache (MOH) is one of the leading pathologies in the structure of chronic headache. However, early diagnosis of the MOH is challenging. We analyzed potential proteomic biomarkers of serum and urine in patients with MOH.

METHODS

We searched PubMed, Springer, Scopus, Web of Science, ClinicalKey, and Google Scholar databases for English publications over the past 10 years using keywords and their combinations.

RESULTS

We found and analyzed seven studies that met the search criteria for the purpose of the review, including 24 serum proteomic biomarkers and 25 urine proteomic biomarkers of MOH. Moreover, the candidate genes and locus of the studied serum (vitamin D-binding protein, lipocalin-type prostaglandin D2 synthase, apolipoprotein E, etc.) and urine proteomic biomarkers (uromodulin, alpha-1-microglobulin, zinc-alpha-2-glycoprotein, etc.) of MOH are presented in this review.

CONCLUSIONS

The serum and urine proteomic biomarkers of MOH can potentially help with the identification of patients with MOH development. Due to the relevance of the problem, the authors believe that further investigation of the MOH proteomic biomarkers in different ethnic and racial groups of patients with primary headache is necessary. In addition, it is important to investigate whether medications of different drug classes influence the levels of serum and urine proteomic biomarkers.

Medication Overuse Headache

Background

Medication overuse headache (MOH) is one of the highly disabling headache disorder and affects about 1% of the population of the world. It is associated with the development of headache for 15 days or more, with consumption of acute symptomatic medications for 10-15 days (depending on the class of drug, like, simple analgesics, triptans, and opioids) in a month, used for relief of headache for three or more months, in a known patient of primary headache disorder.

Objective

The aim of this study was to review the topic of MOH and present the details of this disorder with an emphasis on recent updates in the field of pathophysiology and treatment.

Material and Methods

Literature search was performed in the PubMed/MEDLINE and Cochrane database with appropriate keywords and relevant full-text articles were reviewed for writing this article.

Results

Over the years, the concept of MOH has evolved, although the exact pathophysiology is still being explored. In a susceptible individual interplay of genetics, change in pain pathways, changes in areas of the brain associated with the perception of pain, and changes in the neurotransmitters have been implicated. It has to be differentiated from other secondary chronic daily headache disorders, by a careful history, targeted examination, details of intake of medications. Treatment predominantly involves patient education, removal of the offending agent, and initiation of prophylactic medications for primary headache disorder in the outpatient or inpatient services.

Conclusions

MOH is a secondary headache disorder, which should be considered in any chronic headache patient. There are various pathophysiological mechanisms attributed to its development. Management includes educating the patients about the disorder, detoxification, and prophylactic therapy.

Personality and Headaches: Findings From Six Prospective Studies

OBJECTIVE

The present study examined the association between personality traits and concurrent and incident headaches.

METHODS

Participants (n = 34,989), aged 16 to 107 years were from the Midlife in the United States study, the Midlife in Japan study, the Health and Retirement Study, the Wisconsin Longitudinal Study Graduate and Siblings samples, and the Longitudinal Internet Studies for the Social Sciences. Demographic factors, personality traits, and headaches were assessed at baseline. Headaches were assessed again 4 to almost 20 years later.

RESULTS

Across the samples, higher neuroticism was related to a higher likelihood of concurrent (combined odd ratio = 1.41, 95% confidence interval [CI] = 1.28-1.55, p < .001) and incident (combined odd ratio = 1.28, 95% CI = 1.12-1.46, p < .001) headaches, whereas higher extraversion was associated with a lower likelihood of concurrent (combined odd ratio = 0.87, 95% CI = 0.84-0.89, p < .001) and incident (combined odd ratio = 0.90, 95% CI = 0.85-0.96, p = .001) headaches. Higher conscientiousness (combined odd ratio = 0.90, 95% CI = 0.86-0.94, p < .001) and openness (combined odd ratio = 0.95, 95% CI = 0.90-0.99, p = .025) were associated with a lower probability of reporting concurrent headaches. Agreeableness was unrelated to headaches. Sex was not a consistent moderator.

CONCLUSIONS

The present study provides robust evidence that neuroticism and introversion are risk factors for headaches in concurrent and prospective analyses across multiple cohorts.

Impact of Medical Care on Symptomatic Drug Consumption and Quality of Life in Headache: A One-Year Population Study

Background: Chronic headache is one of the most common pain conditions, often leading to symptomatic drug overuse. The aim of this study was to provide data on symptomatic drug consumption in an Italian outpatient population and to describe how the clinical picture of headache may change after headache experts take charge of the care of affected individuals.

Methods: A total of 199 adults complaining of chronic headache were recruited through 32 pharmacies in the Pavia health district. Participants underwent four evaluations: a baseline assessment (T0) and three follow-up evaluations performed by a neurologist at 3, 6, and 12 months (T3, T6, and T12, respectively). On each occasion, they underwent a complete neurological assessment and received therapeutic adjustments to achieve better management of their headache.

Results: On the basis of a preliminary telephone interview, the prevalence rates of chronic headache and medication overuse headache (MOH) were 16 and 12%, respectively. At 12 months of follow-up, we observed a significant decrease in the frequency of attacks (T0: 9 ± 9/month vs. T12: 2 ± 2/month; p < 0.001), in the number of days/month with headache (T0: 11 ± 9 vs. T12: 4 ± 4; p < 0.001) and in single attack duration (T0: 34 ± 30 h vs. T12: 10 ± 19 h; p < 0.001). Careful headache management resulted in a significant decrease in analgesic consumption (T0: 12 ± 16 vs. T12: 4 ± 6 doses/month; p = 0.014) and a significant increase in quality of life, measured using the Migraine Disability Assessment Scale (MIDAS) and Headache Under-Response to Treatment (HURT) scales (p < 0.001).

Conclusions: Headache management by a specialist is more effective than self-treatment, resulting in an overall benefit for headache patients.

Pathophysiology, prevention, and treatment of medication overuse headache

Regular or frequent use of analgesics and acute antimigraine drugs can increase the frequency of headache, and induce the transition from episodic to chronic headache or medication overuse headache. The 1-year prevalence of this condition in the general population is between 1% and 2%. Medication overuse headache is more common in women and in people with comorbid depression, anxiety, and other chronic pain conditions. Treatment of medication overuse headache has three components. First, patients need education and counselling to reduce the intake of medication for acute headache attacks. Second, some patients benefit from drug withdrawal (discontinuation of the overused medication). Finally, preventive drug therapy and non-medical prevention might be necessary in patients at onset of treatment or in patients who do not respond to the first two steps. The optimal therapeutic approach requires validation in controlled trials.