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Abstract

Background

The term ‘precision medicine’ encompasses strategies to optimize diagnosis and outcome prediction and to tailor treatment for individual patients, in consideration of their unique characteristics. The greater availability of multifaceted datasets and strategies to model such data have made precision medicine increasingly possible in recent years. Precision medicine is especially needed in the migraine field since the response to migraine treatments is not universal amongst all individuals with migraine.

Objective

To provide a narrative review describing contributions to achieving precision medicine for migraine treatment.

Methods

A search of PubMed for English language articles of human participants published from 2005 to January 2024 was conducted to identify articles that reported research contributing to precision medicine for migraine treatment. The published literature was categorized and summarized according to the type of data that were included: clinical phenotypes, genomics, proteomics, physiologic measures, and brain imaging.

Results

Published studies have investigated characteristics associated with acute and preventive treatment responses, such as nonsteroidal anti-inflammatory drugs, triptans, onabotulinumtoxinA, and anti-calcitonin gene-related peptide monoclonal antibodies, in patients with episodic or chronic migraine. There is evidence that clinical, genetic, epigenetic, proteomic, physiologic, and brain imaging features might associate with migraine treatment outcomes, although inconsistencies for such findings clearly exist.

Conclusions

The published literature suggests that there are clinical and biological features which associate with, and might be useful for predicting, migraine treatment responses. To achieve precision medicine for migraine treatment, further research is needed that validates and expands on existing findings and tests the accuracy and value of migraine treatment prediction models in clinical settings.

Keywords

headache migraine precision medicine genomics proteomics imaging prediction treatment personalized medicine

Introduction

Differences in genetic, epigenetic, biological, and environmental characteristics can influence the optimal treatment approach for individual patients. This understanding has led modern medicine to an era of ‘precision medicine’, which can be defined as ‘a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects,’ (1) or as ‘providing the right treatment at the right time to the right person and taking into account patients’ health history, genes, environments, and lifestyles’ (2,3). The precision medicine approach goes beyond treating the disease and instead prioritizes treating the patient. Precision medicine aims to deliver comprehensive, personalized care tailored to the individual needs of each patient (4).

Adopting a precision medicine approach to migraine treatment could reduce the time required to find effective and tolerable treatment. Traditional approaches to migraine management have often relied on a one-size-fits-all model in which acute and preventive treatments are recommended based on knowledge obtained from large groups of people with migraine, such as cohorts enrolled into clinical trials. Although such data are essential for establishing the safety and efficacy of migraine treatments, they typically contribute little to determining which treatment is best suited for each individual patient. Introducing a precision medicine approach to migraine treatment would be a paradigm shift, addressing the unique aspects of each patient and their disease, and ultimately better alleviating migraine-associated burden (5 –7).

Precision medicine in the context of migraine treatment might rely on a diverse array of data types that contribute to a more nuanced understanding of individual patient profiles. Clinical data, including medical history, comorbidities, ictal and interictal migraine characteristics, and prior treatment responses, might be vital for tailoring migraine treatment recommendations. Clinical data that could contribute to precision medicine might be captured through patient interviews, questionnaires, and migraine diaries. Genomic information is likely to be crucial for understanding migraine susceptibility, symptomology, and comorbidity with other traits and conditions (8). Genomic and epigenomic data are likely to be useful for understanding the risk of migraine progression and for selecting migraine therapies. Pharmacogenomic data, which investigates genetic variants impacting drug response, can help clinicians select amongst medications to reduce side effects and maximize treatment effectiveness (9). Measurement of blood or saliva biomarkers, such as ictal and interictal neuropeptides, may pave the way for selecting treatments that target the underlying pathophysiology of an individual person's migraine disease (10,11). Real-time monitoring of physiological parameters, made possible by collecting digital health data from wearables and mobile devices, potentially facilitates the objective measurement of migraine attack onset and resolution, appropriate timing of treatment administration, and responses to such treatments. Neuroimaging data, including structural and functional imaging, might identify specific brain regions involved in an individual's migraine pathophysiology and indicate the severity of migraine disease, potentially assisting with making treatment decisions (12). Machine learning and artificial intelligence algorithms can assist in evaluating large, multifaceted, complex datasets to develop predictive models for migraine treatment responses (13).

We hope that advances in precision medicine will transform migraine treatment from a one-size-fits-all approach to a more personalized and targeted strategy. Overall, precision medicine has the potential to revolutionize migraine therapy by allowing for the selection of specific treatments that are most likely to be effective for an individual patient.

Methods

Search strategy

PubMed was searched for English language articles of human participants published from 2005 to January 2024. The titles, abstracts, and/or manuscripts were reviewed by each author group and those deemed to be original and most relevant to understanding progress made in achieving precision medicine for migraine treatment were selected for inclusion. Search terms are included in Online Supplement 1. For phenotypic data that might contribute to precision medicine in migraine treatment, 29 articles were identified. After reviewing all the abstracts, 23 were chosen for inclusion. One additional article (14), which was included in the reference list of another (15), was included given the relevance to precision medicine. For genomics and other ‘omics’, 735 articles were identified and screened, and 105 and 480 articles were included and excluded, respectively. Review of the other 150 articles resulted in conflicting decisions regarding their inclusion, which was resolved by a third author; ultimately, 45 of these articles were included for full-text screening. In total, 150 articles were included in full-text screening and 31 articles that are relevant to this manuscript's topic were included. Six additional articles, included in the reference lists of the 31 articles, were also included. For physiologic measures, 24 articles were identified; after further review, 14 were chosen for inclusion. Three of these articles (15,–17) are discussed in the phenotypic data section. For brain imaging, 80 articles were identified, of which 11 articles were selected after review.

Results

Phenotypic data

Published studies have investigated patient and migraine characteristics associated with acute and preventive treatment responses (Table 1).

Table 1.

Main findings on clinical characteristics associated with acute and preventive migraine treatment responses.

Author	Year	Treatment	Predictors of Treatment Response
Ezzati (14)	2023	NSAIDs, CCP, ASA, Acetaminophen	Lower average headache pain intensity, less cutaneous allodynia, lower MIDAS grade, less depression, lower MSSS
Viana (18)	2021	Frovatriptan	Unilateral pain, presence of phonophobia, presence of one or more cranial autonomic symptoms, presence of one or more premonitory symptom
Ishii (19)	2012	Triptans	Older age, absence of pain location (periorbital, deep orbital)
Diener (20)	2004	Sumatriptan	Lower baseline pain severity and disability, absence of vomiting
Lipton (21)	2017	Diclofenac potassium	No prior triptan use
Lu (22)	2020	NSAIDs	Shorter disease duration, lower headache intensity, lower frequency, absence of anxiety, absence of depression, and absence of sleep disorder
Lipton (23)	2016	Acute treatment	Cutaneous allodynia predicted inadequate response (2 h and 24 h) and inadequate sustained pain freedom (24 h) to acute treatment.
Saracco (24)	2014	Frovatriptan, rizatriptan, zolmitriptan, almotriptan	Pain-free rates at 2 h were higher in non-obese group than obese group. Pain-relapse rate at 48 h was similar in both groups with frovatriptan, however, it was significantly higher in obese group with rizatriptan, zolmitriptan, and almotriptan.
Cady (25)	2009	Almotriptan	Presence of allodynia-associated symptoms did not influence efficacy of almotriptan for 2 h pain freedom, 2 h pain relief, sustained pain relief, and need for rescue medication use.
Schiano di Cola (26)	2019	BoNT-A	Absence of depressive comorbidity, absence of medication overuse
Ornello (27)	2022	BoNT-A	Presence of medication overuse, lower number of MHD
Eross (28)	2005	BoNT-A	Migraine duration
Martinelli (29)	2023	BoNT-A	Response: older age at migraine onset, higher anxiety subscore in HADS Non-Response: opioid use, higher MIDAS score
Quintas (30)	2019	BoNT-A	None
Ornello (31)	2020	BoNT-A	More recent onset of CM and more headache free days at baseline
Zecca (32)	2023	Erenumab	Absence of hypertension
Zecca (33)	2022	Erenumab	At least 50% reduction in migraine days: non-significant results At least 75% reduction in migraine days: older age at migraine onset, fewer failed preventive medications, higher baseline MIDAS
Eghtesadi (34)	2021	Erenumab	None
Ihara (35)	2023	Erenumab, Galcanezumab, Fremanezumab	Older age, fewer number of prior treatment failures, absence of immuno-rheumatologic disease history
Gonzalez-Martinez (36)	2023	Erenumab, Galcanezumab, Fremanezumab	EM, lower number of baseline MHD
Barbanti (37)	2022	Erenumab, Galcanezumab, Fremanezumab	At least 50% response, HFEM: Unilateral pain, unilateral cranial autonomic symptoms At least 50% response, CM: Unilateral pain, unilateral cranial autonomic symptoms, allodynia, no obesity At least 75% response, HFEM: Unilateral pain, unilateral cranial autonomic symptoms At least 75% response, CM: Unilateral pain, unilateral cranial autonomic symptoms, allodynia 100% response: non-significant results
Caronna (38)	2021	Erenumab, Galcanezumab	Medication overuse resolution: lower baseline pain severity
Argyriou (39)	2023	Fremanezumab	50–74% response: HFEM, strict unilateral pain, pain location in the ophthalmic trigeminal branch At least 75% response: Allodynia
Yalinay Dikmen (40)	2023	Galcanezumab	Previous failures of 2 or fewer preventive medications
Vernieri (41)	2022	Galcanezumab	Lower body mass index, fewer failed preventive treatments, more frequent unilateral pain, medication overuse at baseline, good response to triptans
Lee (42)	2023	Galcanezumab	Accompanying symptoms of migraine, absence of everyday headache, absence of depression
Kim (43)	2023	Galcanezumab	Absence of CM, fewer prior failures
Pensato (44)	2022	Erenumab	Allodynia was negative predictor for erenumab response in patients with CM with MOH.
Pijpers (45)	2023	Detoxification for MOH	Absence of allodynia predicted reversion from CM to EM after detoxification for MOH.
Messina (46)	2020	Candesartan	Younger age, longer disease duration, and absence of daily headaches
Lee (47)	2007	Topiramate	Paresthesia

*The same cohort was used for these two analyses. CM: chronic migraine; NSAIDs: nonsteroidal anti-inflammatory drugs; CCP: caffeine combination products; ASA: acetylsalicylic acid; BoNT-A: onabotulinumtoxinA; MIDAS: migraine disability assessment scale; MSSS: migraine symptom severity scale; MMD: monthly migraine days; HADS: hospital anxiety and depression scale; EM: episodic migraine, HFEM: high-frequency episodic migraine; rNMS: repetitive neuromuscular magnetic stimulation.

Acute treatment

Ezzati et al. analyzed data from 2224 participants with episodic migraine (EM) enrolled in the American Migraine Prevalence and Prevention (AMPP) (14) study to investigate predictors of treatment response to over-the-counter non-steroidal anti-inflammatory drugs (NSAIDs), caffeine combination products, acetylsalicylic acid, and acetaminophen (14). The achievement of adequate pain freedom at 2 h was associated with a lower pre-treatment average headache pain intensity, lower Migraine Disability Assessment Scale (MIDAS) grade, less depression, and lower Migraine Symptom Severity Scale score. Another analysis of the AMPP study of 8233 individuals with EM revealed that a higher BMI was associated with inadequate 2-h pain-free response to acute medications, which was theorized to be due to a pro-inflammatory state associated with obesity, and consistent with another study focused on triptan response (23,24).

A prospective diary study in Italy collected data from three consecutive migraine attacks for which frovatriptan was used for acute treatment. Frovatriptan was found more likely to be effective in patients experiencing unilateral pain, phonophobia, cranial autonomic symptoms, and prodrome symptoms (18). A Japanese study of 60 patients with migraine found that older age and absence of periorbital/deep orbital pain were factors significantly associated with triptan response (19). Other potential predictors of response have been identified, including lower baseline pain severity as a predictor of sumatriptan response (20), no prior triptan use as a diclofenac potassium response predictor (21), and psychiatric characteristics as NSAID response predictors (22).

Furthermore, presence of allodynia symptoms, defined as the sensation of pain in response to a normally non-nociceptive stimulus, has been reported as a negative predictor of acute treatment efficacy (23,25); however, the results are not consistent, with another study reporting cutaneous allodynia as a positive predictor for response to NSAIDs (14).

Preventive treatment

Several studies have investigated the phenotypic predictors of the response to onabotulinumtoxinA (BoNT-A), with varying results. For example, medication overuse (MO) has been noted as both a positive (27) and negative predictor (26). A lower number of monthly headache days (MHD) (27) was considered a positive predictor of response, and longer duration (28) and depressive symptoms (26) were listed as negative predictors, but these findings were not reproduced in other reports. One study revealed that a combination of age at migraine onset, opioid use, anxiety, and MIDAS score, was significantly associated with BoNT-A response (29). Regarding the ‘wearing-off’ of response (i.e., a clinical response with a duration shorter than 10 weeks), a longitudinal study of 193 patients found no significant demographic or baseline headache characteristics to be predictive (30). An Italian study of 115 patients found that more recent onset of CM and a greater number of headache-free days at baseline were predictors of sustained response to BoNT-A over several treatment cycles (31).

There are numerous recent publications describing predictors of response to anti-calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) (32,–43). Typically, these studies defined responders as those experiencing ≥50% reduction in monthly migraine days (MMDs) or monthly headache days (MHDs) after three to six months of CGRP mAb treatment compared to baseline. Other studies have used HIT-6 score reduction (32) or MO resolution (38) as outcomes, while others have focused on specific populations such as those 65 years of age or older (36) or patients who experienced treatment failure with ≥4 preventive medications (34). Several treatment response predictors have been identified across some of these observational real-world studies. For example, at least five studies noted that fewer numbers of previous preventive treatments are a positive predictor of response (33,35,40,41,43). Lower headache frequency, measured as lower baseline MHDs (36), absence of everyday headache (42), presence of EM (36,39), or absence of CM (43), have all been associated with treatment response. Other characteristics consistently identified to be positively associated with treatment response include unilateral pain (37,39,41), lower body mass index (41), and absence of obesity (37). Other studies have identified unique predictors of treatment response that warrant further investigation, such as the absence of immune-rheumatologic disease history (35), absence of hypertension (32), and presence of non-headache symptoms that characterize the migraine attack including nausea, vomiting, and photophobia (42). Previous studies on allodynia revealed mixed results. A real-world study of erenumab found pre-treatment allodynia symptoms were associated with non-response (44). Additionally, a study of CM with MO showed that the likelihood of remission to EM after treatment was higher in patients without pre-treatment cutaneous allodynia symptoms as assessed by the ASC-12 (45). However, two studies (37,39) showed that baseline self-reported allodynia was positively associated with response to CGRP mAbs.

There have also been several reports on conventional treatment options. A real-world study including 253 patients found that candesartan response was positively associated with younger age and longer disease duration and negatively associated with experiencing daily headaches (46). For topiramate, paresthesias, which are a relatively common side effect of topiramate, predicted good response (47).

Summary: Phenotypic data

Numerous studies have sought to identify phenotypic predictive factors for response to acute and preventive migraine treatments. Though many studies have identified predictive factors, due to varying results from study-to-study, no firm conclusions can yet be made.

Genomics and other ‘-omics’

Advances in genomic technology and understanding of the complex molecular mechanisms underlying migraine have provided valuable insights into genetic predictors of treatment response. The key findings from studies that contribute to precision medicine approaches for migraine treatment using ‘omics’ data are presented below (Table 2).

Table 2.

Main findings on genomic and proteomic factors associated with acute and preventive migraine treatment responses.

Author	Year	Category and Specific Biomarker	Treatment	Main Findings
Chase (48)	2024	Polygenic risk scores	CGRP mAb	Non-responder: rs12615320-G in RAMP1 and rs4680-A in COMT. Lower mean genetic risk score than responders, fraction of responders increased with genetic and polygenic risk score percentile.
Kogelman (49)	2019	Polygenic risk scores	Migraine specific acute treatment	Increase in PRS associated with positive migraine-specific acute treatment response, OR 1.25 (95% CI = 1.05–1.49)
Cargnin (50)	2019	Genetic risk score	Triptans	Genetic risk score including susceptibility risk alleles at TRPM8 rs6724624 and FGF6 rs1024905: inversely associated with risk of inconsistent response to triptans, OR, 0.62 (95% CI = 0.43–0.89)
Terrazzino (51)	2010	Genetic risk score	Triptans	STin2 VNTR polymorphism of serotonin transporter gene: higher risk of inconsistent response to triptans
Christensen (52)	2016	SNPs	Triptans	OR of treatment success with triptans, 1.3–2.6: depending on genetic load of 1–12 SNPs associated with migraine (rs2651899 in PRDM16)
Moreno-Mayordomo (53)	2019	CALCA and TRPV1	BoNT-A	Polymorphic variations of CALCA and TRPV1 genes: correlate with positive outcome of OnabotulinumtoxinA in female CM
Mehta (54)	2023	Epigenomics	Medication Overuse Discontinuation	CM-MO: reduction in DNA methylation at an intronic CpG site (cg14377273) within the HDAC4 gene was associated with MHD 50% response 12 wks following the withdrawal of acute medication. Lower baseline DNAm at a CpG site (cg15205829) within MARK3 was associated with MMD response at 12 weeks.
Kogelman (55)	2021	RNA sequences, differential expression analysis	Sumatriptan	Serial blood sample after subcutaneous sumatriptan: differentially expressed genes within the serotonergic synapse (KEGG pathway hsa04726) between sumatriptan responders and nonresponders was investigated: No overall difference but significantly different gene DE: BRAF (P = 3.79 × 10–4)
Alpuente (56)	2022	CGRP	N/A	Photophobia and phonophobia more associated with CGRP dependent attacks than non-CGRP dependent attacks
Cernuda-Morollón (57)	2014	CGRP, VIP	BoNT-A	Interictal CGRP threshold 72 pg/mL, prediction of response to BoNT-A in 95% of cases; CGRP level above threshold multiplies the probability of response by 28.
Greco (58)	2020	CGRP, miR-382-5p and miR-34a-5p	Medication Overuse Discontinuation	CM-MO: detoxification decreased CGRP levels and miRNAs expression
Lee (59)	2018	CGRP	Preventive Medications	Higher serum CGRP concentration did not predict treatment response in CM
Alpuente (60)	2022	CGRP	Erenumab	12 wks erenumab: Higher pretreatment salivary CGRP levels: higher probability of having at least 50% response in EM, but not in CM.
Bellamy (61)	2006	CGRP, VIP	Sumatriptan	Sumatriptan resulted in decreased levels of salivary CGRP and VIP, which correlated with relief of symptoms
Cady (62)	2014	CGRP	BoNT-A	Decrease in interictal salivary CGRP levels for subjects receiving onabotulinumtoxinA
Cady (63)	2009	CGRP	Rizatriptan	Increased level of saliva CGRP predicted response to rizatriptan.
Sarchielli (64)	2006	CGRP, neurokinin A, VIP	Rizatriptan	decrease in CGRP and NKA levels one hour after rizatriptan administration in responders. VIP levels were also significantly reduced at the same time
Zagami (65)	2014	PACAP	Sumatriptan	Elevated PACAP in the external jugular vein during headache, that was reduced 1 h after treatment with sumatriptan 6 mg, and further reduced interictally.
Tuka (66)	2013	PACAP	N/A	Lower PACAP-38-like immunoreactivity in interictal plasma of migraine compared with HC. Elevated levels in ictal period relative to attack-free period. Negative correlation between interictal levels and disease duration.
Han (15)	2015	PACAP	N/A	Lower interictal plasma PACAP levels in migraine than TTH and healthy controls. Interictal PACAP levels negatively correlated with duration of CM.
Hirfanoglu (67)	2009	TNF alpha	Preventive Treatments	Migraine, 77 children, Cytokine levels (tumor necrosis factor a, interleukin-1b, interleukin-6) decreased and leptin levels increased after treatment (cyproheptadine, amitriptyline, propranolol, orflunarizine)
Coveli (68)	1992	TNF alpha	Propranolol	Serum TNF level normalized after three months of propranolol

CGRP mAb: anti-calcitonin gene-related peptide monoclonal antibodies; CGRP: calcitonin gene-related peptide; CM: chronic migraine; MO: medication-overuse; PRS: polygenetic risk score; CI: confidence interval; OR: odds ratio; SNP: single nucleotide polymorphisms; HM: hemiplegic migraine; MMD: monthly migraine days; MHD: monthly headache days; NKA: neurokinin A; VIP: vasoactive intestinal peptide; PACAP: pituitary adenylate cyclase activating peptide; N/A: not applicable.

Genomics

Acute treatment

In the recent few decades, genome-wide association studies (GWAS) have found variants, mostly single nucleotide polymorphisms (SNPs), that are associated with susceptibility to migraine (69 –74). This finding suggests that treatment outcomes could potentially be different according to patients’ genetic profiles. A polygenic risk score (PRS) study identified an association between an increased migraine risk score with a positive response to triptans, with an odds ratio (OR) of 1.25 (95% confidence interval [CI] = 1.05–1.49) (49), but found no significant association between PRS with response to analgesics or preventive treatments. Another study (50) utilized risk scores based on SNPs to predict headache response to triptans in migraine without aura, revealing that the alleles at TRPM8 rs6724624 and FGF6 rs1024905 were inversely associated with the risk of inconsistent responses to triptans. Additional gene polymorphisms of the serotonin transporter gene SLC6A4, and specifically the STin2 VNTR, also confer a higher risk of inconsistent response to triptans (51). In a study assessing triptan response, a collective burden of 1 to 12 SNPs increased the odds ratios (ORs), ranging from 1.3 to 2.6, for treatment success with triptans (52). Specifically, the variant rs2651899 in PRDM16 exhibited a significant association with triptan efficacy, showing an OR of treatment success at 1.3, while a higher aggregate genetic score correlated significantly with triptan efficacy, yielding an OR of success up to 2.6. Combined analyses of triptans and ergotamine strengthened this association.

Preventive treatment

A retrospective study identified characteristics associated with the response to CGRP mAbs, emphasizing the role of genetic factors in predicting treatment efficacy (48). More specifically, in 198 genotyped patients, non-responders were associated with rs12615320-G in RAMP1 (OR [95% confidence interval]: 4.7 [1.5, 14.7]), and rs4680-A in COMT (0.6 [0.4, 0.9]). Non-responders had a lower mean genetic risk score than responders (1.0 vs. 1.1; t(df) = −1.75(174.84), p = 0.041), and the fraction of responders increased with genetic and polygenic risk score percentile. Moreno-Mayordomo et al. identified polymorphisms in CALCA and TRPV1 genes that correlate with positive treatment outcomes in female CM patients receiving BoNT-A (53).

Epigenetic alterations and medication withdrawal

Longitudinal genomic analysis demonstrated that alterations in DNA methylation are associated with reduced migraine and headache days after medication withdrawal treatment in patients with CM and MO (54). These results show that a decrease in HDAC4 DNA methylation status over time, as well as an initial low MARK3 DNA methylation status, are both linked to treatment efficacy. This offers evidence for the involvement of pathways connected to chromatin structure and synaptic plasticity in the progression of headaches and suggests a potential role for epigenetic mechanisms in migraine pathophysiology and treatment response. Epigenetic alterations can also result from the use of specific medications. Sumatriptan acts as a serotonin receptor (5-HT1B/D) agonist. Serial blood samples collected after subcutaneous sumatriptan treatment were analyzed to identify the genes within serotonergic synapses. No overall difference was found between sumatriptan responders and non-responders. However, the gene, BRAF was found to be significantly differentially expressed (55). Additionally, the ‘5HT1 type receptor mediated signaling pathway’ was found to be enriched among the top expression quantitative trait loci.

Other biomarkers

In addition to genomics, salivary and plasma biomarkers of migraine have been investigated. Studies of CGRP, vasoactive intestinal polypeptide (VIP), pituitary adenylate cyclase-activating polypeptide (PACAP), and others are introduced.

Measurement of salivary CGRP might distinguish between CGRP-dependent and CGRP-independent migraine attacks (56,75). In one study, patients were divided into two categories based on the extent of change in CGRP between the preictal and ictal phases: CGRP-dependent (79.6%) and non-CGRP-dependent migraine attacks (20.4%). Symptoms such as photophobia and phonophobia were notably linked to the former group. Although yet to be proven, presumably, CGRP-dependent attacks would be more responsive to CGRP-targeting therapies. Other studies have identified elevated CGRP and peripheral microRNAs (miR-382-5p, miR-34a-5p) associated with CM with MO compared to EM patients (p = 0.003 for all comparisons) (11,58). CGRP plasma concentrations also displayed a significant positive correlation with miR-382-5p and miR-34a-5p across the entire population. In the CM with MO subgroup, detoxification notably reduced CGRP levels and miRNA expression.

A comparison between responders and non-responders to detoxification revealed that the former exhibited significantly higher CGRP levels at baseline and decreased expression of miR-382-5p following detoxification. The association between baseline CGRP levels and treatment response with erenumab has been investigated (60), which showed that elevated salivary CGRP levels before treatment were significantly linked to a greater likelihood of experiencing a ≥ 50% reduction in headache frequency among individuals with EM, but not in those with CM. Following 12 weeks of erenumab treatment, salivary CGRP levels among patients across all migraine frequency ranges became comparable. However, this convergence was not observed in patients with concurrent depressive symptoms, potentially providing insights into the more challenging nature of treating CM with concomitant mood disorders. Salivary CGRP might also be useful for predicting treatment response to triptans and BoNT-A (57,62,63).

In summary, various investigations have highlighted elevated CGRP levels in venous blood, saliva, and tear fluid among migraine patients compared to healthy controls, especially during migraine attacks, suggesting CGRP as a potential migraine biomarker. Yet, conflicting findings and methodological challenges mar the reliability of CGRP measurements in migraine studies (59). Indeed, inconsistent differences in CGRP levels between CM and EM further complicate interpretation. Additionally, CGRP’s involvement extends beyond migraine to non-migraine pain disorders like cluster headaches and osteoarthritis. Therefore, many authors suggested that, presently, CGRP levels lack justification as a migraine diagnostic or severity marker. Further, previous evidence should be interpreted carefully given the challenges with measuring CGRP and variation in sampling methods (i.e., venous blood, saliva, and tear fluid). Nevertheless, measurement of CGRP concentration holds promise as a future biomarker for predicting therapeutic responses, notably to anti-CGRP migraine medications (76).

Similarly, VIP (61,64) has been suggested to serve as a therapeutic marker for triptan therapy and BoNT-A efficacy, particularly in CM (57). PACAP also shows promise as a therapeutic marker for triptan therapy (65,77). Human clinical studies have consistently shown increased plasma PACAP-38 levels during the ictal phase of migraine, specifically during spontaneous migraine attacks, in comparison to the interictal period (65,66). Notably, interictal plasma PACAP concentrations were significantly lower in people with migraine than in healthy controls or those with tension-type headache (15,65,66). Additionally, PACAP levels in the external jugular vein decrease with migraine headache improvement following sumatriptan treatment. Lower PACAP levels were observed between attacks compared to during attacks, indicating a potential correlation with migraine phases (65).

There is evidence that tumor necrosis factor alpha (TNF-α), high sensitivity c-reactive protein, and adiponectin could serve as biomarkers for predicting and/or monitoring migraine treatment efficacy (67,68). Together, these findings underscore the potential for a diverse set of biomarkers being associated with treatment responses, monitoring disease progression, and tailoring migraine therapies to individual patient needs (11).

Summary: Genomics and other ‘-omics’

Genomic studies demonstrate the potential for PRS, SNPs, and epigenetic alterations as predictors of migraine treatment response. In addition, measurement of peptides, such as CGRP, might assist with determining which peptide(s) are driving a person's migraine attacks and thus with selection amongst migraine medications. However, existing results must be interpreted carefully due to challenges with measuring each biomarker and varying methodologies between published studies.

Physiologic measures and brain imaging

Studies have explored relationships between physiological measurements, measurements of pain thresholds, and brain imaging data with treatment outcomes (Table 3).

Table 3.

Main findings on physiologic and brain imaging features associated with acute and preventive migraine treatment responses.

Author	Year	Topic	Treatment	Predictors of Treatment Response
Wei (16)	2024	Functional connectivity	NSAIDs	In NSAID nonresponders compared with responders, causal connectivity from bilateral ACC to LG was increased and connectivity in the opposite direction was decreased. Compared with the healthy controls, nonresponders had heightened causal connectivity from ACC to LG, right IOG, and left superior occipital gyrus, however, diminished connectivity patterns from LG and right IOG to ACC were observed.
Lee (78)	2023	Structural connectivity	Sumatriptan	Global structural connectivity differed between newly diagnosed migraine patients and healthy controls. Local structural connectivity differed significantly between responders and non-responders to sumatriptan.
Wu (79)	2022	Hippocampal volume	Sumatriptan	Larger left hippocampal volume predicted response to sumatriptan.
Ashina (80)	2023	Allodynia	Galcanezumab	Non-ictal cephalic allodynia identified galcanezumab responders with nearly 80% accuracy and non-responders with nearly 85% accuracy, while non-ictal extracephalic allodynia did so less accurately.
Peng (81)	2022	Pain threshold	Galcanezumab	Pre-treatment heat pain threshold at the forearm predicted galcanezumab response.
Nowaczewska (82)	2021	Cerebral blood flow	Erenumab	Baseline Vm in right cerebral arteries and basilar artery is reduced in erenumab responders compared with non-responders. This difference normalized after erenumab treatment with significant increase in Vm of cerebral arteries.
Nowaczewska (83)	2022	Cerebral blood flow	Erenumab Fremanezumab	Baseline Vm in MCAs were significantly lower in responders. ≥ 50% reduction in MMD was significantly negatively associated with Vm in right MCA.
Chuang (84)	2023	Heart rate variability	Flunarizine	CM had reduced heart rate variability compared to healthy controls. Patients with normal heart rate variability were likely to be superior responders to flunarizine.
Ahmed (17)	2023	White matter hyperintensities	Ibuprofen Topiramate	Patients who did not respond to ibuprofen and topiramate had higher frequency of WMHs and higher WMHs Scheltens score. Vomiting, dizziness, and migraine with aura were significant predictors for developing WMHs.
Bumb (85)	2013	White matter lesions	BoNT-A	No difference between responders and non-responders to BoNT-A for age, age at onset, gender, attack duration, frequency, aura, white matter lesions, and size of white matter lesions.
Basedau (86)	2022	Functional MRI	Erenumab	Galcanezumab decreased hypothalamic activation in all patients. The reduction was greater in responders.
Fu (87)	2022	Functional MRI	Transcutaneous vagus nerve stimulation	Seventy predictive features involved in trigeminal cervical complex/rostral ventromedial medulla, medial prefrontal cortex, temporal gyrus, middle cingulate cortex, and insula were identified to predict the reduction in attack frequency after transcutaneous VNS in patients with migraine without aura.
Feng (88)	2022	Resting-state functional MRI	Transcutaneous auricular vagus nerve stimulation	Baseline fALFF features involved in thalamocortical circuits, default mode network, and descending pain modulation system may predict response to transcutaneous auricular VNS in patients with migraine without aura.
Nahman-Averbuch (89)	2021	Resting-state functional connectivity Conditioned pain modulation	Cognitive behavioral therapy	Greater baseline functional connectivity between the right amygdala and frontal gyrus, anterior cingulate cortex, and precentral gyrus was related to response to CBT. Response to CBT was related with less efficient baseline conditioned pain modulation response at the trapezius but not at the leg.

NSAIDs: nonsteroidal anti-inflammatory drugs; CCP: caffeine combination products; fMRI: functional magnetic resonance imaging; ACC: anterior cingulate cortex; LG: lingual gyrus; IOG: inferior occipital gyrus; HFEM: high-frequency episodic; CM: chronic migraine; EM: episodic migraine; MOH: medication overuse headache; Vm: mean blood flow velocity; MCA: middle cerebral artery; WMHs: white matter hyperintensities; BoNT-A: onabotulinumtoxinA; fALFF: fractional amplitude of low frequency fluctuation; MRI: magnetic resonance imaging; MMD: monthly migraine day; VNS: vagus nerve stimulation.

Acute treatment

One study investigated the relationship between brain resting state functional causal connectivity between the anterior cingulate cortex and visual cortex and response to NSAIDs (16). Compared with responders to NSAIDs, the non-responders showed a different causal connectivity between bilateral anterior cingulate cortices with the lingual gyrus. For sumatriptan, two studies identified possible neuroimaging characteristics associated with a good response. In one study, in which diffusion tensor imaging was used to measure brain structural connectivity, pre-treatment local connectivity of the thalamic nuclei differed between sumatriptan responders and non-responders (78). In another study, greater left hippocampal volume was a predictor of response to sumatriptan: 84.6% of those with a hippocampal volume >4032.6 mm³ responded to sumatriptan, whereas 42.1% of those with a smaller hippocampal volume were responders (79).

Preventive treatment

Using quantitative sensory testing (QST) to determine the presence of interictal allodynia before the administration of galcanezumab, a study demonstrated that pre-treatment presence of allodynia was associated with a worse clinical response after three months of treatment. In a separate study, higher pre-treatment heat pain thresholds on the forearm (demonstrating less sensitivity to the stimulus) predicted response to galcanezumab (80,81).

Studies using transcranial Doppler to compare changes in cerebral blood flow have found that the pre-treatment mean blood flow velocity value in the middle cerebral artery is significantly lower in responders than in non-responders to erenumab (82). A larger study including both erenumab and fremanezumab also found consistent results (83). Additionally, a study of CM patients found that those with a normal heart rate variability had a better response to flunarizine compared to those with a reduced heart rate variability, which is suggestive of autonomic dysfunction (84).

Two previous studies have investigated the usefulness of brain white matter hyperintensities (WMHs) for predicting treatment response. One study showed that the higher the WMH burden, including larger size, higher number, and higher Scheltens score, the more likely to have a poor response to ibuprofen and topiramate, though the results should be carefully interpreted given that potential confounding factors were not adjusted (17). On the other hand, another study found that neither the presence nor size of WMHs predicted the effect of BoNT-A (85).

Additionally, a brain functional MRI study investigated 26 migraine patients, who were imaged before and a few weeks after galcanezumab treatment. Response to galcanezumab was associated with baseline activity of the spinal trigeminal nucleus and its coupling with the hypothalamus (86).

Two studies investigated the prediction of neuromodulation effectiveness using the fractional amplitude of low-frequency fluctuation (fALFF) measurements obtained with functional brain MRI. In patients undergoing transcutaneous vagus nerve stimulation (tVNS), migraine attack frequency reduction was correlated with pre-treatment fALFF of the trigeminal cervical complex/rostral ventromedial medulla, prefrontal cortex, temporal gyrus, middle cingulate cortex, and insula (87). The other study also demonstrated that the efficacy of transcutaneous auricular VNS can be predicted by pre-treatment fALFF in several brain regions, including prefrontal cortex and posterior cingulate gyrus (88).

A study investigating predictors of treatment response to eight weeks of cognitive behavioral therapy in adolescents, found that resting-state functional connectivity and conditioned pain modulation (CPM) measures before treatment could be used as predictors (89). This study revealed that a reduction in headache days was correlated with functional connectivity between regions in the right amygdala, precentral gyrus, anterior cingulate cortex, and frontal lobe, and a lower CPM response.

Summary: Physiologic measures and brain imaging

Several studies of cutaneous pain thresholds and structural and functional brain imaging identified predictive factors for migraine treatment response. However, like with the other data modalities discussed above, validation studies are needed to confirm study findings.

Discussion and conclusions

Evidence suggests that there are phenotypic, genomic, proteomic, physiologic, and brain imaging features that might associate with response to migraine treatments. However, the literature includes studies with conflicting results and there is a lack of validation studies. As the available evidence is rapidly expanding, it is reasonable to assume that migraine treatment could, in the upcoming years, be tailored to the individual using a precision medicine approach. Such an approach would shorten the time required for patients to receive an effective treatment, thereby reducing migraine burden and treatment-associated frustration.

To assess the progress towards achieving precision medicine in migraine treatment, the limitations of published studies should be carefully considered. For example, some of the studies included in this article used a retrospective design and included relatively small cohorts of patients, raising the possibility of recall and selection bias, and leading to uncertainties about the generalizability of study results. There are conflicting results regarding the nature of associations between certain features with treatment outcomes. Additionally, it is critical to note that features which are associated with treatment response are not found exclusively in responders or non-responders. Published studies commonly report patient and headache characteristics that are observed more frequently in treatment responders as predictive factors; however, sometimes these characteristics are also seen in non-responders. Furthermore, the cost and ease by which data can be collected needs to be considered when developing migraine treatment prediction models, since the goal is for such models to be available and used in a clinical setting.

To achieve precision medicine for migraine treatment, existing findings need to be confirmed and validated. In addition to investigating associations between treatment response with patient features, future studies with prospective designs should focus on pre-treatment prediction of treatment responses. Published studies often identified predictors of response to an individual treatment type, such as a single migraine medication. Although that could be useful clinically, treatment prediction models that consider more than one treatment and help to select amongst those treatments would be even more useful in the clinical setting. For example, it would be particularly practical to have a model that considered multiple migraine preventive or acute medications simultaneously and determined which of the medications was most likely to be effective for an individual patient. Overall, the published literature demonstrates advancements and feasibility of achieving precision medicine for migraine treatment. Continued work is needed to reach this most important goal.

Key findings

A precision medicine approach to migraine treatment would tailor treatment recommendations according to individual patient characteristics and would reduce the time needed to find effective and tolerable treatments.

There are clinical, genomic, proteomic, physiologic, and brain imaging features that might associate with responses to acute and preventive migraine treatments.

Further research is needed to develop models that accurately predict migraine treatment responses and tolerability, and that assist with choosing amongst treatment options.

Supplemental Material

sj-docx-1-cep-10.1177_03331024241281518 - Supplemental material for Are we closer to achieving precision medicine for migraine treatment? A narrative review

Supplemental material, sj-docx-1-cep-10.1177_03331024241281518 for Are we closer to achieving precision medicine for migraine treatment? A narrative review by Keiko Ihara, Francesco Casillo, Ahmed Dahshan, Hamit Genç, Asel Jusupova, Kunduz Karbozova, Wonwoo Lee, Yi Chia Liaw, Theodoros Mavridis, Hong-Kyun Park, Burcu Polat, Triin Helin Unt, Nina Vashchenko, Aisha Zhantleuova, Patricia Pozo-Rosich, and Todd J. Schwedt in Cephalalgia

Footnotes

Author contributions

Authors of this manuscript include attendees and faculty from the 2023 International Headache Academy (iHEAD) education conference held in Seoul, South Korea. Authors were divided into five groups: 1) introduction; 2) overview of data types contributing to precision medicine in migraine management; and summaries of progress in achieving precision medicine in migraine management using 3) phenotypes, 4) genomics and other ‘omics’, and 5) physiological measures and brain imaging. The output from the five groups was then combined and all authors reviewed, revised, and approved the final manuscript.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: K.I., F.C., A.D., H.G., A.J. and K.K.: None. W.L.: Was involved as a site investigator of a multicenter trial sponsored by Eli Lilly and Co., WhanIn Pharm Co. Ltd., and Handok-Teva. He has received lecture honoraria from Abbott and SK chemical in the past 36 months. Y.C.L. and T.M.: None H.K.P.: Lecture honorarium from AbbVie, Allergan, Amgen, Boryung Pharmaceutical Co., Ltd, Chong Kun Dang Pharmaceutical Corp, Dong-A ST, Jeil Pharmaceutical Co, Ltd, Pfizer, SAMJIN Pharm, SK Chemicals, TEVA-HANDOK, Takeda Pharmaceuticals Korea Co, Ltd, and YuYu. B.P., T.H.U., N.V. and A.Z.: None. P.P.R.: has received honoraria as a consultant and speaker for: AbbVie, Amgen, Biohaven, Eli Lilly, Lundbeck, Medscape, Novartis, Pfizer and Teva. Her research group has received research grants from Novartis, Teva, AbbVie, EraNET Neuron, RIS3CAT FEDER, AGAUR, ISCIII; has received funding for clinical trials from Alder, Amgen, Biohaven, Electrocore, Eli Lilly, Lundbeck, Novartis, Teva. She is the Honorary Secretary of the International Headache Society. She is the founder of . P.P.R. does not own stocks from any pharmaceutical company. T.J.S.: Consulting income from AbbVie, Allergan, Amgen, Axsome, Biodelivery Science, Biohaven, Click Therapeutics, Collegium, Eli Lilly, Ipsen, Linpharma, Lundbeck, Novartis, Satsuma, Scilex, Theranica, and Tonix. Royalties from Up To Date. Stock options in Aural Analytics and Nocira. Research funding from American Heart Association, Amgen, Henry Jackson Foundation, National Headache Foundation, National Institutes of Health, Patient Centered Outcomes Research Institute, Pfizer, Spark Neuro, and United States Department of Defense.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Francesco Casillo

Hamit Genç

Wonwoo Lee

Yi Chia Liaw

Theodoros Mavridis

Hong-Kyun Park

Aisha Zhantleuova

Patricia Pozo-Rosich

Todd J. Schwedt

Supplemental Material

Supplemental material for this article is available online.

References

Marchiano

RDM

Di Sante

Piro

, et al. Translational research in the era of precision medicine: where we are and where we will go. J Pers Med 2021; 11: 216.

Akhoon

. Precision medicine: a new paradigm in therapeutics. Int J Prev Med 2021; 12: 12.

Stone

. Precision medicine: health care tailored to you. The White House Blog, 2016. https://obamawhitehouse.archives.gov/blog/2016/02/25/precision-medicine-health-care-tailored- you

Gomez

Ansotegui

Canonica

. Will precision medicine be available for all patients in the near future? Curr Opin Allergy Clin Immunol 2019; 19: 75–80.

Johnson

Freitag

. New approaches to shifting the migraine treatment paradigm. Front Pain Res (Lausanne) 2022; 3: 873179.

Ashina

Terwindt

Al-Karagholi

, et al. Migraine: disease characterisation, biomarkers, and precision medicine. Lancet 2021; 397: 1496–1504.

Zhang

Dong

. Migraine in the era of precision medicine. Ann Transl Med 2016; 4: 105.

Sutherland

Albury

Griffiths

. Advances in genetics of migraine. J Headache Pain 2019; 20: 72.

Belyaeva

Subbotina

Eremenko

, et al. Pharmacogenetics in primary headache disorders. Front Pharmacol 2021; 12: 820214.

10.

Demartini

Francavilla

Zanaboni

, et al. Biomarkers of migraine: an integrated evaluation of preclinical and clinical findings. Int J Mol Sci 2023; 24: 5334.

11.

Ferroni

Barbanti

Spila

, et al. Circulating biomarkers in migraine: new opportunities for precision medicine. Curr Med Chem 2019; 26: 6191–6206.

12.

Ellingson

Hesterman

Johnston

, et al. Advanced imaging in the evaluation of migraine headaches. Neuroimaging Clin N Am 2019; 29: 301–324.

13.

Bohr

Memarzadeh

. The rise of artificial intelligence in healthcare applications. Artif Intell Healthc 2020: 25–60.

14.

Ezzati

Fanning

Reed

, et al. Predictors of treatment-response to caffeine combination products, Acetaminophen, acetylsalicylic acid (aspirin), and nonsteroidal anti-inflammatory drugs in acute treatment of episodic migraine. Headache 2023; 63: 342–352.

15.

Han

Dong

Hou

, et al. Interictal plasma pituitary adenylate cyclase-activating polypeptide levels are decreased in migraineurs but remain unchanged in patients with tension-type headache. Clin Chim Acta 2015; 450: 151–154.

16.

Wei

Yang

Zhou

, et al. Abnormal causal connectivity of anterior cingulate cortex-visual cortex circuit related to nonsteroidal anti-inflammatory drug efficacy in migraine. Eur J Neurosci 2024; 59: 446–456.

17.

Ahmed

Mohamed

AAM

Salem

, et al. Association of white matter hyperintensities with migraine phenotypes and response to treatment. Acta Neurol Belg 2023; 123: 1725–1733.

18.

Viana

Sances

Terrazzino

, et al. Predicting the response to a triptan in migraine using deep attack phenotyping: a feasibility study. Cephalalgia 2021; 41: 197–202.

19.

Ishii

Sakairi

Hara

, et al. Negative predictors of clinical response to triptans in patients with migraine. Neurol Sci 2012; 33: 453–461.

20.

Diener

Ferrari

Mansbach

, et al. Predicting the response to sumatriptan: the sumatriptan naratriptan Aggregate patient database. Neurology 2004; 63: 520–524.

21.

Lipton

Schmidt

Diener

. Post hoc subanalysis of two randomized, controlled phase 3 trials evaluating diclofenac potassium for oral solution: impact of migraine-associated nausea and prior triptan use on efficacy. Headache 2017; 57: 756–765.

22.

Dong

Wei

, et al. Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment. Front Pharmacol 2022; 13: 1002080.

23.

Lipton

Munjal

Buse

, et al. Predicting inadequate response to acute migraine medication: results from the American migraine prevalence and prevention (AMPP) study. Headache 2016; 56: 1635–1648.

24.

Saracco

Allais

Tullo

, et al. Efficacy of frovatriptan and other triptans in the treatment of acute migraine of normal weight and obese subjects: a review of randomized studies. Neurol Sci 2014; 35: 115–119.

25.

Cady

Freitag

Mathew

, et al. Allodynia-associated symptoms, pain intensity and time to treatment: predicting treatment response in acute migraine intervention. Headache 2009; 49: 350–363.

26.

Schiano di Cola

Caratozzolo

Liberini

, et al. Response predictors in chronic migraine: medication overuse and depressive symptoms negatively impact onabotulinumtoxin-A treatment. Front Neurol 2019; 10: 678.

27.

Ornello

Baraldi

Ahmed

, et al. Excellent response to OnabotulinumtoxinA: different definitions, different predictors. Int J Environ Res Public Health 2022; 19: 10975.

28.

Eross

Gladstone

Lewis

, et al. Duration of migraine is a predictor for response to botulinum toxin type A. Headache 2005; 45: 308–314.

29.

Martinelli

Pocora

De Icco

, et al. Searching for the predictors of response to BoNT-A in migraine using machine learning approaches. Toxins (Basel) 2023; 15: 364.

30.

Quintas

Garcia-Azorin

Heredia

, et al. Wearing off response to OnabotulinumtoxinA in chronic migraine: analysis in a series of 193 patients. Pain Med 2019; 20: 1815–1821.

31.

Ornello

Guerzoni

Baraldi

, et al. Sustained response to onabotulinumtoxin A in patients with chronic migraine: real-life data. J Headache Pain 2020; 21: 40.

32.

Zecca

Terrazzino

Para

, et al. Response to erenumab assessed by headache impact test-6 is modulated by genetic factors and arterial hypertension: an explorative cohort study. Eur J Neurol 2023; 30: 1099–1108.

33.

Zecca

Cargnin

Schankin

, et al. Clinic and genetic predictors in response to erenumab. Eur J Neurol 2022; 29: 1209–1217.

34.

Eghtesadi

Leroux

Page

. Real-life response to erenumab in a therapy-resistant case series of migraine patients from the province of Quebec, Eastern Canada. Clin Drug Investig 2021; 41: 733–739.

35.

Ihara

Ohtani

Watanabe

, et al. Predicting response to CGRP-monoclonal antibodies in patients with migraine in Japan: a single-centre retrospective observational study. J Headache Pain 2023; 24: 23.

36.

Gonzalez-Martinez

Sanz-Garcia

Garcia-Azorin

, et al. Effectiveness, tolerability and response predictors of preventive anti-CGRP mAbs for migraine in patients over 65 years old: a multicenter real-world case-control study. Pain Med 2024; 25: 194–202.

37.

Barbanti

Egeo

Aurilia

, et al. Predictors of response to anti-CGRP monoclonal antibodies: a 24-week, multicenter, prospective study on 864 migraine patients. J Headache Pain 2022; 23: 138.

38.

Caronna

Gallardo

Alpuente

, et al. Anti-CGRP monoclonal antibodies in chronic migraine with medication overuse: real-life effectiveness and predictors of response at 6 months. J Headache Pain 2021; 22: 120.

39.

Argyriou

Dermitzakis

Xiromerisiou

, et al. Predictors of response to fremanezumab in migraine patients with at least three previous preventive failures: post hoc analysis of a prospective, multicenter, real-world Greek registry. J Clin Med 2023; 12: 3218.

40.

Yalinay Dikmen

Baykan

Uluduz

, et al. Real-life experiences with galcanezumab and predictors for treatment response in Turkey. BMC Neurol 2023; 23: 418.

41.

Vernieri

Altamura

Brunelli

, et al. Rapid response to galcanezumab and predictive factors in chronic migraine patients: a 3-month observational, longitudinal, cohort, multicenter, Italian real-life study. Eur J Neurol 2022; 29: 1198–1208.

42.

Lee

Cho

Kim

. Predictors of response to galcanezumab in patients with chronic migraine: a real-world prospective observational study. Neurol Sci 2023; 44: 2455–2463.

43.

Kim

Jang

Lee

. Predictors of galcanezumab response in a real-world study of Korean patients with migraine. Sci Rep 2023; 13: 14825.

44.

Pensato

Baraldi

Favoni

, et al. Real-life assessment of erenumab in refractory chronic migraine with medication overuse headache. Neurol Sci 2022; 43: 1273–1280.

45.

Pijpers

Kies

van Zwet

, et al. Cutaneous allodynia as predictor for treatment response in chronic migraine: a cohort study. J Headache Pain 2023; 24: 118.

46.

Messina

Lastarria Perez

Filippi

, et al. Candesartan in migraine prevention: results from a retrospective real-world study. J Neurol 2020; 267: 3243–3247.

47.

Lee

Chu

Park

, et al. Paresthesia as a favorable predictor of migraine prophylaxis using topiramate. Eur J Neurol 2007; 14: 654–658.

48.

Chase

Semenov

Rubin

, et al. Characteristics associated with response to subcutaneously administered anti-CGRP monoclonal antibody medications in a real-world community cohort of persons living with migraine: a retrospective clinical and genetic study. Headache 2024; 64: 68–92.

49.

Kogelman

LJA

Esserlind

Francke Christensen

, et al. Migraine polygenic risk score associates with efficacy of migraine-specific drugs. Neurol Genet 2019; 5: e364.

50.

Cargnin

Viana

Sances

, et al. Using a genetic risk score approach to predict headache response to triptans in migraine without aura. J Clin Pharmacol 2019; 59: 288–294.

51.

Terrazzino

Viana

Floriddia

, et al. The serotonin transporter gene polymorphism STin2 VNTR confers an increased risk of inconsistent response to triptans in migraine patients. Eur J Pharmacol 2010; 641: 82–87.

52.

Christensen

Esserlind

Werge

, et al. The influence of genetic constitution on migraine drug responses. Cephalalgia 2016; 36: 624–639.

53.

Moreno-Mayordomo

Ruiz

Pascual

, et al. CALCA And TRPV1 genes polymorphisms are related to a good outcome in female chronic migraine patients treated with OnabotulinumtoxinA. J Headache Pain 2019; 20: 39.

54.

Mehta

de Boer

Sutherland

, et al. Alterations in DNA methylation associate with reduced migraine and headache days after medication withdrawal treatment in chronic migraine patients: a longitudinal study. Clin Epigenetics 2023; 15: 190.

55.

Kogelman

LJA

Falkenberg

Buil

, et al. Changes in the gene expression profile during spontaneous migraine attacks. Sci Rep 2021; 11: 8294.

56.

Alpuente

Gallardo

Asskour

, et al. Salivary CGRP can monitor the different migraine phases: CGRP (in)dependent attacks. Cephalalgia 2022; 42: 186–196.

57.

Cernuda-Morollon

Martinez-Camblor

Ramon

, et al. CGRP And VIP levels as predictors of efficacy of onabotulinumtoxin type A in chronic migraine. Headache 2014; 54: 987–995.

58.

Greco

De Icco

Demartini

, et al.

Plasma levels of CGRP and expression of specific microRNAs in blood cells of episodic and chronic migraine subjects: towards the identification of a panel of peripheral biomarkers of migraine?

J Headache Pain 2020; 21: 122.

59.

Lee

Cho

, et al. Feasibility of serum CGRP measurement as a biomarker of chronic migraine: a critical reappraisal. J Headache Pain 2018; 19: 53.

60.

Alpuente

Gallardo

Asskour

, et al. Salivary CGRP and erenumab treatment response: towards precision medicine in migraine. Ann Neurol 2022; 92: 846–859.

61.

Bellamy

Cady

Durham

. Salivary levels of CGRP and VIP in rhinosinusitis and migraine patients. Headache 2006; 46: 24–33.

62.

Cady

Turner

Dexter

, et al. An exploratory study of salivary calcitonin gene-related peptide levels relative to acute interventions and preventative treatment with onabotulinumtoxinA in chronic migraine. Headache 2014; 54: 269–277.

63.

Cady

Vause

, et al. Elevated saliva calcitonin gene-related peptide levels during acute migraine predict therapeutic response to rizatriptan. Headache 2009; 49: 1258–1266.

64.

Sarchielli

Pini

Zanchin

, et al. Clinical-biochemical correlates of migraine attacks in rizatriptan responders and non-responders. Cephalalgia 2006; 26: 257–265.

65.

Zagami

Edvinsson

Goadsby

. Pituitary adenylate cyclase activating polypeptide and migraine. Ann Clin Transl Neurol 2014; 1: 1036–1040.

66.

Tuka

Helyes

Markovics

, et al. Alterations in PACAP-38-like immunoreactivity in the plasma during ictal and interictal periods of migraine patients. Cephalalgia 2013; 33: 1085–1095.

67.

Hirfanoglu

Serdaroglu

Gulbahar

, et al. Prophylactic drugs and cytokine and leptin levels in children with migraine. Pediatr Neurol 2009; 41: 281–287.

68.

Covelli

Munno

Pellegrino

, et al. In vivo administration of propranolol decreases exaggerated amounts of serum TNF-alpha in patients with migraine without aura. Possible Mech Act 1992; 14: 313–319.

69.

Anttila

Stefansson

Kallela

, et al. Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nat Genet 2010; 42: 869–873.

70.

Anttila

Winsvold

Gormley

, et al. Genome-wide meta-analysis identifies new susceptibility loci for migraine. Nat Genet 2013; 45: 912–917.

71.

Chasman

Schurks

Anttila

, et al. Genome-wide association study reveals three susceptibility loci for common migraine in the general population. Nat Genet 2011; 43: 695–698.

72.

Freilinger

Anttila

de Vries

, et al. Genome-wide association analysis identifies susceptibility loci for migraine without aura. Nat Genet 2012; 44: 777–782.

73.

Gormley

Anttila

Winsvold

, et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat Genet 2016; 48: 856–866.

74.

Hautakangas

Winsvold

Ruotsalainen

, et al. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat Genet 2022; 54: 152–160.

75.

Cernuda-Morollon

Larrosa

Ramon

, et al. Interictal increase of CGRP levels in peripheral blood as a biomarker for chronic migraine. Neurology 2013; 81: 1191–1196.

76.

Tesfay

Karlsson

Moreno

, et al.

Is calcitonin gene-related peptide a reliable biochemical marker of migraine?

Curr Opin Neurol 2022; 35: 343–352.

77.

Edvinsson

Tajti

Szalardy

, et al. PACAP And its role in primary headaches. J Headache Pain 2018; 19: 21.

78.

Lee

Kim

Lee

, et al. Predicting sumatriptan responsiveness based on structural connectivity in patients newly diagnosed with migraine. J Clin Neurol 2023; 19: 573–580.

79.

Lai

Chen

, et al. The use of neuroimaging for predicting sumatriptan treatment response in patients with migraine. Front Neurol 2022; 13: 798695.

80.

Ashina

Melo-Carrillo

Szabo

, et al. Pre-treatment non-ictal cephalic allodynia identifies responders to prophylactic treatment of chronic and episodic migraine patients with galcanezumab: a prospective quantitative sensory testing study (NCT04271202). Cephalalgia 2023; 43: 3331024221147881.

81.

Peng

Basedau

Oppermann

, et al. Trigeminal sensory modulatory effects of galcanezumab and clinical response prediction. Pain 2022; 163: 2194–2199.

82.

Nowaczewska

Straburzynski

Meder

, et al. Changes in cerebral blood flow after erenumab treatment in good and non-responders-a pilot study of migraine patients. J Clin Med 2021; 10: 2523.

83.

Nowaczewska

Straburzynski

Waliszewska-Prosol

, et al. Cerebral blood flow and other predictors of responsiveness to erenumab and fremanezumab in migraine-A real-life study. Front Neurol 2022; 13: 895476.

84.

Chuang

King

, et al. Abnormal heart rate variability and its application in predicting treatment efficacy in patients with chronic migraine: an exploratory study. Cephalalgia 2023; 43: 3331024231206781.

85.

Bumb

Seifert

Wetzel

, et al. Patients profiling for botox(R) (onabotulinum toxin A) treatment for migraine: a look at white matter lesions in the MRI as a potential marker. Springerplus 2013; 2: 377.

86.

Basedau

Sturm

Mehnert

, et al. Migraine monoclonal antibodies against CGRP change brain activity depending on ligand or receptor target - an fMRI study. Elife 2022; 11: e77146.

87.

Zhang

, et al. Predicting response to tVNS in patients with migraine using functional MRI: a voxels-based machine learning analysis. Front Neurosci 2022; 16: 937453.

88.

Feng

Zhang

Wen

, et al. Early fractional amplitude of low frequency fluctuation can predict the efficacy of transcutaneous auricular Vagus nerve stimulation treatment for migraine without aura. Front Mol Neurosci 2022; 15: 778139.

89.

Nahman-Averbuch

Schneider

2nd Chamberlin

, et al. Identification of neural and psychophysical predictors of headache reduction after cognitive behavioral therapy in adolescents with migraine. Pain 2021; 162: 372–381.

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