Next Article in Journal
Effects of a Pedometer-Based Walking Program in Patients with COPD—A Pilot Study
Next Article in Special Issue
Managing of Migraine in the Workplaces: Knowledge, Attitudes and Practices of Italian Occupational Physicians
Previous Article in Journal
Cardiovascular Disorder after Cardiotoxic Non-Hodking’s Lymphoma Treatment: A Case Report
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 58
Issue 4
10.3390/medicina58040491
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Deciphering the Role of the rs2651899, rs10166942, and rs11172113 Polymorphisms in Migraine: A Meta-Analysis

by
Vasileios Siokas
1,†,
Ioannis Liampas
1,†,
Athina-Maria Aloizou
1,
Maria Papasavva
2,
Christos Bakirtzis
3,
Eleftherios Lavdas
4,5,
Panagiotis Liakos
6,
Nikolaos Drakoulis
2,
Dimitrios P. Bogdanos
7 and
Efthimios Dardiotis
1,*
1
Laboratory of Neurogenetics, Department of Neurology, University Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41100 Larissa, Greece
2
Research Group of Clinical Pharmacology and Pharmacogenomics, Faculty of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
3
B’ Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
4
Department of Biomedical Sciences, University of West Attica, 12243 Athens, Greece
5
Department of Medical Imaging, Animus Kyanoys Larisas Hospital, 41222 Larissa, Greece
6
Laboratory of Biochemistry, Faculty of Medicine, University of Thessaly, 41100 Larissa, Greece
7
Department of Rheumatology and clinical Immunology, University General Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 40500 Larissa, Greece
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Medicina 2022, 58(4), 491; https://doi.org/10.3390/medicina58040491
Submission received: 16 February 2022 / Revised: 23 March 2022 / Accepted: 24 March 2022 / Published: 29 March 2022
(This article belongs to the Special Issue Emerging Concepts in Migraine Pathophysiology, Treatment and Management)
Browse Figures
Versions Notes

Abstract

:
The genetic basis of migraine is rather complex. The rs2651899 in the PR/SET domain 16 (PRDM16) gene, the rs10166942 near the transient receptor potential cation channel subfamily M member 8 (TRPM8) gene, and the rs11172113 in the LDL receptor-related protein 1 (LRP1) gene, have been associated with migraine in a genome-wide association study (GWAS). However, data from subsequent studies examining the role of these variants and their relationship with migraine remain inconclusive. The aim of the present study was to meta-analyze the published data assessing the role of these polymorphisms in migraine, migraine with aura (MA), and migraine without aura (MO). We performed a search in the PubMed, Scopus, Web of Science, and Public Health Genomics and Precision Health Knowledge Base (v7.7) databases. In total, eight, six, and six studies were included in the quantitative analysis, for the rs2651899, rs10166942, and rs11172113, respectively. Cochran’s Q and I2 tests were used to calculate the heterogeneity. The random effects (RE) model was applied when high heterogeneity was observed; otherwise, the fixed effects (FE) model was applied. The odds ratios (ORs) and the respective 95% confidence intervals (CIs) were calculated to estimate the effect of each variant on migraine. Funnel plots were created to graphically assess publication bias. A significant association was revealed for the CC genotype of the rs2651899, with the overall migraine group (RE model OR: 1.32; 95% CI: 1.02–1.73; p-value = 0.04) and the MA subgroup (FE model OR: 1.40; 95% CI: 1.12–1.74; p-value = 0.003). The rs10166942 CT genotype was associated with increased migraine risk (FE model OR: 1.36; 95% CI: 1.18–1.57; p-value < 0.0001) and increased MO risk (FE model OR: 1.41; 95% CI: 1.17–1.69; p-value = 0.0003). No association was detected for the rs11172113. The rs2651899 and the rs10166942 have an effect on migraine. Larger studies are needed to dissect the role of these variants in migraine.

1. Introduction

Migraine is a complex disorder of the brain, with great variety in its pathogenesis, clinical presentation, genetic make-up, and therapeutic approach [1]. It is the second most common cephalalgia, after the tension-type headache [2]. Moreover, it is considered to be among the commonest neurological disorders globally, while it confers greater disability compared with other neurological diseases [3].
Phenotypically, migraine manifests with recurrent episodes characterized by pulsating intense pain in the head unilaterally [4]. Additionally, symptoms such as photophobia, vomiting, phonophobia, and nausea usually accompany migraine attacks [5]. Migraine with aura (MA) and migraine without aura (MO) are considered to be the major migraine subtypes, which are mainly differentiated by the presence of focal neurological symptoms that can either precede or accompany headache in patients with MA [6].
From a pathophysiological perspective, several theories including various molecular mechanisms have been connected to migraine risk, such as the release of vasoactive neuropeptides vascular dysfunction, vasodilation, defective function of brain networks, plasma protein extravasation, cortical spreading depression (CSD), and “neurogenic inflammation” [7,8]. Moreover, increased glucose uptake has been reported in patients, with migraine especially in the posterior white matter of the cerebrum and cerebellum [9]. Additionally, glucose levels and metabolism may influence the frequency of CSD, and as such, migraine development [10,11].
While the pathophysiological pathways via which the previously referred mechanisms can lead to the migraine are not fully understood [12], there are multiple lines of evidence that genetic, environmental, and epigenetic factors all contribute, to some extent, to migraine’s susceptibility [13,14]. Among the environmental factors, body mass index (BMI), smoking, dietary habits, nutrients, physical activity, and socioeconomic status (to name a few) have been incriminated in altering migraine risk or as precipitating factors for migraine attacks [15,16,17,18,19,20,21].
The genetic architecture of migraine is complex, as migraine is considered a polygenic disease, where a few genetic factors are implicated in its appearance and phenotypic traits [22,23,24]. The complexity of the genetic influence on migraine is also evident considering that triggers and factors (genetic and environmental) heavily vary amongst the affected patients [25]. Nevertheless, there are a few known mutations in single genes that can cause the entity known as the familial hemiplegic migraine (FHM) [26]. As such, mutations in the Calcium Voltage-Gated Channel Subunit Alpha1 A (CACNA1A), encoding the α1 subunit of the brain specific P/Q- type calcium channel, in ATPase Na+/K+ Transporting Subunit Alpha 2 (ATP1A2), encoding the sodium–potassium- transporting ATPase, in Sodium Voltage-Gated Channel Alpha Subunit 1 (SCN1A) encoding a voltage- gated sodium channel subunit, can all lead to FHM [26,27]. Additionally, other monogenetic migraine with aura syndromes such as cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), retinal vasculopathy with cerebral leukoencephalopathy and systemic manifestations (RVCL-S), and familial advanced sleep phase syndrome (FASPS), also exists [23]. However, phenotypic appearance may exhibit great variance (with the migraine not being a prominent feature), and there are also cases where novel mutations have been identified [27].
Apart from FHM and monogenic migraine with aura syndromes, there are polymorphisms (e.g., the MTHFR C677T, and BDNF rs6265 gene polymorphisms) that have been further found to be associated with migraine [28,29,30,31,32] and other headaches [33,34]. In 2011, in a genome-wide association study (GWAS), three genetic loci emerged as genetic risk factors for migraine [35]. These genetic loci are the rs2651899 in the PR/SET domain 16 (PRDM16) gene, the rs10166942 near the transient receptor potential cation channel subfamily M member 8 (TRPM8) gene, and the rs11172113 in the LDL receptor-related protein 1 (LRP1) gene [35]. These results for the rs10166942 and the rs11172113 have been replicated by a further GWAS [36]. However, results from subsequent studies examining the role of the aforementioned variants and their relationship with migraine remain inconclusive. While the PRDM16 rs2651899 has been reported to associate with migraine and MA and/or MO subtypes in an earlier meta-analysis [37], studies that followed revealed no association with migraine [38], while both the alleles of the polymorphism have been associated with increased migraine risk [39,40]. In the same manner, studies for the rs10166942 near the TRPM8 gene and the LRP1 rs11172113 have yielded inconsistent results [41].
In view of the former considerations, the aim of the present study was to retrieve, review, and meta-analyze the available published data assessing the role of the PRDM16 rs2651899, the rs10166942 near the TRPM8 gene, and the LPR1 rs11172113 polymorphisms on migraine. Additionally, we attempted to assess the role of these variants on the risk of the main migraine endophenotypes, namely the MA and the MO.

2. Materials and Methods

2.1. General Information

The Preferred Reporting items Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary File S1) were applied for the current meta-analysis [42], while this study was not registered in any database. Two authors (V.S. and I.L.), independently performed the processes, while any divergences were unraveled by a third author (E.D.).

2.2. Literature Search Strategy

We searched through the PubMed, Scopus, Web of Science, and Public Health Genomics and Precision Health Knowledge Base (v7.7) databases for eligible articles examining the relationship between migraine and the PRDM16 rs2651899, the rs10166942 near the TRPM8 gene, and the LPR1 rs11172113 polymorphisms (the last search was performed on 18 March 2022). The search for each variant was performed separately. We used the term “migraine” in combination with either “rs2651899”, or “rs10166942”, or “rs11172113”, as free words. The PubMed algorithm of the literature search for the present meta-analysis is presented at Supplementary File S2.

2.3. Identification of Eligible Articles

We initially checked titles and abstracts of identified articles for relevance. From the articles that passed the initial screening, full texts were retrieved. Additionally, the reference lists of the identified articles were scanned for supplementary eligible studies.

2.4. Eligibility Criteria

Studies that met the following criteria were included: (1) written in the English language, (2) publication before the 18 March 2022, and (3) the absolute genotype numbers for the examined variants were available for controls and patients with migraine. Data from GWASs and studies containing irrelevant data were not included.

2.5. Data Extraction

The following data from each eligible study were extracted when possible: (1) author, (2) year of publication, (3) ethnicity/location of the examined population, (4) numbers (n) of cases with migraine and controls, (5) age at onset of migraine, (6) mean age of the participants during examination, (7) number of males and females in patients with migraine and controls, (8) criteria applied to the assessment of the diagnosis of migraine, (9) test of the Hardy–Weinberg Equilibrium (HWE) principle, (10) applied method for correction for multiple comparisons, (11) genotype absolute numbers, and (12) main results.

2.6. Statistical Analysis

2.6.1. Calculation of the Effect Size

Statistical analyses were performed with Review Manager (RevMan) Version 5.4 (https://training.cochrane.org/online-learning/core-software-cochrane-reviews/revman/revman-5-download, accessed on 16 December 2021). The odds ratios (ORs) and the respective 95% confidence intervals (CIs) were calculated in order for the effect of each variant on migraine to be estimated. The following effects were calculated: (a) homozygosity for the variant allele genotype, (b) heterozygosity, and (c) homozygosity for the wild-type allele. Three phenotypic traits were considered as outcomes: (a) migraine, (b) MA, and (c) MO. Statistically significant values were considered those with values lower than 0.05 (p < 0.05).

2.6.2. Heterogeneity and Assessment of Publication Bias

Cochran’s Q and I2 tests were used to calculate the heterogeneity. The random effects (RE) model was applied when high heterogeneity was observed (PQ < 0.10 and/or I2 > 75%) [43]; otherwise, the fixed effects (FE) model was applied [44]. Funnel plots were created to graphically assess the publication bias.

3. Results

3.1. Study Selection and Study Characteristics

3.1.1. PRDM16 rs2651899

The search of the databases (after the removal of duplicate records) yielded 14 articles, published between 2011 and 2021. After the initial evaluation of titles and abstracts, three articles were excluded as review articles. Consequently 11 full texts were examined for eligibility. Four articles [35,38,45,46] were excluded (GWAS or no available genotypic data). One additional eligible study was identified via the manual screening of the reference lists [47]. Thus, eight studies were finally included in the quantitative meta-analysis [39,40,41,47,48,49,50,51], consisting of 2320 patients with migraine and 2615 controls. The flowchart with the selection procedure of eligible studies for the PRDM16 rs2651899 is presented in Supplementary File S3. The baseline characteristics of the studies that fulfilled the eligibility criteria are presented in Table 1.

3.1.2. rs10166942 near TRPM8 Gene

The search of the databases (after the remove of duplicate records) yielded 14 articles, published between 2011 and 2021. After the initial evaluation of titles and abstracts, three articles were excluded (no genetic studies or review articles). Consequently 11 full texts were examined for eligibility. Five articles [35,45,46,52,53] were excluded (GWAS or no available genotypic data, no examination of this polymorphism). As such, six studies were finally included in the quantitative meta-analysis [39,40,41,48,50,51], consisting of 1633 patients with migraine and 1514 controls. The flowchart with the selection procedure of eligible studies for the rs10166942 is presented in Supplementary File S4. The baseline characteristics of the studies that fulfilled the eligibility criteria are depicted in Table 1.

3.1.3. LPR1 rs11172113

The search of the databases (after the remove of duplicate records) yielded 18 articles, published between 2011 and 2022. After the initial evaluation of titles and abstracts, four articles were excluded (no genetic studies or review articles). Consequently 14 full texts were examined for eligibility. Eight articles [35,38,45,46,54,55,56,57] were excluded (GWAS or no available genotypic data). Accordingly, six studies were finally included in the quantitative meta-analysis [41,47,48,50,51,58], consisting of 1462 patients with migraine and 1659 controls. The flowchart with the selection procedure of eligible studies for the LPR1 rs11172113 is presented in Supplementary File S5. The baseline characteristics of the studies that fulfilled the eligibility criteria are depicted in Table 1.
Table
Table 1. The baseline characteristics of the studies included in the current meta-analysis.
Table 1. The baseline characteristics of the studies included in the current meta-analysis.
CasesControls
Author (Year) [Ref]Population or Location Gene (Polymorphism)HWE Test/Multiple Test Correction Diagnosis Assessment Mean Age ± SD/Age of Onset ± SD nMale/Female Mean Age ± SD nMale/Female Main Results and Comments
An et al. (2013) [48]Han-ChinesePRDM16 (rs2651899); TRPM8 (rs10166942); and LPR1 (rs11172113)Yes (cases and controls)/-International Classification
of Headache Disorders, 2nd edition (ICHDII)		36.0 ± 10.9 years/-20737/17035.8 ± 11.5
years		20549/156The rs2651899 G allele was associated with migraine and MO in allelic mode. No association for the TRPM8 rs10166942 and the LPR1 rs11172113.
Gosh et al. (2013) [50]IndiaPRDM16 (rs2651899); TRPM8 (rs10166942); and LPR1 (rs11172113)Yes (controls)/Yes (Benjamini–Hochberg false discovery
rate (FDR) test)		International Classification
of Headache Disorders, 2nd edition (ICHDII)		-/<50 years340-matched200matchedProtective effect of the rs2651899 (T) on migraine and MO susceptibility (genotypic, dominant, allelic models). Protective effect of the LPR1 rs11172113 C allele on migraine MA and MO in various models.
No association for the TRPM8 rs10166942.
Fan et al. (2014) [51]Han-ChinesePRDM16 (rs2651899); TRPM8 (rs10166942); and LPR1 (rs11172113)Yes (controls)/Yes (Bonferroni)International Classification
of Headache Disorders, 2nd edition (ICHDII)		40.65 ± 12.18 years/24.03 ± 11.13 years30453/251matched304matchedThe rs2651899 minor allele (C) was associated with migraine and MO. No association for the TRPM8 rs10166942 and the LPR1 rs11172113.
Sintas et al. (2015) [40]SpanishPRDM16 (rs2651899); TRPM8 (rs10166942); and LPR1 (rs11172113)Yes (cases and controls)/10,000 permutations and
Bonferroni’s correction		International Classification
of Headache Disorders, 2nd edition (ICHDII)		-/13.5 ± 12 years51278.13% femalematched53578.83% femaleThe rs2651899 minor allele (C) was nominally associated with migraine and MA. The TRPM8 rs10166942 (T) allele nominally associated with migraine. No significance remained after multiple comparison correction.
An et al. (2017) [47]ChinesePRDM16 (rs2651899); and LPR1 (rs11172113)Yes (controls)/Yes (Benjamini–Hochberg false discovery
rate (FDR) test and Bonferroni)		International Classification of Headache Disorders
(ICHD-III beta)		-/35.4 ± 10.2 years58161/52034.8 ± 8.9 years53357/476The rs2651899 C allele was associated MO and migraine with family history subgroup. No association for the LPR1 rs11172113.
Ran et al. (2018) [49]SwedishPRDM16 (rs2651899);Yes/-International Classification
of Headache Disorders, 2nd edition (ICHDII)		-100--58156.3%
male		No association.
Kaur et al. (2019) [39]North IndianPRDM16 (rs2651899) and TRPM8 (rs1016694)Yes (controls)/-International Classification of Headache
disorders, 3rd edition		35.28 ± 6.6 years/15040/110no statistical difference in terms of age as p = 0.3515060% femalesThe rs2651899 T allele was associated with migraine in genotypic, allelic, and dominant model. Association was found for the variant with the MO and the female migraineurs. The TRPM8 rs1016694 was associated with MA and in males.
Kaur et al. (2019) [58]IndiaLPR1 (rs11172113)Yes/-International Classification of Headache
disorders, 3rd edition		MA:35.13 ± 6.0 years/-
MO: 36.40 ± 5.2 years/-		10028/7234.45 ± 7.6 years10038/62No association
Zafar et al. (2021) [41]PakistanPRDM16 (rs2651899); TRPM8 (rs10166942); and LPR1 (rs11172113Yes (controls)/-International Classification
of Headache Disorders, 2nd edition (ICHDII)		25.79 ± 5.19 years/-12731/9626.26 ± 5.57 years12038/82The rs2651899 G allele was associated with migraine, MO, and MA. The TRPM8 rs10166942 and the LPR1 rs11172113 were associated with migraine and MO.
PRDM16, PR/SET Domain 16; TRPM8, Transient Receptor Potential Cation Channel Subfamily M Member 8; LRP1, LDL receptor-related protein 1; MA, migraine with aura; MO, migraine without aura; CH, cluster headache.

3.2. Tests of Heterogeneity, Effect Size, and Publication Bias

3.2.1. PRDM16 rs2651899

Overall Migraine Group

A significant association was revealed between the PRDM16 rs2651899 and the overall migraine group for the CC genotype (RE model OR: 1.32; 95% CI: 1.02–1.73; p-value = 0.04). The forest plots can be accessed in Figure 1. Analysis for publication bias suggested that Zafar et al. [41] overestimated the risk conferring effect of the CT model and oversized the protective impact of the TT model (Supplementary File S6).

MA Group

A significant association revealed between the PRDM16 rs2651899 and the MA subgroup group for the CC genotype (FE model OR: 1.40; 95% CI: 1.12–1.74; p-value = 0.003) and a marginal trend for a protective effect of the AA genotype (FE model OR: 0.81; 95% CI: 0.66–1.00; p-value = 0.05). The forest plots can be accessed in Figure 2. Funnel plots were not indicative for publication bias (Supplementary File S7).

MO Group

No association was revealed for the PRDM16 rs2651899 and the MO subgroup. The forest plots can be accessed in Figure 3. Analysis for publication bias suggested that smaller studies tended to exaggerate the risk conferring association of the CT model (OR probably lies closer to 1.00 than estimated) (Supplementary File S8).

3.2.2. rs10166942 near TRPM8 Gene

Overall Migraine Group

A significant association for a protective effect was revealed between the rs10166942 and the overall migraine group for the CC genotype (FE model OR: 0.75; 95% CI: 0.62–0.91; p-value = 0.003) and for the TT genotype (FE model OR: 0.84; 95% CI: 0.71–0.99; p-value = 0.03). On the contrary, the heterozygosity CT was associated with increased migraine risk (FE model OR: 1.36; 95% CI: 1.18–1.57; p-value < 0.0001). The forest plots can be accessed in Figure 4. Analysis for publication bias (Supplementary File S9) suggested that smaller, less precise articles appeared to overestimate the risk conferring association of the CT model (the true OR is probably closer to 1.00, i.e., relatively mitigated), as well as the protective effect of the TT model (the true OR may be even equal to 1.00, i.e., no true effect). Publication bias was not apparent with respect to the CC model.

MA Group

No association was detected between the rs10166942 and the MA subgroup. The forest plots can be accessed in Figure 5. The funnel plots are not indicative of a clear direction for a biased publication trend (Supplementary File S10).

MO Group

A significant association, with a protective effect was revealed between the rs10166942 and the overall migraine group for the CC genotype (FE model OR: 0.78; 95% CI: 0.64–0.96; p-value = 0.02) while the heterozygosity CT was associated with increased MO risk (FE model OR: 1.41; 95% CI: 1.17–1.69; p-value = 0.0003). A marginal protective effect against MO was found for the TT genotype (FE model OR: 0.80; 95% CI: 0.63–1.01; p-value = 0.06). The forest plots can be accessed in Figure 6. Analysis for publication bias (Supplementary File S11) suggested that the smallest, least precise article seemed to mildly downsize the protective effect of the CC model and exaggerate the risk conferring effect of the CT model, as well as the protective effect of the TT model. Therefore, real associations are probably relatively stronger for the CC model and mitigated for the CT and TT models.

3.2.3. LPR1 rs11172113

Overall Migraine Group

Only a marginal trend for association was revealed between the LPR1 rs11172113 and migraine for the CT genotype (FE model OR: 0.86; 95% CI: 0.74–1.00; p-value = 0.05). The forest plots can be accessed in Figure 7. Analysis for publication bias (Supplementary File S12) suggested that smaller, less precise studies tended to overestimate the true effect (OR) of the CC model, which was probably closer to 1.00 than estimated (i.e., no association), and tended to mildly underestimate the true effect of the recessive model TT. The CT model appears to be less (if at all) affected by publication bias.

MA Group

No association was detected between the LPR1 rs11172113 and MA subgroup. The forest plots can be accessed in Figure 8. The funnel plots are not indicative of a clear direction for a biased publication trend (Supplementary File S13).

MO Group

No association was detected between the LPR1 rs11172113 and MO subgroup. The forest plots can be accessed in Figure 9. Analysis for publication bias (Supplementary File S14) suggested that smaller, less precise studies tended to overestimate the true effect (OR) of the CC model, which was probably closer to 1.00 than estimated (i.e., no association), and mildly underestimated the true effect of the recessive model TT. The CT model appears to be less (if at all) affected by publication bias.

4. Discussion

In this meta-analysis, we investigated the effect of three variants (namely the PRDM16 rs2651899, the rs10166942 near the TRPM8 gene, and the LPR1 rs11172113) on the risk of migraine, as well as on the risk of the main migraine phenotypes, namely the MA and the MO. Our study detected a significant influence of the PRDM16 rs2651899 on the risk of overall migraine and MA. Moreover, we detected a significant association between the rs10166942 (near the TRPM8 gene) CT genotype and increased migraine risk and MO risk, while the homozygosities appear to confer a protective effect. Finally, we did not detect any association between the LRP1 rs11172113 and any of the migraine phenotypes.
The PRDM16 gene encodes a zinc finger transcription factor, which contains an N-terminal PR domain [59,60]. The precise mechanism by which PRDM16 may be involved in migraine remains unknown. There are indications that the PRDM16 may be implicated in a molecular mechanism related to brown extra fat cells and preadipocytes, and as such, it may possibly be related to obesity [61]. This is of great interest, considering that obesity (total body and abdominal) has been associated with an increased prevalence of migraine and frequency of migraine attacks [62]. Moreover, the PRDM16 is implicated in oxidative stress and neurogenesis [63]. Such mechanisms have also been implicated in migraine pathogenesis [64,65,66].
The PRDM16 rs2651899 polymorphism is an intronic variant located at chromosome 1:3167148. This variant polymorphism may alter PRDM16 gene splicing or may have an effect on downstream regulatory elements, influencing the expression of PRDM16 mRNA [37]. The variety in the direction of the association between migraine and the rs2651899 (meaning that both the alleles have been reported to be associated with the migraine risk), denotes that the possible biological consequences of the rs2651899 on the PRDM16 polymorphism are far from being fully elucidated. This could possibly explain the fact that we did not detect any association between the PRDM16 rs2651899 and MO, as in a previous meta-analysis [37].
TRPM8 proteins are cold-sensitive channels responding to a great variety of ligands [67]. They are primarily expressed on peripheral sensory neurons and also on sensory afferents of the meninges [68,69]. Exposure to cold temperatures is a known trigger of migraine attacks [70]. While it is not clear whether meningeal TRPM8 protein are sensitive to weather fluctuations [70], it has been observed that activation of meningeal TRPM8 can alter the feeling of pain [67]. TRPM8 has also drawn attention as it is considered as a possible therapeutic target for migraine, neuropathic pain, and non-headache disorders [67,71,72,73].
The rs10166942 is an upstream gene variant located at Chromosome 2:233916448, near the TRPM8 gene. Interestingly, carriers of the rs10166942 C allele appeared to have decreased TRPM8 expression and reduced sensitivity to cold stimuli [74]. Moreover, carriers of the rs10166942 T allele presented more allodynic symptoms compared with the non-T allele carriers [53].
The third examined polymorphism in our study is the intronic rs11172113 located at Chromosome 12:57133500 of the LRP1 gene. The LRP1 gene is expressed in the brain, vasculature and, many other human tissues [75]. It is implicated in synaptic transmission, neuronal calcium signaling, amyloid precursor protein metabolism, and neuronal and glutamate signaling. [75]. Considering that elevated interictal glutamate levels have been found in the visual cortex of patients with MA, cortical hyperexcitability may be among the pathophysiological mechanisms that connect LRP1 with migraine [76,77,78]. Notably, our study did not detect any association between migraine and LRP1 rs11172113.
The fact that none of the examined polymorphisms has been associated with both MA and MO subgroups could be attributed to several reasons. Firstly, despite the similarities in genetic architecture between MA and MO, a few differences also exist [23]. Moreover, patients with MA were fewer than participants with MO, and obviously both the MA and MO datasets were smaller compared to the overall migraine group, suggesting that the analysis of the subgroup may lack the statistical power needed to detect a possible association with the tested variants.
Migraine causes severe impairment, influencing the quality of life, and the patients are unable to be productive in their daily activities [79,80]; consequently, migraine has a considerable economic impact on societies [81]. In an attempt to offer effective and personalized treatment, genetic studies are paving the way towards “precision medicine” targeted healthcare strategies, that take into account an individual’s genetic make-up and other environmental factors, to offer the optimal therapeutic and preventive options for each case. However, whether or not the variants meta-analyzed in our study could eventually have an impact on the diagnosis, prognosis, and treatment response remains elusive, highlighting the necessity for research on migraine, given the high prevalence in patients who suffer from this disease [14,82,83].

5. Conclusions

In conclusion, based on our findings the PRDM16 rs2651899 is associated with migraine and MA, and the rs10166942 (near the TRPM8 gene) CT genotype is associated with increased migraine risk and MO risk, while the homozygosities appear to confer a protective effect. Additionally, we did not detect any association between the LRP1 rs11172113 and any of the migraine phenotypes. In any case, considering that therapeutic approaches for migraine are often ineffective, it will be interesting to observe whether a personalized treatment based on the genetic architecture of each individual could be applied in the future. Future larger collaborative studies are needed, in cohorts with multiethnic backgrounds, for the role of these variants in migraine to be more accurately elucidated.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/medicina58040491/s1, Supplementary File S1. PRISMA 2009 checklist; Supplementary File S2. The PubMed search algorithm; Supplementary File S3. The flowchart presenting the selection procedure of eligible studies for the rs2651899; Supplementary File S4. The flowchart presenting the selection procedure of eligible studies for the rs10166942; Supplementary File S5. The flowchart presenting the selection procedure of eligible studies for the rs11172113; Supplementary File S6. The funnel plots presenting the results from meta-analysis of the rs2651899 and overall migraine group; Supplementary File S7. The funnel plots presenting the results from meta-analysis of the rs2651899 and migraine with aura group; Supplementary File S8. The funnel plots presenting the results from meta-analysis of the rs2651899 and migraine without aura group; Supplementary File S9. The funnel plots presenting the results from meta-analysis of the rs10166942 and overall migraine group; Supplementary File S10.The funnel plots presenting the results from meta-analysis of the rs10166942 and migraine with aura group; Supplementary File S11. The funnel plots presenting the results from meta-analysis of the rs10166942 and migraine without aura group; Supplementary File S12. The funnel plots presenting the results from meta-analysis of the rs11172113 and overall migraine group; Supplementary File S13. The funnel plots presenting the results from meta-analysis of the rs11172113 and migraine with aura group; Supplementary File S14. The funnel plots presenting the results from meta-analysis of the rs11172113 and migraine without aura group.

Author Contributions

Conceptualization, V.S. and E.D.; methodology, V.S., I.L. and A.-M.A.; software, V.S. and I.L.; validation, V.S., I.L., A.-M.A., M.P. and C.B.; formal analysis, V.S. and I.L.; investigation, V.S., I.L., A.-M.A., M.P., C.B., E.L., P.L., N.D., D.P.B. and E.D.; writing—original draft preparation, V.S., I.L. and A.-M.A.; writing—review and editing, V.S., I.L., A.-M.A., M.P., C.B., E.L., P.L., N.D., D.P.B. and E.D.; supervision, E.D.; project administration, E.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Eigenbrodt, A.K.; Ashina, H.; Khan, S.; Diener, H.-C.; Mitsikostas, D.D.; Sinclair, A.J.; Pozo-Rosich, P.; Martelletti, P.; Ducros, A.; Lantéri-Minet, M.; et al. Diagnosis and management of migraine in ten steps. Nat. Rev. Neurol. 2021, 17, 501–514. [Google Scholar] [CrossRef] [PubMed]
  2. Agosti, R. Migraine Burden of Disease: From the Patient’s Experience to a Socio-Economic View. Headache 2018, 58 (Suppl. 1), 17–32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Feigin, V.L.; Nichols, E.; Alam, T.; Bannick, M.S.; Beghi, E.; Blake, N.; Culpepper, E.; Dorsey, R.; Elbaz, A.; Richard, G.; et al. Global, regional, and national burden of neurological disorders, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019, 18, 459–480. [Google Scholar] [CrossRef] [Green Version]
  4. Robbins, M.S. Diagnosis and Management of Headache: A Review. JAMA 2021, 325, 1874–1885. [Google Scholar] [CrossRef] [PubMed]
  5. Ashina, M. Migraine. N. Engl. J. Med. 2020, 383, 1866–1876. [Google Scholar] [CrossRef] [PubMed]
  6. The International Classification of Headache Disorders, 3rd edition (beta version). Cephalalgia 2013, 33, 629–808. [CrossRef] [Green Version]
  7. Akerman, S.; Holland, P.R.; Hoffmann, J. Pearls and pitfalls in experimental in vivo models of migraine: Dural trigeminovascular nociception. Cephalalgia 2013, 33, 577–592. [Google Scholar] [CrossRef]
  8. Gasparini, C.F.; Sutherland, H.G.; Griffiths, L.R. Studies on the pathophysiology and genetic basis of migraine. Curr. Genom. 2013, 14, 300–315. [Google Scholar] [CrossRef]
  9. Kassab, M.; Bakhtar, O.; Wack, D.; Bednarczyk, E. Resting brain glucose uptake in headache-free migraineurs. Headache 2009, 49, 90–97. [Google Scholar] [CrossRef]
  10. Grech, O.; Mollan, S.P.; Wakerley, B.R.; Fulton, D.; Lavery, G.G.; Sinclair, A.J. The Role of Metabolism in Migraine Pathophysiology and Susceptibility. Life 2021, 11, 415. [Google Scholar] [CrossRef]
  11. Hoffmann, U.; Sukhotinsky, I.; Eikermann-Haerter, K.; Ayata, C. Glucose modulation of spreading depression susceptibility. J. Cereb. Blood Flow Metab. 2013, 33, 191–195. [Google Scholar] [CrossRef] [PubMed]
  12. Yamanaka, G.; Suzuki, S.; Morishita, N.; Takeshita, M.; Kanou, K.; Takamatsu, T.; Suzuki, S.; Morichi, S.; Watanabe, Y.; Ishida, Y.; et al. Role of Neuroinflammation and Blood-Brain Barrier Permutability on Migraine. Int. J. Mol. Sci. 2021, 22, 8929. [Google Scholar] [CrossRef] [PubMed]
  13. Fila, M.; Chojnacki, C.; Chojnacki, J.; Blasiak, J. Is an “Epigenetic Diet” for Migraines Justified? The Case of Folate and DNA Methylation. Nutrients 2019, 11, 2763. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Koute, V.; Michalopoulou, A.; Siokas, V.; Aloizou, A.M.; Rikos, D.; Bogdanos, D.P.; Kontopoulos, E.; Grivea, I.N.; Syrogiannopoulos, G.A.; Papadimitriou, A.; et al. Val66Met polymorphism is associated with decreased likelihood for pediatric headache and migraine. Neurol. Res. 2021, 43, 715–723. [Google Scholar] [CrossRef]
  15. Amin, F.M.; Aristeidou, S.; Baraldi, C.; Czapinska-Ciepiela, E.K.; Ariadni, D.D.; Di Lenola, D.; Fenech, C.; Kampouris, K.; Karagiorgis, G.; Braschinsky, M.; et al. The association between migraine and physical exercise. J. Headache Pain 2018, 19, 83. [Google Scholar] [CrossRef]
  16. López-Mesonero, L.; Márquez, S.; Parra, P.; Gámez-Leyva, G.; Muñoz, P.; Pascual, J. Smoking as a precipitating factor for migraine: A survey in medical students. J. Headache Pain 2009, 10, 101–103. [Google Scholar] [CrossRef] [Green Version]
  17. Huang, Q.; Liang, X.; Wang, S.; Mu, X. Association between Body Mass Index and Migraine: A Survey of Adult Population in China. Behav. Neurol. 2018, 2018, 6585734. [Google Scholar] [CrossRef] [Green Version]
  18. Stewart, W.F.; Roy, J.; Lipton, R.B. Migraine prevalence, socioeconomic status, and social causation. Neurology 2013, 81, 948–955. [Google Scholar] [CrossRef] [Green Version]
  19. Liampas, I.; Siokas, V.; Brotis, A.; Dardiotis, E. Vitamin D serum levels in patients with migraine: A meta-analysis. Rev. Neurol. (Paris) 2020, 176, 560–570. [Google Scholar] [CrossRef]
  20. Liampas, I.N.; Siokas, V.; Aloizou, A.M.; Tsouris, Z.; Dastamani, M.; Aslanidou, P.; Brotis, A.; Dardiotis, E. Pyridoxine, folate and cobalamin for migraine: A systematic review. Acta Neurol. Scand. 2020, 142, 108–120. [Google Scholar] [CrossRef]
  21. Liampas, I.; Siokas, V.; Mentis, A.A.; Aloizou, A.M.; Dastamani, M.; Tsouris, Z.; Aslanidou, P.; Brotis, A.; Dardiotis, E. Serum Homocysteine, Pyridoxine, Folate, and Vitamin B12 Levels in Migraine: Systematic Review and Meta-Analysis. Headache 2020, 60, 1508–1534. [Google Scholar] [CrossRef] [PubMed]
  22. Sutherland, H.G.; Albury, C.L.; Griffiths, L.R. Advances in genetics of migraine. J. Headache Pain 2019, 20, 72. [Google Scholar] [CrossRef] [PubMed]
  23. de Boer, I.; Terwindt, G.M.; van den Maagdenberg, A.M.J.M. Genetics of migraine aura: An update. J. Headache Pain 2020, 21, 64. [Google Scholar] [CrossRef] [PubMed]
  24. Mikol, D.D.; Picard, H.; Klatt, J.; Wang, A.; Peng, C.; Stefansson, K. Migraine Polygenic Risk Score Is Associated with Severity of Migraine—Analysis of Genotypic Data from Four Placebo-controlled Trials of Erenumab (1214). Neurology 2020, 94, 1214. [Google Scholar]
  25. May, A.; Schulte, L.H. Chronic migraine: Risk factors, mechanisms and treatment. Nat. Rev. Neurol. 2016, 12, 455–464. [Google Scholar] [CrossRef]
  26. Di Stefano, V.; Rispoli, M.G.; Pellegrino, N.; Graziosi, A.; Rotondo, E.; Napoli, C.; Pietrobon, D.; Brighina, F.; Parisi, P. Diagnostic and therapeutic aspects of hemiplegic migraine. J. Neurol. Neurosurg. Psychiatry 2020, 91, 764–771. [Google Scholar] [CrossRef]
  27. Bron, C.; Sutherland, H.G.; Griffiths, L.R. Exploring the Hereditary Nature of Migraine. Neuropsychiatr. Dis. Treat. 2021, 17, 1183–1194. [Google Scholar] [CrossRef]
  28. van den Maagdenberg, A.; Nyholt, D.R.; Anttila, V. Novel hypotheses emerging from GWAS in migraine? J. Headache Pain 2019, 20, 5. [Google Scholar] [CrossRef] [Green Version]
  29. Anttila, V.; Winsvold, B.S.; Gormley, P.; Kurth, T.; Bettella, F.; McMahon, G.; Kallela, M.; Malik, R.; de Vries, B.; Terwindt, G.; et al. Genome-wide meta-analysis identifies new susceptibility loci for migraine. Nat. Genet. 2013, 45, 912–917. [Google Scholar] [CrossRef]
  30. Gormley, P.; Anttila, V.; Winsvold, B.S.; Palta, P.; Esko, T.; Pers, T.H.; Farh, K.H.; Cuenca-Leon, E.; Muona, M.; Furlotte, N.A.; et al. Meta-analysis of 375,000 individuals identifies 38 susceptibility loci for migraine. Nat. Genet. 2016, 48, 856–866. [Google Scholar] [CrossRef] [Green Version]
  31. Rai, V.; Kumar, P. Relation Between Methylenetetrahydrofolate Reductase Polymorphisms (C677T and A1298C) and Migraine Susceptibility. Indian J. Clin. Biochem. 2022, 37, 3–17. [Google Scholar] [CrossRef] [PubMed]
  32. Terrazzino, S.; Cargnin, S.; Viana, M.; Sances, G.; Tassorelli, C. Brain-Derived Neurotrophic Factor Val66Met Gene Polymorphism Impacts on Migraine Susceptibility: A Meta-analysis of Case-Control Studies. Front. Neurol. 2017, 8, 159. [Google Scholar] [CrossRef] [PubMed]
  33. Papasavva, M.; Vikelis, M.; Katsarou, M.S.; Siokas, V.; Dermitzakis, E.; Papademetriou, C.; Karakostis, K.; Lazopoulos, G.; Dardiotis, E.; Drakoulis, N. Evidence That HFE H63D Variant Is a Potential Disease Modifier in Cluster Headache. J. Mol. Neurosci. 2021, 72, 393–400. [Google Scholar] [CrossRef] [PubMed]
  34. Papasavva, M.; Vikelis, M.; Siokas, V.; Katsarou, M.S.; Dermitzakis, E.; Raptis, A.; Dardiotis, E.; Drakoulis, N. VDR Gene Polymorphisms and Cluster Headache Susceptibility: Case-Control Study in a Southeastern European Caucasian Population. J. Mol. Neurosci. 2021, 72, 382–392. [Google Scholar] [CrossRef]
  35. Chasman, D.I.; Schürks, M.; Anttila, V.; de Vries, B.; Schminke, U.; Launer, L.J.; Terwindt, G.M.; van den Maagdenberg, A.M.J.M.; Fendrich, K.; Völzke, H.; et al. Genome-wide association study reveals three susceptibility loci for common migraine in the general population. Nat. Genet. 2011, 43, 695–698. [Google Scholar] [CrossRef] [Green Version]
  36. Freilinger, T.; Anttila, V.; de Vries, B.; Malik, R.; Kallela, M.; Terwindt, G.M.; Pozo-Rosich, P.; Winsvold, B.; Nyholt, D.R.; van Oosterhout, W.P.; et al. Genome-wide association analysis identifies susceptibility loci for migraine without aura. Nat. Genet. 2012, 44, 777–782. [Google Scholar] [CrossRef]
  37. Lee, H.H.; Chen, C.C.; Ong, J.R.; Lin, Y.F.; Lee, F.P.; Hu, C.J.; Wang, Y.H. Association of rs2651899 Polymorphism in the Positive Regulatory Domain 16 and Common Migraine Subtypes: A Meta-Analysis. Headache 2020, 60, 71–80. [Google Scholar] [CrossRef]
  38. Fu, X.; Yang, J.; Wu, X.; Lin, Q.; Zeng, Y.; Xia, Q.; Cao, L.; Huang, B.; Huang, G. Association between PRDM16, MEF2D, TRPM8, LRP1 gene polymorphisms and migraine susceptibility in the She ethnic population in China. Clin. Invest. Med. 2019, 42, E21–E30. [Google Scholar] [CrossRef] [Green Version]
  39. Kaur, S.; Ali, A.; Ahmad, U.; Pandey, A.K.; Singh, B. rs2651899 variant is associated with risk for migraine without aura from North Indian population. Mol. Biol. Rep. 2019, 46, 1247–1255. [Google Scholar] [CrossRef]
  40. Sintas, C.; Fernández-Morales, J.; Vila-Pueyo, M.; Narberhaus, B.; Arenas, C.; Pozo-Rosich, P.; Macaya, A.; Cormand, B. Replication study of previous migraine genome-wide association study findings in a Spanish sample of migraine with aura. Cephalalgia 2015, 35, 776–782. [Google Scholar] [CrossRef]
  41. Zafar, R.; Saleem, T.; Sheikh, N.; Maqbool, H.; Mukhtar, M.; Abbasi, M.H. PRDM16, LRP1 and TRPM8 genetic polymorphisms are risk factor for Pakistani migraine patients. Saudi J. Biol. Sci. 2021, 28, 5793–5799. [Google Scholar] [CrossRef] [PubMed]
  42. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 2009, 339, b2700. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef]
  44. Mantel, N.; Haenszel, W. Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl. Cancer Inst. 1959, 22, 719–748. [Google Scholar] [PubMed]
  45. Ran, C.; Graae, L.; Magnusson, P.K.; Pedersen, N.L.; Olson, L.; Belin, A.C. A replication study of GWAS findings in migraine identifies association in a Swedish case-control sample. BMC Med. Genet. 2014, 15, 38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Esserlind, A.L.; Christensen, A.F.; Le, H.; Kirchmann, M.; Hauge, A.W.; Toyserkani, N.M.; Hansen, T.; Grarup, N.; Werge, T.; Steinberg, S.; et al. Replication and meta-analysis of common variants identifies a genome-wide significant locus in migraine. Eur. J. Neurol. 2013, 20, 765–772. [Google Scholar] [CrossRef] [PubMed]
  47. An, X.K.; Fang, J.; Yu, Z.Z.; Lin, Q.; Lu, C.X.; Qu, H.L.; Ma, Q.L. Multilocus analysis reveals three candidate genes for Chinese migraine susceptibility. Clin. Genet. 2017, 92, 143–149. [Google Scholar] [CrossRef]
  48. An, X.K.; Ma, Q.L.; Lin, Q.; Zhang, X.R.; Lu, C.X.; Qu, H.L. PRDM16 rs2651899 variant is a risk factor for Chinese common migraine patients. Headache 2013, 53, 1595–1601. [Google Scholar] [CrossRef]
  49. Ran, C.; Fourier, C.; Zinnegger, M.; Steinberg, A.; Sjöstrand, C.; Waldenlind, E.; Belin, A.C. Implications for the migraine SNP rs1835740 in a Swedish cluster headache population. J. Headache Pain 2018, 19, 100. [Google Scholar] [CrossRef]
  50. Ghosh, J.; Pradhan, S.; Mittal, B. Multilocus analysis of hormonal, neurotransmitter, inflammatory pathways and genome-wide associated variants in migraine susceptibility. Eur. J. Neurol. 2014, 21, 1011–1020. [Google Scholar] [CrossRef]
  51. Fan, X.; Wang, J.; Fan, W.; Chen, L.; Gui, B.; Tan, G.; Zhou, J. Replication of migraine GWAS susceptibility loci in Chinese Han population. Headache 2014, 54, 709–715. [Google Scholar] [CrossRef] [PubMed]
  52. Chen, S.P.; Fuh, J.L.; Chung, M.Y.; Lin, Y.C.; Liao, Y.C.; Wang, Y.F.; Hsu, C.L.; Yang, U.C.; Lin, M.W.; Chiou, J.J.; et al. Genome-wide association study identifies novel susceptibility loci for migraine in Han Chinese resided in Taiwan. Cephalalgia 2018, 38, 466–475. [Google Scholar] [CrossRef] [PubMed]
  53. Ling, Y.H.; Chen, S.P.; Fann, C.S.; Wang, S.J.; Wang, Y.F. TRPM8 genetic variant is associated with chronic migraine and allodynia. J. Headache Pain 2019, 20, 115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Chasman, D.I.; Anttila, V.; Buring, J.E.; Ridker, P.M.; Schürks, M.; Kurth, T. Selectivity in genetic association with sub-classified migraine in women. PLoS Genet. 2014, 10, e1004366. [Google Scholar] [CrossRef] [Green Version]
  55. Meng, W.; Adams, M.J.; Hebert, H.L.; Deary, I.J.; McIntosh, A.M.; Smith, B.H. A Genome-Wide Association Study Finds Genetic Associations with Broadly-Defined Headache in UK Biobank (N = 223,773). EBioMedicine 2018, 28, 180–186. [Google Scholar] [CrossRef] [Green Version]
  56. Daghals, I.; Sargurupremraj, M.; Danning, R.; Gormley, P.; Malik, R.; Amouyel, P.; Metso, T.; Pezzini, A.; Kurth, T.; Debette, S.; et al. Migraine, Stroke, and Cervical Arterial Dissection: Shared Genetics for a Triad of Brain Disorders With Vascular Involvement. Neurol. Genet. 2022, 8, e653. [Google Scholar] [CrossRef]
  57. Shoba, U.S.; Srinivasan, G.; Gundlapally, J.; Kuppamuthu, K. Association of Single Nucleotide Polymorphism rs11172113 of LRP1 Gene with Migraine in South Indian Population–A Study. Helix-Sci. Explor. Peer Rev. Bimon. Int. J. 2020, 10, 7–11. [Google Scholar]
  58. Kaur, S.; Ali, A.; Siahbalaei, Y.; Ahmad, U.; Pandey, A.K.; Singh, B. Could rs4379368 be a genetic marker for North Indian migraine patients with aura?: Preliminary evidence by a replication study. Neurosci. Lett. 2019, 712, 134482. [Google Scholar] [CrossRef]
  59. Casamassimi, A.; Rienzo, M.; Di Zazzo, E.; Sorrentino, A.; Fiore, D.; Proto, M.C.; Moncharmont, B.; Gazzerro, P.; Bifulco, M.; Abbondanza, C. Multifaceted Role of PRDM Proteins in Human Cancer. Int. J. Mol. Sci. 2020, 21, 2648. [Google Scholar] [CrossRef] [Green Version]
  60. Di Zazzo, E.; De Rosa, C.; Abbondanza, C.; Moncharmont, B. PRDM Proteins: Molecular Mechanisms in Signal Transduction and Transcriptional Regulation. Biology 2013, 2, 107–141. [Google Scholar] [CrossRef] [Green Version]
  61. Seale, P.; Kajimura, S.; Yang, W.; Chin, S.; Rohas, L.M.; Uldry, M.; Tavernier, G.; Langin, D.; Spiegelman, B.M. Transcriptional control of brown fat determination by PRDM16. Cell Metab. 2007, 6, 38–54. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Kristoffersen, E.S.; Børte, S.; Hagen, K.; Zwart, J.A.; Winsvold, B.S. Migraine, obesity and body fat distribution—A population-based study. J. Headache Pain 2020, 21, 97. [Google Scholar] [CrossRef] [PubMed]
  63. Shimada, I.S.; Acar, M.; Burgess, R.J.; Zhao, Z.; Morrison, S.J. Prdm16 is required for the maintenance of neural stem cells in the postnatal forebrain and their differentiation into ependymal cells. Genes Dev. 2017, 31, 1134–1146. [Google Scholar] [CrossRef]
  64. Geyik, S.; Altunısık, E.; Neyal, A.M.; Taysi, S. Oxidative stress and DNA damage in patients with migraine. J. Headache Pain 2016, 17, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Gross, E.C.; Putananickal, N.; Orsini, A.-L.; Vogt, D.R.; Sandor, P.S.; Schoenen, J.; Fischer, D. Mitochondrial function and oxidative stress markers in higher-frequency episodic migraine. Sci. Rep. 2021, 11, 4543. [Google Scholar] [CrossRef] [PubMed]
  66. Hoffmann, J.; Baca, S.M.; Akerman, S. Neurovascular mechanisms of migraine and cluster headache. J. Cereb. Blood Flow Metab. 2019, 39, 573–594. [Google Scholar] [CrossRef] [PubMed]
  67. Dussor, G.; Cao, Y.Q. TRPM8 and Migraine. Headache 2016, 56, 1406–1417. [Google Scholar] [CrossRef] [Green Version]
  68. Silverman, H.A.; Chen, A.; Kravatz, N.L.; Chavan, S.S.; Chang, E.H. Involvement of Neural Transient Receptor Potential Channels in Peripheral Inflammation. Front. Immunol. 2020, 11, 2742. [Google Scholar] [CrossRef]
  69. Burgos-Vega, C.C.; Ahn, D.D.; Bischoff, C.; Wang, W.; Horne, D.; Wang, J.; Gavva, N.; Dussor, G. Meningeal transient receptor potential channel M8 activation causes cutaneous facial and hindpaw allodynia in a preclinical rodent model of headache. Cephalalgia 2016, 36, 185–193. [Google Scholar] [CrossRef]
  70. Prince, P.B.; Rapoport, A.M.; Sheftell, F.D.; Tepper, S.J.; Bigal, M.E. The effect of weather on headache. Headache 2004, 44, 596–602. [Google Scholar] [CrossRef]
  71. González-Muñiz, R.; Bonache, M.A.; Martín-Escura, C.; Gómez-Monterrey, I. Recent Progress in TRPM8 Modulation: An Update. Int. J. Mol. Sci. 2019, 20, 2618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Knowlton, W.M.; Daniels, R.L.; Palkar, R.; McCoy, D.D.; McKemy, D.D. Pharmacological blockade of TRPM8 ion channels alters cold and cold pain responses in mice. PLoS ONE 2011, 6, e25894. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Weyer, A.D.; Lehto, S.G. Development of TRPM8 Antagonists to Treat Chronic Pain and Migraine. Pharmaceuticals 2017, 10, 37. [Google Scholar] [CrossRef] [PubMed]
  74. Gavva, N.R.; Sandrock, R.; Arnold, G.E.; Davis, M.; Lamas, E.; Lindvay, C.; Li, C.-M.; Smith, B.; Backonja, M.; Gabriel, K.; et al. Reduced TRPM8 expression underpins reduced migraine risk and attenuated cold pain sensation in humans. Sci. Rep. 2019, 9, 19655. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Actis Dato, V.; Chiabrando, G.A. The Role of Low-Density Lipoprotein Receptor-Related Protein 1 in Lipid Metabolism, Glucose Homeostasis and Inflammation. Int. J. Mol. Sci. 2018, 19, 1780. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Ferrari, M.D.; Odink, J.; Bos, K.D.; Malessy, M.J.; Bruyn, G.W. Neuroexcitatory plasma amino acids are elevated in migraine. Neurology 1990, 40, 1582–1586. [Google Scholar] [CrossRef]
  77. Harder, A.V.E.; Vijfhuizen, L.S.; Henneman, P.; Willems van Dijk, K.; van Duijn, C.M.; Terwindt, G.M.; van den Maagdenberg, A. Metabolic profile changes in serum of migraine patients detected using (1)H-NMR spectroscopy. J. Headache Pain 2021, 22, 142. [Google Scholar] [CrossRef]
  78. Haigh, S.; Karanovic, O.; Wilkinson, F.; Wilkins, A. Cortical hyperexcitability in migraine and aversion to patterns. Cephalalgia Int. J. Headache 2012, 32, 236–240. [Google Scholar] [CrossRef] [Green Version]
  79. Salazar, A.; Berrocal, L.; Failde, I. Prevalence of Migraine in General Spanish Population; Factors Related and Use of Health Resources. Int. J. Environ. Res. Public Health 2021, 18, 1145. [Google Scholar] [CrossRef]
  80. Fuh, J.L.; Wang, S.J.; Lu, S.R.; Liao, Y.C.; Chen, S.P.; Yang, C.Y. Headache disability among adolescents: A student population-based study. Headache 2010, 50, 210–218. [Google Scholar] [CrossRef]
  81. Yucel, A.; Thach, A.; Kumar, S.; Loden, C.; Bensink, M.; Goldfarb, N. Estimating the economic burden of migraine on US employers. Am. J. Manag. Care 2020, 26, e403–e408. [Google Scholar] [CrossRef] [PubMed]
  82. Abu-Arafeh, I.; Razak, S.; Sivaraman, B.; Graham, C. Prevalence of headache and migraine in children and adolescents: A systematic review of population-based studies. Dev. Med. Child. Neurol. 2010, 52, 1088–1097. [Google Scholar] [CrossRef] [PubMed]
  83. Wöber-Bingöl, C. Epidemiology of migraine and headache in children and adolescents. Curr. Pain Headache Rep. 2013, 17, 341. [Google Scholar] [CrossRef] [PubMed]
Medicina 58 00491 g001 550
Figure 1. The forest plots presenting the results from meta-analysis of the rs2651899 and overall migraine group.
Figure 1. The forest plots presenting the results from meta-analysis of the rs2651899 and overall migraine group.
Medicina 58 00491 g001
Medicina 58 00491 g002 550
Figure 2. The forest plots presenting the results from meta-analysis of the rs2651899 and migraine with aura group.
Figure 2. The forest plots presenting the results from meta-analysis of the rs2651899 and migraine with aura group.
Medicina 58 00491 g002
Medicina 58 00491 g003 550
Figure 3. The forest plots presenting the results from meta-analysis of the rs2651899 and migraine without aura group.
Figure 3. The forest plots presenting the results from meta-analysis of the rs2651899 and migraine without aura group.
Medicina 58 00491 g003
Medicina 58 00491 g004 550
Figure 4. The forest plots presenting the results from meta-analysis of the rs10166942 and overall migraine group.
Figure 4. The forest plots presenting the results from meta-analysis of the rs10166942 and overall migraine group.
Medicina 58 00491 g004
Medicina 58 00491 g005 550
Figure 5. The forest plots presenting the results from meta-analysis of the rs10166942 and migraine with aura group.
Figure 5. The forest plots presenting the results from meta-analysis of the rs10166942 and migraine with aura group.
Medicina 58 00491 g005
Medicina 58 00491 g006 550
Figure 6. The forest plots presenting the results from meta-analysis of the rs10166942 and migraine without aura group.
Figure 6. The forest plots presenting the results from meta-analysis of the rs10166942 and migraine without aura group.
Medicina 58 00491 g006
Medicina 58 00491 g007 550
Figure 7. The forest plots presenting the results from meta-analysis of the rs11172113 and overall migraine group.
Figure 7. The forest plots presenting the results from meta-analysis of the rs11172113 and overall migraine group.
Medicina 58 00491 g007
Medicina 58 00491 g008 550
Figure 8. The forest plots presenting the results from meta-analysis of the rs11172113 and migraine with aura group.
Figure 8. The forest plots presenting the results from meta-analysis of the rs11172113 and migraine with aura group.
Medicina 58 00491 g008
Medicina 58 00491 g009 550
Figure 9. The forest plots presenting the results from meta-analysis of the rs11172113 and migraine without aura group.
Figure 9. The forest plots presenting the results from meta-analysis of the rs11172113 and migraine without aura group.
Medicina 58 00491 g009
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Siokas, V.; Liampas, I.; Aloizou, A.-M.; Papasavva, M.; Bakirtzis, C.; Lavdas, E.; Liakos, P.; Drakoulis, N.; Bogdanos, D.P.; Dardiotis, E. Deciphering the Role of the rs2651899, rs10166942, and rs11172113 Polymorphisms in Migraine: A Meta-Analysis. Medicina 2022, 58, 491. https://doi.org/10.3390/medicina58040491

AMA Style

Siokas V, Liampas I, Aloizou A-M, Papasavva M, Bakirtzis C, Lavdas E, Liakos P, Drakoulis N, Bogdanos DP, Dardiotis E. Deciphering the Role of the rs2651899, rs10166942, and rs11172113 Polymorphisms in Migraine: A Meta-Analysis. Medicina. 2022; 58(4):491. https://doi.org/10.3390/medicina58040491

Chicago/Turabian Style

Siokas, Vasileios, Ioannis Liampas, Athina-Maria Aloizou, Maria Papasavva, Christos Bakirtzis, Eleftherios Lavdas, Panagiotis Liakos, Nikolaos Drakoulis, Dimitrios P. Bogdanos, and Efthimios Dardiotis. 2022. "Deciphering the Role of the rs2651899, rs10166942, and rs11172113 Polymorphisms in Migraine: A Meta-Analysis" Medicina 58, no. 4: 491. https://doi.org/10.3390/medicina58040491

Article Metrics

Back to TopTop