Candidate Genes/Proteomic Biomarkers of MOH: History
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Chronic headache is a topical problem of neurology, psychiatry and general practice. The medication-overuse headache (MOH) is one of the leading pathologies in the structure of chronic headache. The serum and urine proteomic biomarkers of MOH can potentially help with the identification of patients with MOH development. 

  • headache
  • chronic headache
  • proteomics
  • proteogenomics
  • serum biomarker
  • urine biomarker

1. Introduction

Chronic pain (CP) is an essential problem in healthcare. Approximately 20% of the European population is affected by CP [1], which has a significant influence on their daily social and working lives. Moreover, the economic impact of CP is more than heart disease, cancer and diabetes put together [2]. It is worth mentioning that among chronic painful disorders, headaches are predominant. According to world literature, with an incidence of 3% per year, 4–5% of the general population suffers from chronic headache, known as headache occurrence ≥15 days per month [3,4,5]. Chronic forms of headache, such as chronic migraine or chronic tension-type headache, often involve a high and daily intake of combination analgesics and acute headache medications (AHMs), such as nonsteroidal anti-inflammatory drugs (NSAIDs) and triptans [6]. Paradoxically, they only worsen the chronic symptoms, resulting in the development of the secondary headache, so-called medication-overuse headache (MOH) [7]. In the latest and current Third Edition of the International Classification of Headache Disorders (IHS ICHD-3), MOH, also known as a rebound headache, is described as a headache that is present on 15 or more days per month developing as a consequence of regular overuse of acute or symptomatic headache medication. Criterions of MOH: the overuse of simple analgesics on 15 or more days per month; or else the overuse of triptans, ergotamines, analgesics, opioids, and (caffeine or codeine-containing) combined analgesics on 10 or more days per month, for more than 3 consecutive months [8]. Although the prevalence of MOH in the general population is around 1–2% [9,10], MOH is still defined as a socio-economic burden worldwide, especially in lesser developed countries [11], associated with significant long-term morbidity, disability, and lower quality of life. According to systematic reviews of MOH epidemiology, it is most predominant in middle-aged women from 30 to 50 years old, with the male to female ratio around 1 to 3–4 [9,12,13,14,15]. The pathophysiological mechanisms of MOH development are still an ongoing debate. Nevertheless, frequent and regular consumption of acute headache medication does not seem enough to cause MOH, therefore individual predisposition and specifics of the medication class combined play a significant role in its development [16].
The proteomic analysis and the exploration of proteomic biomarkers are crucial in understanding a lot of medical conditions, especially cancer, cardiovascular and neurodegenerative diseases [17,18,19]. Thus, identification of chronic pain biomarkers can be helpful and valuable for clinicians in the diagnosis of patients at risk or with an already developed disorder, reducing the time and costs, selection of rational personalized pain treatment, and understanding of the underlying pathophysiological mechanisms of chronic pain development [20]. Moreover, a considerable number of patients with MOH can hide the truth from the doctor about the frequency and daily amount of acute headache medications they are consuming. Besides, the MOH phenotype is almost indistinguishable from other chronic headache phenotypes [11]. Therefore, it becomes even more challenging to diagnose MOH and selecting and monitoring the headache treatment. Furthermore, acute headache medication overuse leads to side effects, such as nephrotoxicity and kidney damage, gastrointestinal bleeding, liver impairment [11], especially in a group of patients who abuse NSAIDs [21]. It is not possible to prevent the development of renal impairment and acute renal failure caused by drug-induced nephrotoxicity by using traditional laboratory analyses, such as creatinine, creatinine clearance, urea, electrolytes, urine sediment [22]. However, urinary proteomics allows the potential risks of developing severe drug-induced kidney damage to be minimized by its detection in the early stages during a normal clinical presence, particularly in NSAIDs abusers, using a panel of protein biomarkers each informing on the integrated aspects of glomerular, tubular, and interstitial function [23].
The underlying mechanisms of chronic pain pathophysiology still remain poorly understood. Proteomics is one of the most promising areas that can significantly contribute to pain chronicity research, its better understanding and management.

2. Candidate Genes and Proteomic Biomarkers in Medication-Overuse Headache

2.1. Candidate Serum Proteomic Biomarkers of Patients with Medication-Overuse Headache

Candidate serum proteomic biomarkers of MOH are shown in Table 1.

Table 1. Candidate serum proteomic biomarkers of medication-overuse headache.
Protein Full Name Entry Name Gene Name Locus Protein Main Function Theor. Mass. References
Lipocalin-type prostaglandin D2 synthase L-PGDS PTGDS 9q34.3 Prostaglandin biosynthesis process 21,029 [23,30]
Apolipoprotein B100 APOB APOB 2p24.1 Cholesterol metabolism 516,651 [23,30]
Alpha-2-macroglobulin A2MG A2M 12p13.31 Enzyme binding 164,613 [23,30]
Complement factor H CFAH CFH 1q31.3 Complement activation 143,480 [23,30]
Complement C3 (fragm) CO3 C3 19p13.3 Complement activation 188,569 [23,30]
Hemopexin HEMO HPX 11p15.4 Metal ion binding 52,385 [23,30]
Serum albumin ALBU ALB 4q13.3 Metal binding 71,317 [23,30]
Alpha-1B-glycoprotein AIBG A1BG 19q13.43 Neutrophil, platelet degranulation 54,790 [23,30]
Complement component C9 CO9 C9 5p13.1 Complement activation 64,615 [23,30]
Kininogen-1 KNG1 KNG1 3q27.3 Cysteine-type endopeptidase inhibitor activity 72,996 [23,30]
Vitronectin VTNC VTN 17q11.2 Heparin binding 55,069 [23,30]
Haptoglobin HPT HP 16q22.2 Acute phase response 45,861 [23,30]
Apolipoprotein A-4 APOA4 APOA4 11q23.3 Lipid binding 45,371 [23,30]
Alpha-1-acid glycoprotein 1 A1AG1 ORM1 9q32 Inflammatory response 23,725 [23,30]
Serum paraoxonase/arylesterase 1 PON1 PON1 7q21.3 Hydrolase 39,877 [23,30]
Zinc-alpha-2-glycoprotein ZA2G AZGP1 7q22.1 Protein transmembrane transporter activity 34,465 [23,30]
Alpha-1-acid glycoprotein 2 A1AG2 ORM2 9q32 Acute phase response 23,873 [23,30]
Alpha-1-antitrypsin A1AT SERPINA1 14q32.13 Protease inhibitor 46,737 [23,30]
Immunoglobulin heavy constant alpha 1 IGHA1 IGHA1 14q32.33 Antigen binding 37,655 [23,30]
Retinol-binding protein RETBP RBP4 10q23.33 Retinol binding 23,010 [23,30]
Transthyretin TTHY TTR 18q12.1 Hormone binding 15,887 [23,30]
Apolipoprotein E APOE APOE 19q13.32 Lipid transport 36,154 [23,30]
Vitamin D-binding protein VDBP GC 4q13.3 Vitamin D transport 52,918 [23,30]
Protein entry name, according to the UniProtKB database. Theoretical molecular weight (Da).

2.2. Candidate Urine Proteomic Biomarkers of Patients with Medication-Overuse Headache

Candidate urine proteomic biomarkers of MOH are shown in Table 2.

Table 2. Candidate urine proteomic biomarkers of medication-overuse headache.
Protein Full Name Entry Name Gene Name Locus Protein Main Function Theor. Mass. References
Lipocalin-type prostaglandin D2 synthase L-PGDS PTGDS 9q34.3 Prostaglandin biosynthesis process 21,029 [23,30]
Uromodulin (or Tamm-Horsfall urinary glycoprotein) UROM UMOD 16p12.3 Cellular defense response 69,761 [21,23,30,33]
Alpha-1-microglobulin AMBP AMBP 9q32 Calcium channel inhibitor activity 38,999 [21,23,33]
Zinc-alpha-2-glycoprotein ZAZG AZGP1 7q22.1 Protein binding 34,259 [21]
Inter-alpha-trypsin heavy chain H4 ITIH4 ITIH4 3p21.1 Acute-phase response 103,357 [21,23,33]
Ig kappa chain C region IGKC IGKC 2p11.2 Complement activation 11,765 [21,23,33]
Non-secretory ribonuclease RNAS2 RNASE2 14q11.2 Chemotaxis 18,354 [21]
Cystatin M CYTM CST6 11q13.1 Cystein-type endopeptidase inhibitor activity 16,511 [21]
Cystatin C CYTC CST3 20p11.21 Cystein-type endopeptidase inhibitor activity 15,799 [21,30,33]
Serum albumin ALBU ALB 4q13.3 Metal binding 69,367 [23,33]
Alpha-1-antitrypsin A1AT SERPINA1 14q32.13 Protease inhibitor 46,737 [23,33]
Actin, cytoplasmic 1 ACTB ACTB 7p22.1 Cell junction assembly 41,737 [23,33]
Apolipoprotein H APOH APOH 17q24.2 Heparin binding 38,298 [23,33]
Serpin B3 SPB3 SERPINB3 18q21.33 Cystein-type endopeptidase inhibitor activity 44,565 [23,33]
Annexin A1 ANXA1 ANXA1 9q21.13 Calcium ion binding 38,714 [23,33]
Prostaglandin-H2-D-isomerase PTGDS PTGDS 9q34.3 Prostaglandin biosynthesis process 21,029 [23,30,33]
Perlecan (fragment) PGBM HSPG2 1p36.12 Angiogenesis 468,830 [23,33]
Transthyretin TTHY TTR 18q12.1 Protein binding 15,887 [23,33]
Proactivator polypeptide SAP PSAP 10q22.1 Enzyme activator activity 58,113 [23,33]
Nuclear transport factor 2 NTF2 NUTF2 16q22.1 Positive regulation of protein import into nucleus 14,478 [23,33]
Fatty acid-binding protein FABP5 FABP5 8q21.13 Fatty acid binding 15,164 [23,33]
Beta-2-microglobulin B2MG B2M 15q21.1 Antigen processing and presentation of endogenous peptide antigen via MHC class I 13,715 [23,33]
Protein S100-A11 S10AB S100A11 1q21.3 Calcium ion binding 11,740 [23,33]
Non-secretory ribonuclease RNAS2 RNASE2 14q11.2 Chemotaxis 18,354 [23,33]
Protein S100-A8 S10A8 S100A8 1q21.3 Calcium ion binding 10,835 [23,33]
Protein entry name, according to the UniProtKB database. Theoretical molecular weight (Da).
The summary the Biomarkers of MOH are presented in Figure 1.

This entry is adapted from the peer-reviewed paper 10.3390/ijms22169024

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