1. Introduction
Chronic pain (CP) is an essential problem in healthcare. Approximately 20% of the European population is affected by CP
, 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
. 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
. 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
. Paradoxically, they only worsen the chronic symptoms, resulting in the development of the secondary headache, so-called medication-overuse headache (MOH)
. 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
. Although the prevalence of MOH in the general population is around 1–2%
, MOH is still defined as a socio-economic burden worldwide, especially in lesser developed countries
, 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
. 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
.
The proteomic analysis and the exploration of proteomic biomarkers are crucial in understanding a lot of medical conditions, especially cancer, cardiovascular and neurodegenerative diseases
. 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
. 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
. 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
, especially in a group of patients who abuse NSAIDs
. 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
. 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
.
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.
Lipocalin-type prostaglandin D2 synthase |
L-PGDS |
PTGDS |
9q34.3 |
Prostaglandin biosynthesis process |
21,029 |
[23][24] |
Uromodulin (or Tamm-Horsfall urinary glycoprotein) |
UROM |
UMOD |
16p12.3 |
Cellular defense response |
69,761 |
[21][23][24][25] |
Alpha-1-microglobulin |
AMBP |
AMBP |
9q32 |
Calcium channel inhibitor activity |
38,999 |
[21][23][25] |
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][25] |
Ig kappa chain C region |
IGKC |
IGKC |
2p11.2 |
Complement activation |
11,765 |
[21][23][25] |
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][24][25] |
Serum albumin |
ALBU |
ALB |
4q13.3 |
Metal binding |
69,367 |
[23][25] |
Alpha-1-antitrypsin |
A1AT |
SERPINA1 |
14q32.13 |
Protease inhibitor |
46,737 |
[23][25] |
Actin, cytoplasmic 1 |
ACTB |
ACTB |
7p22.1 |
Cell junction assembly |
41,737 |
[23][25] |
Apolipoprotein H |
APOH |
APOH |
17q24.2 |
Heparin binding |
38,298 |
[23][25] |
Serpin B3 |
SPB3 |
SERPINB3 |
18q21.33 |
Cystein-type endopeptidase inhibitor activity |
44,565 |
[23][25] |
Annexin A1 |
ANXA1 |
ANXA1 |
9q21.13 |
Calcium ion binding |
38,714 |
[23][25] |
Prostaglandin-H2-D-isomerase |
PTGDS |
PTGDS |
9q34.3 |
Prostaglandin biosynthesis process |
21,029 |
[23][24][25] |
Perlecan (fragment) |
PGBM |
HSPG2 |
1p36.12 |
Angiogenesis |
468,830 |
[23][25] |
Transthyretin |
TTHY |
TTR |
18q12.1 |
Protein binding |
15,887 |
[23][25] |
Proactivator polypeptide |
SAP |
PSAP |
10q22.1 |
Enzyme activator activity |
58,113 |
[23][25] |
Nuclear transport factor 2 |
NTF2 |
NUTF2 |
16q22.1 |
Positive regulation of protein import into nucleus |
14,478 |
[23][25] |
Fatty acid-binding protein |
FABP5 |
FABP5 |
8q21.13 |
Fatty acid binding |
15,164 |
[23][25] |
Beta-2-microglobulin |
B2MG |
B2M |
15q21.1 |
Antigen processing and presentation of endogenous peptide antigen via MHC class I |
13,715 |
[23][25] |
Protein S100-A11 |
S10AB |
S100A11 |
1q21.3 |
Calcium ion binding |
11,740 |
[23][25] |
Non-secretory ribonuclease |
RNAS2 |
RNASE2 |
14q11.2 |
Chemotaxis |
18,354 |
[23][25] |
Protein S100-A8 |
S10A8 |
S100A8 |
1q21.3 |
Calcium ion binding |
10,835 |
[23][25] |
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][24] |
Apolipoprotein B100 |
APOB |
APOB |
2p24.1 |
Cholesterol metabolism |
516,651 |
[23][24] |
Alpha-2-macroglobulin |
A2MG |
A2M |
12p13.31 |
Enzyme binding |
164,613 |
[23][24] |
Complement factor H |
CFAH |
CFH |
1q31.3 |
Complement activation |
143,480 |
[23][24] |
Complement C3 (fragm) |
CO3 |
C3 |
19p13.3 |
Complement activation |
188,569 |
[23][24] |
Hemopexin |
HEMO |
HPX |
11p15.4 |
Metal ion binding |
52,385 |
[23][24] |
Serum albumin |
ALBU |
ALB |
4q13.3 |
Metal binding |
71,317 |
[23][24] |
Alpha-1B-glycoprotein |
AIBG |
A1BG |
19q13.43 |
Neutrophil, platelet degranulation |
54,790 |
[23][24] |
Complement component C9 |
CO9 |
C9 |
5p13.1 |
Complement activation |
64,615 |
[23][24] |
Kininogen-1 |
KNG1 |
KNG1 |
3q27.3 |
Cysteine-type endopeptidase inhibitor activity |
72,996 |
[23][24] |
Vitronectin |
VTNC |
VTN |
17q11.2 |
Heparin binding |
55,069 |
[23][24] |
Haptoglobin |
HPT |
HP |
16q22.2 |
Acute phase response |
45,861 |
[23][24] |
Apolipoprotein A-4 |
APOA4 |
APOA4 |
11q23.3 |
Lipid binding |
45,371 |
[23][24] |
Alpha-1-acid glycoprotein 1 |
A1AG1 |
ORM1 |
9q32 |
Inflammatory response |
23,725 |
[23][24] |
Serum paraoxonase/arylesterase 1 |
PON1 |
PON1 |
7q21.3 |
Hydrolase |
39,877 |
[23][24] |
Zinc-alpha-2-glycoprotein |
ZA2G |
AZGP1 |
7q22.1 |
Protein transmembrane transporter activity |
34,465 |
[23][24] |
Alpha-1-acid glycoprotein 2 |
A1AG2 |
ORM2 |
9q32 |
Acute phase response |
23,873 |
[23][24] |
Alpha-1-antitrypsin |
A1AT |
SERPINA1 |
14q32.13 |
Protease inhibitor |
46,737 |
[23][24] |
Immunoglobulin heavy constant alpha 1 |
IGHA1 |
IGHA1 |
14q32.33 |
Antigen binding |
37,655 |
[23][24] |
Retinol-binding protein |
RETBP |
RBP4 |
10q23.33 |
Retinol binding |
23,010 |
[23][24] |
Transthyretin |
TTHY |
TTR |
18q12.1 |
Hormone binding |
15,887 |
[23][24] |
Apolipoprotein E |
APOE |
APOE |
19q13.32 |
Lipid transport |
36,154 |
[23][24] |
Vitamin D-binding protein |
VDBP |
GC |
4q13.3 |
Vitamin D transport |
52,918 |
[23][24] |
Candidate urine proteomic biomarkers of MOH are shown in Table 2.