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Article

Increased Inflammatory Markers at AMPH-Addicts Are Related to Neurodegenerative Conditions: Alzheimer’s Disease

by
Aziza R. Alrafiah
1,*,
Mohammed M. Abu-Illah
1,2,
Rania M. Magadmi
3,
Aqeel Aqeel
2,
Abdulmuttaleb Najmi
2 and
Sattam Jaddoh
2
1
Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia
2
Eradaa for Mental Health—Jazan, Jazan Health—Ministry of Health, Jazan 45142, Saudi Arabia
3
Pharmacology Department, Faculty of Medicine, King Abdulaziz University, Jeddah 22254, Saudi Arabia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(11), 5536; https://doi.org/10.3390/app12115536
Submission received: 24 April 2022 / Revised: 21 May 2022 / Accepted: 26 May 2022 / Published: 30 May 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
Amphetamine addiction is widespread worldwide despite causing severe physical and mental problems, including neurodegeneration. One of the most common neurodegenerative disorders is Alzheimer’s disease (AD). Several inflammatory markers have been linked to AD. Previous studies have also found these biomarkers in amphetamine-addicts (AMPH-add). This study thus seeks to understand how AD and AMPH-addiction are related. A case–control observational study was conducted. Seventeen AMPH-adds ranging in age from 23 to 40 were recruited from Al Amal Psychiatric Hospital. In addition, 19 healthy subjects matching their age and gender were also recruited. The Luminex technique was used to measure serum alpha 1 antichymotrypsin (ACT), pigment epithelium-derived factor (PEDF), and macrophage inflammatory protein-4 (MIP-4), after complying with ethical guidelines and obtaining informed consent. In addition, liver function enzymes were correlated to AD’s predictive biomarkers in AMPH-adds. AMPH-adds had significantly higher serum levels of ACT, PEDF, and MIP-4 when compared to healthy controls (p = 0.03, p = 0.001, and p = 0.012, respectively). Furthermore, there is a significant correlation between lower ALT levels and elevated AST to ALT ratios in AMPH-adds (r = 0.618, 0.651, and p = 0.0001). These changes in inflammatory biomarkers may be linked to the onset of AD at a young age in amphetamine-drug addicts.

1. Introduction

Neurodegenerative disorders are conditions that damage and impair the structure and function of neurons in the brain. Amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), Alzheimer’s disease (AD), and Huntington’s disease are examples of these diseases (HD). These are incapacitating conditions that impair mobility and cognition [1]. AD and PD are thought to have a higher prevalence of distribution than other neurodegenerative diseases [2,3].
Alzheimer’s disease pathogenesis is characterized by degeneration and inflammation of the neurons in the brain of the aging patient. Inflammatory response proteins have also been discovered to contribute to plaque formation [4].
The development of novel and viable pharmaceutical targets for this disease is therefore crucial. Neurotoxicity, researchers believe, is a major cause of neurodegenerative disease. As a result, understanding neurotoxicity may improve therapy options for brain diseases such as Alzheimer’s [5].
Alcohol, amphetamines, marijuana, cannabis, heroin, and cocaine are just some of the substances that are abused in our community. Around 7% of Saudis admit to using drugs [6]. Among the most commonly used medications is amphetamine, a class of synthetic psychoactive substances that stimulate the central nervous system. Among the most popular recreational psychostimulants are methamphetamine (METH) and 3,4-methylenedioxymethamphetamine (MDMA), both of which are linked to amphetamines [7,8,9]. Neurons and other cells are affected by addiction to these substances. A drug or alcohol addiction may affect the activity of oligodendrocytes and astrocytes, causing inflammation in the brain [10]. According to local research, amphetamine users had a 100 percent prevalence of psychotic symptoms [11]. Therefore, amphetamine is considered to be one of the most intriguing chemicals associated with neurodegeneration.
The identification of neurodegenerative biomarkers will enhance the understanding of the pathophysiology of the disease. In addition to their potential use in diagnosing drug addiction, the presence of these biomarkers may help in better understanding the pharmacodynamic action of those substances in the central nervous system (CNS). Recent studies have demonstrated the neurotoxic and neuroinflammatory effects of amphetamine-related medications. The biochemical changes associated with amphetamine abusers are similar to those associated with stroke, Alzheimer’s, Parkinson’s, and brain tumors. Since these substances have unknown effects on the nervous system, some studies have reported a connection between addiction to some types of stimulants, such as amphetamine, and biomarkers associated with neurodegenerative disorders [12,13]. A number of these biomarkers are high when a patient uses drugs frequently.
Alzheimer’s disease is currently not treatable. Understanding the CNS mechanism of AMPH-add and the evolution of AD is therefore imperative [14,15]. Saudi Arabian authorities failed to conduct systematic studies on the biomarkers associated with amphetamine users’ neurodegenerative conditions; therefore, the purpose of this case–control observational study was to assess the link between AMPH-add and neurodegenerative disorders using inflammatory biomarkers. Furthermore, researchers investigate the connection between liver function enzymes (LFTs) and biomarkers of neurodegenerative diseases.

2. Materials and Methods

2.1. Study Design and Population

An observational case–control study was conducted. Nineteen healthy subjects (group I, age 40:24 × 5:3 years) and seventeen AMPH-adds (group II, age 32:24 × 1:72 years) participated. Those enrolled had a history of at least two years of being pure amphetamine abusers. The diagnosis was determined by urine and blood GCMS screening tests, considering all amphetamine routes and doses. In these cases, amphetamine drug abusers were newly admitted patients who had not begun rehabilitation yet. None of them were known to have any chronic health problems such as diabetes, hypertension, heart problems, or kidney problems.
Healthy subjects were also recruited based on age, sex, and nationality. A physician performed a physical examination and a complete history to exclude chronically ill subjects (heart, kidney, or liver disease, and psychological disorders). In addition to patients suffering from active depression and delirium, the study excluded patients with severe head trauma within the past three months, as well as those under the age of 18 and over 40 years old. Patients with chronic diseases (heart, kidney, and liver disease and psychological disorders) were excluded.
All subjects were tested for serum ACT, PEDF, and MIP-4 using the LuminexTM 200TM Instrument System (Austin, TX, USA) and using the Luminex human neurodegenerative kit; in addition, another sample was taken in a chemistry tube for liver function tests. Moreover, a questionnaire was distributed to AMPH-adds only to obtain detailed information about their addiction.
Informed consent was obtained from all study participants (or their caregivers) after explaining the study’s aim and procedure to them. It was approved by the Ministry of Health of Jazan’s Institutional Review Board and Ethics Committee (IRB Reg. No. 11-10-Z-073).

2.2. Questionnaire Design

In the current study, the questionnaire was only distributed among AMPH-adds to correlate the history of AMPH-addion with the expression of biomarkers and the risk of neurodegenerative diseases. The questionnaire comprised 9 questions that were divided into two parts. The first part was related to the demographic data of the participants. The second part aimed at assessing the dose and frequency of AMPH-addiction and duration of addiction (years) and the age of the participants when they started the drug.

2.3. Inflammatory Biomarker Assessment (ACT, PEDF, and MIP-4)

A multiplex assay for the detection of these biomarkers was conducted using MILLIPLEX’s human neurodegenerative disease panel 2 kit, cat#: HNDG2MAG-36K, which was also used to determine ACT levels, PEDF levels, and MIP-4 levels. All of the above parameters were assessed in duplicate on frozen serum samples collected from AMPH-adds and healthy subjects in a single plate, following manufacturer assay protocols. A Luminex 200 machine and MILLIPLEX Analyst software were used for data analysis.
The kit has a 96-well format and includes a standard lyophilized cocktail and two quality controls for measuring up to 38 serum samples in triplicate. In addition, measurements of the multidimensional fatigue inventory (MFI) were taken, and the data were examined for sensitivity, consistency, and reproducibility.
In summary, 25 g of serum (1:2) was incubated with antibody-conjugated magnetic beads overnight in the refrigerator at 4 °C. After being cleaned, bead complexes were maintained for half an hour on a plate shaker at room temperature with a 50 μL biotinylated detection antibody. They were then incubated for half an hour on a plate shaker at 20–25° with 50 μL streptavidin-phycoerythrin. After three washes, 100 μL of Sheath Fluid was poured into each well. The bead complexes were then read on a Luminex® 200TM Run plate (Austin, TX, USA) and analyzed using MAGPIX® (Luminex, Austin, TX, USA) and xPONENT® software (Luminex, Austin, TX, USA).

2.4. Measuring the Level of Liver Transaminases (ALT and AST)

The separated samples were transported to the biochemistry department to start the analysis process using a Vitros 350® Machine (VITROS® 350 Chemistry System, Ortho Clinical Diagnostics, Raritan, NJ, USA, 2020). All reagents used for this test were left out at room temperature for at least 30 min. Before adding the rack to the machine, the information for every sample was programmed. The LFT panel was selected for each sample to test ALP, AST, ALT, Alb, TP, Tbil, and Dbil. After that, a sufficient amount of each sample was added. Then, the rack was placed in the right position and the analyses started. The analysis process took 8 to 10 min to start printing the results.

2.5. Statistical Analysis

Data were analyzed using IBM SPSS Statistical for Windows, version 21 (IBM Corp., Armonk, NY, USA). Descriptive data were expressed as mean and standard error (S.E.). The Shapiro test used to measure normality of distribution of parametric parameters. All data were statistically analyzed using an unpaired Student’s t-test to compare the addiction group and the control group in all investigated biomarkers as well. Correlations between measured parameters were made using Pearson correlations. p-values less than 0.05 were considered significant. Graphs were obtained using GraphPad Prism version 8 software (GraphPad Sotware, La Jolla, CA, USA).

3. Results

3.1. Sociodemographic Data of AMPH-add Participants

The mean age of the AMPH-add participants was 32.23 ± 7.09 years and control was 31.56 ± 5.04. In AMPH-add and control, almost 17.6% and 21% of the participants had higher education (university), whereas only 23.5% and 26.4% had intermediate education, and 58.8% and 52.6% had secondary education. All participants were not suffering from chronic diseases such as diabetes, hypertension, heart, and renal diseases. In the current questionnaire, all the participants admitted to using amphetamine and starting the abuse of the drug (addiction period) at least two years ago. In the present questionnaire, 16 AMPH-add and 18 of control participants were smokers (94.1% and 94.1%), while 6 participants were taking amphetamine and toombak (35.3%), and 11 had not taken either (64.7%) (Table 1).

3.2. Biomarkers and Proinflammatory Cytokines Levels in AMPH-adds’ and Control Subjects Sera

3.2.1. α1-Anti-Chymotrypsin (ACT) Level

The ACT level was significantly (p = 0.037) increased in the serum of amphetamine-addicts compared to corresponding control healthy subjects. The serum ACT level increased from 163,406.31 ± 60,769.13 to 834,027.99 ± 329,296.57 ng/mL (Figure 1) & (Table 2).

3.2.2. Pigment Epithelium-Derived Factor (PEDF) Level

There was a significant (p < 0.001) increase in serum PEDF levels in AMPH-adds to 51,804.41 ± 3205.92 ng/mL in comparison to control healthy subjects 27,709.64 ± 1672.57 ng/mL (Figure 2) & (Table 2).

3.2.3. Macrophage Inflammatory Protein 4 (MIP-4) Level

Interestingly, the MIP-4 level was significantly (p = 0.012) increased in the serum of AMPH-adds compared to corresponding control healthy subjects. The serum MIP-4 level increased from 439.84 ± 17.19 to 597.59 ± 61.31 ng/mL (Figure 3) & (Table 2).

3.3. Comparison of Liver Function Enzymes among Serum of AMPH-adds’ and Control Subjects

3.3.1. Alanine Aminotransferase (ALT) Level

The ALT level was significantly (p < 0.001) decreased in the serum of AMPH-adds compared to the corresponding normal control group. The serum ALT level decreased from 34.36 ± 2.21 to 11.94 ± 0.824 U/L (Figure 4a).

3.3.2. Aspartate Aminotransferase (AST) Level

There was a significant (p < 0.001) increase in serum AST level in AMPH-adds to 84.88 ± 4.77 U/L in comparison to the control healthy group at 29.31 ± 3.64 U/L (Figure 4b).

3.3.3. AST to ALT Ratio

Interestingly, the AST/ALT ratio was significantly (p < 0.001) increased in AMPH-adds compared to the corresponding normal healthy control group. The serum AST/ALT ratio increased from 0.84 ± 0.084 to 7.48 ± 0.59 (Table 3 and Figure 4c).

3.4. Pearson’s Correlation between the Biomarkers and Proinflammatory Cytokines and Liver Transaminases (ALT and AST) in Both AMPH-adds’ Serum and Control Subjects

Because amphetamine-induced liver injury can explain the change in neurodegenerative biomarkers, the correlation between liver function markers and neurodegenerative biomarkers has been investigated.
As shown in Table 3, there was significant inverse correlation between PEDF and ALT (r = −0.618, *** p = 0.001), with positive significant correlations with AST (r = 0.551, *** p = 0.0001) and the AST/ALT ratio (r = 0.651, *** p = 0.0001); moreover, there were non-significant correlations between liver function tests versus ACT and MIP-4.

4. Discussion

It has been reported that neuroinflammatory processes in drug addiction disorders can lead to neurodegeneration in specific brain areas [16]. According to independent research, the prevalence of neurodegenerative disease and dementia is significantly higher among AMPH-adds [17,18,19]. Currently, this is the first study to measure neurodegenerative biomarkers in serum from AMPH-adds.
AMPH-adds showed a significant increase in ACT compared to control healthy subjects in the current study; as a result, AMPH-adds may exhibit early symptoms of AD.
Serine protease inhibitors include ACT and SERPINA3, which are members of the acute-phase protein family [20]. While liver cells predominantly synthesize ACT, brain cells, primarily astrocytes, also produce it. Furthermore, there is a connection between high ACT levels in plasma, serum, and cerebrospinal fluid (CSF) in patients with AD and a cognitive decline in elderly individuals [18]. According to this evidence, ACT may serve as a biomarker for early diagnosis. In addition, ACT catalyzed amyloid β-peptide polymerization in vitro, an acute-phase inflammatory protein that contributes to amyloid deposits in AD [21].
A high level of interleukin (IL)-1 β is expressed by microglia around plaques in AD brains, which correlates with the severity of the pathology. In response to these elevated levels of A β, there is an inflammatory response leading to an increased expression of proteins associated with inflammation, such as IL-1β, ACT, and complement factors [2,3]. In addition, astrocytes stimulated by IL-1 β express higher ACT levels, and the amyloid precursor protein was translated more efficiently [4,21]. According to studies identifying ACT as associated with Alzheimer’s amyloid plaques and is expressed exclusively in regions with IL-1 β overexpression, these findings suggest that this coupled expression may play a significant role in disease pathogenesis [21].
Compared to healthy control subjects, AMPH-adds had a significant increase in PEDF. In AMPH-adds, PEDF concentration in serum is important as a predictor of AD based on the concentration of PEDF in the serum. To the best of our knowledge, there are no previous studies performed to measure PEDF in AMPH-adds or any other substance users. However, one study showed a significant increase in PEDF levels in the serum of eight alcohol users [22].
Neurotrophic and neuroprotective protein PEDF, a 50-kDa molecule expressed widely in the nervous system plays a role in neuroregulation [23]. PEDF protects against insults such as quinolinic acid excitotoxicity and glutamate excitotoxicity [23]. In addition, PEDF can attenuate ischemic brain damage [24,25].
According to Yumagishi and his colleagues, PEDF exhibits strong immunoreactivity in astrocytes and cortical neurons in Alzheimer’s brains [24]. In addition, the presence of PEDF proteins was well-correlated with the presence of the receptor for advanced glycation end products (RAGE), one of the receptors for amyloid β peptides, which are involved in the death of neuronal cells and the activation of microglial cells in AD [14,19]. They also suggested that PEDF may provide an indicator of PEDF turnover in the brain and be used to measure neuronal disruption induced by oxidative stress in AD [25].
Accordingly, Lang et al. studied PEDF levels in paired CSF and serum samples by ELISA in people with Alzheimer’s disease, frontotemporal dementia, vascular dementia, multiple sclerosis, pseudotumor cerebri, and other non-inflammatory neurologic diseases [26]. They established CSF/serum quotient diagrams to determine the fraction of intrathecally synthesized PEDF in CSF. In patients with AD, frontotemporal disorders (FTD), and bacterial meningitis, PEDF levels are significantly higher. Patients with AD also had significantly elevated PEDF concentrations in their serum [27]. PEDF can be used clinically as a biomarker to assist in more accurate diagnosis and the treatment of patients suffering from AD. However, the changes in PEDF content in AD are controversial [27]. The PEDF is reduced in AD, according to a study conducted by Huang and his colleagues [28]. The PEDF content in AD was also unchanged according to Roher et al. and Abraham et al. [29,30]. PEDF needs to be studied further to understand its effects on AD and AMPH-add [31].
Compared to control healthy subjects, AMPH-adds had a significant increase in MIP-4. Ziliotto and his colleagues observed similar findings by reporting that CCL18 plasma levels correlate with more severe inflammatory and neurodegenerative outcomes in multiple sclerosis [32]. According to McDonnell-Dowling and Kelly, amphetamine-induced toxicity could be explained by its links to excitotoxicity (excess glutamate release), mitochondrial dysfunction, blood–brain barrier dysfunction, inflammation, and DNA damage. These factors explain, at a cellular level, the harmful effects of AMPH-add [33]. Tripathy and his colleagues reported that AD-derived brain microvessels release high MIP protein and mRNA levels compared to controls; furthermore, oxidative and lipid insults to the brain microvasculature potentially contribute to the inflammatory environment in the AD brain [32].
Chemokine ligands and co-stimulatory factors are engaged in macrophage activation and differentiation processes, which may have a role in developing Alzheimer’s disease [33]. Cytokines and chemokines can also activate microglia and astrocytes (AC), increasing cytokine and chemokine expression. Reactivated microglia might engulf the debris of injured tissue and the deposition of AC as phagocytes in the CNS. AC that has been reactivated also plays a significant function. AC plays several functions in the onset and progression of Alzheimer’s disease [34].
On the one hand, AC may phagocytose trash, offer food, and aid in controlling the chemical composition of the fluid surrounding neurons. On the other hand, AC promotes inflammation and is implicated in the creation of free radicals (FR) and intracellular neurofibrillary tangles, all of which contribute to the progression of AD [35]. Because AC is involved in the internalization, degradation, and generation of Aβ, its effects on Aβ are more complicated and controversial [36].
The association between serum-based liver function indicators and neurodegenerative biomarkers was explored in this study. The findings of this study revealed that the lower levels of ALT and higher AST to ALT ratio seen in amphetamine users were linked to AD biomarkers as well. In addition, an elevated AST to ALT ratio was linked to a higher PEDF level. In addition, a lower level of ALT was linked to a lower level of PEDF [37].
In patients with Alzheimer’s disease, lower ALT and higher AST to ALT ratio values were found, which were linked to worsened memory and executive function scores [37]. The results of this investigation are similar to those of a previous study [38], which found higher AST to ALT ratio values and lower ALT levels in patients with AD compared to controls. In addition, changes in liver enzymes lead to abnormalities in liver-associated metabolites such as branched-chain amino acids, ether-phosphatidylcholines, and lipids, which are changed in AD and may have a role in disease pathophysiologic features, according to Varma and colleagues [39].
PEDF is thought to be mostly derived from the liver [40,41]. According to the data published by Nho and colleagues, altered liver function indicators and amyloid-, tau-, and neurodegenerative biomarkers of AD pathogenesis appeared to be related to AD diagnosis and reduced memory and executive function [42]. The researchers discovered that a higher AST to ALT ratio and lower ALT levels were connected to the diagnosis of Alzheimer’s disease [42]. According to the researchers, higher AST to ALT ratio values were also linked to lower CSF levels of amyloid, and they increased amyloid deposition and CSF levels of PEDF [43,44]. Lower ALT levels have also been linked to increased amyloid deposition and brain shrinkage [42].
The current study used Luminex technology to identify a plasma protein neurodegenerative panel containing ACT, PEDF, and MIP-4 that demonstrated the highest determinative score for AD in AMPH-adds for the first time. In addition, this study focused on the interaction and alterations in those inflammatory biomarkers and AD development in amphetamine users. As a result, our data imply that combining three plasma proteins is an excellent diagnostic biomarker for early AD prediction in AMPH-adds, providing a unique insight into AD’s early diagnosis and management. The following stages in determining the neuropsychological profile of amphetamine components among their abusers will be to correlate neuropsychological performance with biological markers and conduct prospective studies to follow up on the neuropsychological performance of AMPH-adds.
In future studies, we recommend extending the sample size to provide additional statistical power and confirmation of our findings. Tests of other sensitive inflammatory biomarkers, such as interleukins, should also be conducted; moreover, more functional, sensitive, and specific techniques are needed.

5. Limitation and Recommendation

There was a limitation in the present study. Our study is an observational case–control study; therefore, there was a lack of the control Alzheimer group, which resulted in it not being included in the study population. Therefore, we recommend further research into the association between amphetamine-addiction and the risk of developing dementia and Alzheimer disease specified to compare with the Alzheimer disease group.

6. Conclusions

Amphetamine-drug addicts may develop AD at an early age due to changes in inflammatory biomarkers. Our results indicate that abusers of amphetamines exhibit increased levels of these inflammatory biomarkers such as ACT, PEDF, and MIP-4. Therefore, they may provide a potential screening marker for AD incidence or progression in amphetamine abusers that can be considered in their diagnosis and treatment. However, the change in the levels of these biomarkers did not prove that they were causing neurodegenerative diseases, including AD.

Author Contributions

Conceptualization, A.R.A. and R.M.M.; methodology, A.R.A. and M.M.A.-I. software, A.A. and M.M.A.-I.; validation, A.R.A., R.M.M. and M.M.A.-I.; formal analysis, A.R.A., R.M.M. and M.M.A.-I.; investigation, A.R.A. and M.M.A.-I.; resources, A.A., A.R.A., R.M.M., M.M.A.-I., A.A., A.N. and S.J.; data curation, A.A., R.M.M., M.M.A.-I., A.A., A.N. and S.J.; writing—original draft preparation, A.R.A. and M.M.A.-I.; writing—review and editing, A.R.A., R.M.M., A.N. and S.J.; visualization, A.R.A. and M.M.A.-I.; supervision, A.R.A. and R.M.M.; project administration, A.R.A.; funding acquisition, A.R.A., A.N. and M.M.A.-I. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of Najran University and approved by the Institutional scientific and Research Committee of Jazan Ministry of health under the reference number of IRB Reg. # 11-10-Z-073. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors would like to thank “The International Conference and Addiction Psychiatry and Mental Health (Addiction 2019)” for accepting the abstract for presentation at the conference. The work is a requirement for a postgraduate degree for Mohammad Abu-Illah offered by the Department of Medical Laboratory Sciences, Faculty of Applied Medical Science, King Abdulaziz University at Jeddah, which facilitated the progress of these experiments.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Meghraoui, D.; Boudraa, B.; Djeddou, M.; Meksen, T.M.; Boudraa, M. Healthy and Parkinson voices discrimination based on compensation/normalization cepstral features. In Proceedings of the 2018 International Conference on Applied Smart Systems (ICASS), Médéa, Algeria, 24–25 November 2018; pp. 1–5. [Google Scholar]
  2. Thies, W.; Bleiler, L.; Alzheimer’s Association. 2013 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 2013, 9, 208–245. [Google Scholar]
  3. Padmanabhan, J.; Levy, M.; Dickson, D.W.; Potter, H. Alpha1-antichymotrypsin, an inflammatory protein overexpressed in Alzheimer’s disease brain, induces tau phosphorylation in neurons. Brain 2006, 129, 3020–3034. [Google Scholar] [CrossRef] [PubMed]
  4. Mielke, M.M.; Vemuri, P.; Rocca, W.A. Clinical Epidemiology of Alzheimer’s Disease: Assessing Sex and Gender Differences. Clin. Epidemiol. 2014, 6, 37–48. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Soleimani, S.M.A.; Ekhtiari, H.; Cadet, J.L. Drug-induced neurotoxicity in addiction medicine: From prevention to harm reduction. Prog. Brain Res. 2016, 223, 19–41. [Google Scholar]
  6. Alshmrani, S. 7% of Saudis Are Drug Users [Internet]. Saudi Arabia: Al-Hayat; Newspaper 2017. Available online: http://www.alhayat.com/article/812946/ (accessed on 15 September 2019).
  7. Bazmi, E.; Mousavi, F.; Giahchin, L.; Mokhtari, T.; Behnoush, B. Cardiovascular complications of acute amphetamine abuse: Cross-sectional study. Sultan Qaboos Univ. Med. J. 2017, 17, e31. [Google Scholar] [CrossRef]
  8. Greene, S.L.; Kerr, F.; Braitberg, G. Review article: Amphetamines and related drugs of abuse. Emerg. Med. Australas. 2008, 20, 391–402. [Google Scholar] [CrossRef]
  9. Rusyniak, D.E. Neurologic Manifestations of Chronic Methamphetamine Abuse. Neurol. Clin. 2011, 29, 641–655. [Google Scholar] [CrossRef] [Green Version]
  10. Costa, L.G.; Giordano, G.; Guizzetti, M. In vitro approaches to developmental neurotoxicity. In Reproductive and Developmental Toxicology; Academic Press: Cambridge, MA, USA, 2011; pp. 159–166. [Google Scholar]
  11. Alibrahim, O.; Elawad, N.; Misau, Y.A.; Shaikh, T.M.; Allam, N. Drug dependence and psychotic symptoms: A retrospective study of adolescents who abuse drugs at Al-Amal Hospital in Jeddah, Saudi Arabia. J. Public Health Afr. 2012, 3, e5. [Google Scholar] [CrossRef]
  12. Deik, A.; Saunders-Pullman, R.; Luciano, M.S. Substance Abuse and Movement Disorders: Complex Interactions and Comorbidities. Curr. Drug Abus. Rev. 2012, 5, 243–253. [Google Scholar] [CrossRef]
  13. Steinkellner, T.; Freissmuth, M.; Sitte, H.H.; Montgomery, T. The ugly side of amphetamines: Short- and long-term toxicity of 3,4-methylenedioxymethamphetamine (MDMA, ‘Ecstasy’), methamphetamine and d-amphetamine. Biol. Chem. 2011, 392, 103–115. [Google Scholar] [CrossRef] [Green Version]
  14. da Hage-Melim, L.I.S.; Ferreira, J.V.; de Oliveira, N.K.; Correia, L.C.; Almeida, M.R.; Poiani, J.G.; Taft, C.A.; de Paula da Silva, C.H. The Impact of Natural Compounds on the Treatment of Neurodegenerative Diseases. Curr. Org. Chem. 2019, 23, 335–360. [Google Scholar] [CrossRef]
  15. Tonda-Turo, C.; Origlia, N.; Mattu, C.; Accorroni, A.; Chiono, V. Current Limitations in the Treatment of Parkinson’s and Alzheimer’s Diseases: State-of-the-Art and Future Perspective of Polymeric Carriers. Curr. Med. Chem. 2019, 25, 5755–5771. [Google Scholar] [CrossRef] [PubMed]
  16. Alrafiah, A.; Alofi, E.; Almohaya, Y.; Hamami, A.; Qadah, T.; Almaghrabi, S.; Hakami, N.; Alrawaili, M.S.; Tayeb, H.O. Angiogenesis Biomarkers in Ischemic Stroke Patients. J. Inflamm. Research. 2021, 14, p. 4893. Available online: https://www.dovepress.com/by168.149.12.66 (accessed on 22 September 2021).
  17. Vicente-Rodríguez, M.; Fernández-Calle, R.; Gramage, E.; Pérez-García, C.; Ramos, M.P.; Herradón, G. Midkine Is a Novel Regulator of Amphetamine-Induced Striatal Gliosis and Cognitive Impairment: Evidence for a Stimulus-Dependent Regulation of Neuroinflammation by Midkine. Mediat. Inflamm. 2016, 2016, 1–11. [Google Scholar] [CrossRef]
  18. Al-Rafiah, A.; Magadmi, R.; Al-Kaabi, A.; Alsomali, N. Parkinson’s Disease-Related Biomarkers That May Appear in Amphetamine Abusers. BioMed Res. Int. 2021, 2021, 1–8. [Google Scholar] [CrossRef] [PubMed]
  19. Xu, E.; Liu, J.; Liu, H.; Wang, X.; Xiong, H. Inflammasome Activation by Methamphetamine Potentiates Lipopolysaccharide Stimulation of IL-1β Production in Microglia. J. Neuroimmune Pharmacol. 2018, 13, 237–253. [Google Scholar] [CrossRef]
  20. Tyagi, E.; Fiorelli, T.; Norden, M.; Padmanabhan, J. Alpha 1-Antichymotrypsin, an Inflammatory Protein Overexpressed in the Brains of Patients with Alzheimer’s Disease, Induces Tau Hyperphosphorylation through c-Jun N-Terminal Kinase Activation. Int. J. Alzheimer’s Dis. 2013, 2013, 606083. [Google Scholar] [CrossRef] [Green Version]
  21. Nilsson LN, G.; Bales, K.R.; DiCarlo, G.; Gordon, M.N.; Morgan, D.; Paul, S.M.; Potter, H. α-1-Antichymotrypsin promotes β-sheet amyloid plaque deposition in a transgenic mouse model of Alzheimer’s disease. J. Neurosci. 2001, 21, 1444–1451. [Google Scholar] [CrossRef] [Green Version]
  22. Hickman, S.E.; Allison, E.K.; El Khoury, J. Microglial dysfunction and defective β-amyloid clearance pathways in aging Alzheimer’s disease mice. J. Neurosci. 2008, 28, 8354–8360. [Google Scholar] [CrossRef]
  23. Sogawa, K.; Kodera, Y.; Satoh, M.; Kawashima, Y.; Umemura, H.; Maruyama, K.; Takizawa, H.; Yokosuka, O.; Nomura, F. Increased Serum Levels of Pigment Epithelium-Derived Factor by Excessive Alcohol Consumption-Detection and Identification by a Three-Step Serum Proteome Analysis. Alcohol. Clin. Exp. Res. 2010, 35, 211–217. [Google Scholar] [CrossRef]
  24. Arina, R.; Marietta, Z.; Menderes, Y.T.; Ryan, C.; Melina, N.-K.K.; Ana, L.P. Pigment Epithelium-Derived Factor Improves Paracellular Blood-Brain Barrier Integrity in the Normal and Ischemic Mouse Brain. Cell. Mol. Neurobiol. 2020, 15, 728–741. [Google Scholar] [CrossRef]
  25. Sanagi, T.; Yabe, T.; Yamada, H. Gene transfer of PEDF attenuates ischemic brain damage in the rat middle cerebral artery occlusion model. J. Neurochem. 2008, 106, 1841–1854. [Google Scholar] [CrossRef] [PubMed]
  26. Yamagishi, S.; Inagaki, Y.; Takeuchi, M.; Sasaki, N. Is pigment epithelium-derived factor level in cerebrospinal fluid a promising biomarker for early diagnosis of Alzheimer’s disease? Med. Hypotheses 2004, 63, 115–117. [Google Scholar] [CrossRef] [PubMed]
  27. Lang, V.; Zille, M.; Infante-Duarte, C.; Jarius, S.; Jahn, H.; Paul, F.; Ruprecht, K.; Pina, A.L. Alzheimer’s disease: Elevated pigment epithelium-derived factor in the cerebrospinal fluid is mostly of systemic origin. J. Neurol. Sci. 2017, 375, 123–128. [Google Scholar] [CrossRef] [PubMed]
  28. Huang, W.J.; Zhang, X.; Chen, W.W. Association between alcohol and Alzheimer’s disease. Exp. Ther. Med. 2016, 12, 1247–1250. [Google Scholar] [CrossRef] [Green Version]
  29. Huang, M.; Qi, W.; Fang, S.; Jiang, P.; Yang, C.; Mo, Y.; Dong, C.; Li, Y.; Zhong, J.; Cai, W.; et al. Pigment Epithelium-Derived Factor Plays a Role in Alzheimer’s Disease by Negatively Regulating Aβ42. Neurotherapeutics 2018, 15, 728–741. [Google Scholar] [CrossRef] [Green Version]
  30. Abraham, J.-D.; Calvayrac-Pawlowski, S.; Cobo, S.; Salvetat, N.; Vicat, G.; Molina, L.; Touchon, J.; Michel, B.-F.; Molina, F.; Verdier, J.-M.; et al. Combined measurement of PEDF, haptoglobin and tau in cerebrospinal fluid improves the diagnostic discrimination between alzheimer’s disease and other dementias. Biomarkers 2011, 16, 161–171. [Google Scholar] [CrossRef]
  31. Roher, A.E.; Maarouf, C.L.; Sue, L.I.; Hu, Y.; Wilson, J.; Beach, T.G. Proteomics-derived cerebrospinal fluid markers of autopsy-confirmed Alzheimer’s disease. Biomarkers 2009, 14, 493–501. [Google Scholar] [CrossRef] [Green Version]
  32. Ziliotto, N.; Bernardi, F.; Jakimovski, D.; Baroni, M.; Bergsland, N.; Ramasamy, D.P.; Weinstock-Guttman, B.; Zamboni, P.; Marchetti, G.; Zivadinov, R.; et al. Increased CCL18 plasma levels are associated with neurodegenerative MRI outcomes in multiple sclerosis patients. Mult. Scler. Relat. Disord. 2018, 25, 37–42. [Google Scholar] [CrossRef]
  33. McDonnell-Dowling, K. The Role of Oxidative Stress in Methamphetamine-induced Toxicity and Sources of Variation in the Design of Animal Studies. Curr. Neuropharmacol. 2017, 15, 300–314. [Google Scholar] [CrossRef] [Green Version]
  34. Tripathy, D.; Thirumangalakudi, L.; Grammas, P. Expression of macrophage inflammatory protein 1-α is elevated in Alzheimer’s vessels and is regulated by oxidative stress. J. Alzheimer’s Dis. 2007, 11, 447–455. [Google Scholar] [CrossRef]
  35. Liu, C.; Cui, G.; Zhu, M.; Kang, X.; Guo, H. Neuroinflammation in Alzheimer’s disease: Chemokines produced by astrocytes and chemokine receptors. Int. J. Clin. Exp. Pathol. 2014, 7, 8342. [Google Scholar] [PubMed]
  36. Bennett, S.; Grant, M.; Creese, A.J.; Mangialasche, F.; Cecchetti, R.; Cooper, H.J.; Aldred, S. Plasma levels of complement 4a protein are increased in Alzheimer’s disease. Alzheimer Dis. Assoc. Disord. 2012, 26, 329–334. [Google Scholar] [CrossRef] [PubMed]
  37. Zuena, A.R.; Casolini, P.; Lattanzi, R.; Maftei, D. Chemokines in alzheimer’s disease: New insights into prokineticins, chemokine-like proteins. Front. Pharm. 2019, 10, 622. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Nho, K.; Kueider-Paisley, A.; Ahmad, S.; Mahmoudiandehkordi, S.; Arnold, M.; Risacher, S.L.; Louie, G.; Blach, C.; Baillie, R.; Han, X.; et al. Association of Altered Liver Enzymes With Alzheimer Disease Diagnosis, Cognition, Neuroimaging Measures, and Cerebrospinal Fluid Biomarkers. JAMA Netw. Open 2019, 2, e197978. [Google Scholar] [CrossRef]
  39. Giambattistelli, F.; Bucossi, S.; Salustri, C.; Panetta, V.; Mariani, S.; Siotto, M.; Cassetta, E. Effects of hemochromatosis and transferrin gene mutations on iron dyshomeostasis, liver dysfunction and on the risk of Alzheimer’s disease. Neurobiol. Aging 2012, 33, 1633–1641. [Google Scholar] [CrossRef]
  40. Varma, V.R.; Oommen, A.M.; Varma, S.; Casanova, R.; An, Y.; Andrews, R.M.; Toledo, J. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study. PLoS Med. 2018, 15, e1002482. [Google Scholar] [CrossRef]
  41. Sugimoto, K.; Takei, Y. Pathogenesis of alcoholic liver disease. Hepatol. Res. 2017, 47, 70–79. [Google Scholar] [CrossRef]
  42. He, R.; Yan, X.; Guo, J.; Xu, Q.; Tang, B.; Sun, Q. Recent Advances in Biomarkers for Parkinson’s Disease. Front. Aging Neurosci. 2018, 10, 305. [Google Scholar] [CrossRef]
  43. He, X.; Cheng, R.; Benyajati, S.; Ma, J. PEDF and its roles in physiological and pathological conditions: Implication in diabetic and hypoxia-induced angiogenic diseases. Clin. Sci. 2015, 128, 805–823. [Google Scholar] [CrossRef] [Green Version]
  44. Cheng, Z.; Yin, J.; Yuan, H.; Jin, C.; Zhang, F.; Wang, Z.; Xiao, S. Blood-Derived Plasma Protein Biomarkers for Alzheimer’s Disease in Han Chinese. Front. Aging Neurosci. 2018, 10, 414. [Google Scholar] [CrossRef] [Green Version]
Applsci 12 05536 g001 550
Figure 1. Comparison of α1-antichymotrypsin serum level between AMPH-add and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. *: statistically significant at p < 0.05.
Figure 1. Comparison of α1-antichymotrypsin serum level between AMPH-add and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. *: statistically significant at p < 0.05.
Applsci 12 05536 g001
Applsci 12 05536 g002 550
Figure 2. Comparison of PEDF serum level between AMPH-add and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. ***: statistically significant at p < 0.001.
Figure 2. Comparison of PEDF serum level between AMPH-add and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. ***: statistically significant at p < 0.001.
Applsci 12 05536 g002
Applsci 12 05536 g003 550
Figure 3. Comparison of MIP-4 serum level between AMPH-add and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. *: statistically significant at p < 0.05.
Figure 3. Comparison of MIP-4 serum level between AMPH-add and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. *: statistically significant at p < 0.05.
Applsci 12 05536 g003
Applsci 12 05536 g004 550
Figure 4. Comparison of ALT (a), AST (b) and AST/ALT ratio (c) serum level AMPH-adds and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. ***: statistically significant at p < 0.001.
Figure 4. Comparison of ALT (a), AST (b) and AST/ALT ratio (c) serum level AMPH-adds and control. Data are expressed as mean ± standard error. Statistical analysis was carried out using unpaired Student’s t-test. ***: statistically significant at p < 0.001.
Applsci 12 05536 g004
Table
Table 1. Sociodemographic characteristics of the AMPH-add participants and control.
Table 1. Sociodemographic characteristics of the AMPH-add participants and control.
ParametersControl (n = 19)AMPH-add (n = 17)p-Value
Age (years)31.56 ± 5.0432.23 ± 7.090.901
Male gender19 (100%)17 (100%)1.000
Education
University4 (21%)3 (17.6%)0.820
Intermediate5 (26.4%)4 (23.5%)
Secondary10 (52.6%)10 (58.8%)
Smoking18 (94.1%)16 (94.1%)0.798
Toombak12 (64.7%)--
Table
Table 2. Biomarkers and proinflammatory cytokines from AMPH-adds’ serum and control subjects.
Table 2. Biomarkers and proinflammatory cytokines from AMPH-adds’ serum and control subjects.
ParametersControl (n = 19)AMPH-add (n = 17)p-Value
ACT (ng/mL)163,406.31 ± 60,769.13834,027.99 ± 329,296.570.037 *
PEDF (ng/mL)27,709.64 ± 1672.5751,804.41 ± 3205.920.001 ***
MIP-4 (ng/mL)439.84 ± 17.19597.59 ± 61.310.012 *
Data are expressed as mean ± standard error. Difference between patients and control was examined using unpaired Student’s t-test. α1-anti chymotrypsin (ACT) and pigment epithelium-derived factor (PEDF) versus macrophage inflammatory protein-4 (MIP-4). *: p < 0.05; ***: p < 0.001.
Table
Table 3. Correlations between the biomarkers and proinflammatory cytokines and liver transaminases (ALT and AST) among all study groups.
Table 3. Correlations between the biomarkers and proinflammatory cytokines and liver transaminases (ALT and AST) among all study groups.
ParametersALTASTAST/ALT
Ratio		ACTPEDF
AST (r)−0.617 ***----
Sig.0.000
AST/ALT
ratio (r)		−0.800 ***0.915 ***---
Sig.0.0000.000
ACT (r)−0.2960.1830.250--
Sig.0.0800.2850.142
PEDF (r)−0.618 ***0.551 ***0.651 ***0.237-
Sig.0.0000.0000.0000.164
MIP4 (r)−0.0520.0690.0770.0780.181
Sig.0.7720.6990.6630.6610.306
Correlation coefficient was made using Pearson’s test. α1-anti chymotrypsin (ACT) and pigment epithelium-derived factor (PEDF) versus macrophage inflammatory protein-4 (MIP-4).
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Alrafiah, A.R.; Abu-Illah, M.M.; Magadmi, R.M.; Aqeel, A.; Najmi, A.; Jaddoh, S. Increased Inflammatory Markers at AMPH-Addicts Are Related to Neurodegenerative Conditions: Alzheimer’s Disease. Appl. Sci. 2022, 12, 5536. https://doi.org/10.3390/app12115536

AMA Style

Alrafiah AR, Abu-Illah MM, Magadmi RM, Aqeel A, Najmi A, Jaddoh S. Increased Inflammatory Markers at AMPH-Addicts Are Related to Neurodegenerative Conditions: Alzheimer’s Disease. Applied Sciences. 2022; 12(11):5536. https://doi.org/10.3390/app12115536

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Alrafiah, Aziza R., Mohammed M. Abu-Illah, Rania M. Magadmi, Aqeel Aqeel, Abdulmuttaleb Najmi, and Sattam Jaddoh. 2022. "Increased Inflammatory Markers at AMPH-Addicts Are Related to Neurodegenerative Conditions: Alzheimer’s Disease" Applied Sciences 12, no. 11: 5536. https://doi.org/10.3390/app12115536

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